From 4b3e0360664aad0af201d06ba5c8c28319a9515a Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 01:05:36 +0200
Subject: [PATCH 01/33] test: freeze the mini-dataset outputs of aa5daa5 as
 golden references (G2)

Histograms of hist, estimate (fake factors and an ABCD variant), the datacard
text and shapes, and the ML-export frames, stored as exact floats. The rebuilt
core must reproduce them bitwise.
---
 tests/golden/datacard_mt.txt        |    20 +
 tests/golden/make_golden_aa5daa5.py |    83 +
 tests/golden/mini_golden.json       | 13844 ++++++++++++++++++++++++++
 tests/test_golden.py                |   109 +
 4 files changed, 14056 insertions(+)
 create mode 100644 tests/golden/datacard_mt.txt
 create mode 100644 tests/golden/make_golden_aa5daa5.py
 create mode 100644 tests/golden/mini_golden.json
 create mode 100644 tests/test_golden.py

diff --git a/tests/golden/datacard_mt.txt b/tests/golden/datacard_mt.txt
new file mode 100644
index 0000000..2a307c5
--- /dev/null
+++ b/tests/golden/datacard_mt.txt
@@ -0,0 +1,20 @@
+imax * number of bins
+jmax * number of processes minus 1
+kmax * number of nuisance parameters
+------------------------------------------------------------
+shapes * htt_mt_1_2018 common/htt_input_2018.root htt_mt_1_2018/$PROCESS htt_mt_1_2018/$PROCESS_$SYSTEMATIC
+shapes * htt_mt_2_2018 common/htt_input_2018.root htt_mt_2_2018/$PROCESS htt_mt_2_2018/$PROCESS_$SYSTEMATIC
+------------------------------------------------------------
+bin htt_mt_1_2018 htt_mt_2_2018
+observation 118.0 81.0
+------------------------------------------------------------
+bin htt_mt_1_2018 htt_mt_1_2018 htt_mt_1_2018 htt_mt_1_2018 htt_mt_2_2018 htt_mt_2_2018 htt_mt_2_2018 htt_mt_2_2018
+process HH ZTT ZL jetFakes HH ZTT ZL jetFakes
+process 0 1 2 3 0 1 2 3
+rate -1 -1 -1 -1 -1 -1 -1 -1
+------------------------------------------------------------
+lumi lnN 1.025 1.025 1.025 1.025 1.025 1.025 1.025 1.025
+zjXsec lnN - 1.02 1.02 - - 1.02 1.02 -
+ffNorm_mt lnN - - - 1.1 - - - 1.1
+CMS_pu shape 1 1 1 - 1 1 1 -
+* autoMCStats 0
diff --git a/tests/golden/make_golden_aa5daa5.py b/tests/golden/make_golden_aa5daa5.py
new file mode 100644
index 0000000..96376c9
--- /dev/null
+++ b/tests/golden/make_golden_aa5daa5.py
@@ -0,0 +1,83 @@
+"""G2: golden outputs of the core mini dataset, frozen with core aa5daa5 (run once; needs that core on PYTHONPATH).
+
+Writes <out>/mini_golden.json (every histogram as exact floats: edges, values, variances), the datacard text
+<out>/datacard_mt.txt, the ML-export frames and the skim contracts of the old manifests.
+"""
+import dataclasses
+import json
+import sys
+import tempfile
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import uproot
+
+from shapesmith.config import FriendConfig, NtupleConfig, RunConfig
+from shapesmith.datacards import run_datacards
+from shapesmith.estimators import run_estimate
+from shapesmith.histograms import run_hist
+from shapesmith.ml_export import run_ml_export
+from shapesmith.model import Estimator, Region
+from shapesmith.skim import run_skim
+from shapesmith.testing import make_mini_dataset
+from tests.mini_analysis import build
+
+
+def digest(hset) -> dict:
+    return {key.path: [h.edges.tolist(), h.values.tolist(), h.variances.tolist()] for key, h in hset.items()}
+
+
+def abcd_variant(analysis):
+    channel = analysis.channel("mt")
+    anti = "(id_tight < 0.5) & (id_loose > 0.5)"
+    regions = channel.regions + (
+        Region("abcd_anti_iso", replace_cuts={"tau_iso": anti}),
+        Region("abcd_same_sign_anti_iso", replace_cuts={"os": "(q_1 * q_2) > 0", "tau_iso": anti}),
+    )
+    estimator = Estimator("abcd", {"B": "abcd_anti_iso", "C": "same_sign", "D": "abcd_same_sign_anti_iso"}, ("ZTT", "ZL"), "QCD")
+    return dataclasses.replace(analysis, channels={"mt": dataclasses.replace(channel, regions=regions)}, estimator=estimator)
+
+
+def main(out: Path) -> None:
+    out.mkdir(parents=True, exist_ok=True)
+    tmp = Path(tempfile.mkdtemp())
+    make_mini_dataset(tmp / "data", n_files=2, n_events=400)
+    config = RunConfig(
+        analysis="tests.mini_analysis:build", era="2018", channels=["mt"], switches={"jet_fakes": "ff", "embedding": False},
+        ntuples=NtupleConfig(base=str(tmp / "data" / "CROWNRun"), friends=[FriendConfig(base=str(tmp / "data" / "CROWNFriends" / "nn"))]),
+        skim_dir=tmp / "skims", output_dir=tmp / "out", ml_dir=tmp / "ml", workers=1,
+    )
+    analysis = build(config)
+    run_skim(config, analysis)
+    golden = {}
+    control = run_hist(config, analysis, ["mt"], True, None, True, None, tmp / "out" / "control_shapes.root", force=True)
+    golden["control_hist"] = digest(control)
+    run_estimate(control, analysis, ["mt"])
+    golden["control_estimate"] = digest(control)
+    regions = run_hist(config, analysis, ["mt"], True, None, True, None, tmp / "out" / "regions.root", force=True, regions=["all"])
+    golden["control_regions_all"] = digest(regions)
+    shapes = run_hist(config, analysis, ["mt"], False, None, True, None, tmp / "out" / "shapes.root", force=True)
+    golden["shapes_hist"] = digest(shapes)
+    run_estimate(shapes, analysis, ["mt"])
+    golden["shapes_estimate"] = digest(shapes)
+    cards = run_datacards(shapes, analysis, {"mt": ["mt"]}, tmp / "out" / "datacards", True, 1.0)
+    (out / "datacard_mt.txt").write_text(cards["mt"].read_text())
+    with uproot.open(tmp / "out" / "datacards" / "mt" / "common" / "htt_input_2018.root") as f:
+        golden["datacard_shapes"] = {name: [f[name].axis().edges().tolist(), f[name].values().tolist(), f[name].variances().tolist()] for name, cls in sorted(f.classnames(recursive=True, cycle=False).items()) if cls.startswith("TH1")}
+    abcd = abcd_variant(analysis)
+    abcd_hset = run_hist(config, abcd, ["mt"], True, None, False, None, tmp / "out" / "abcd.root", force=True)
+    run_estimate(abcd_hset, abcd, ["mt"])
+    golden["abcd_control_estimate"] = digest(abcd_hset)
+    ml = {}
+    for path in run_ml_export(config, analysis, ["mt"]):
+        frame = pd.read_feather(path)
+        ml[path.name] = {"|".join(column): np.asarray(frame[column]).astype(np.float64).tolist() for column in frame.columns}
+    golden["ml_export"] = ml
+    golden["skim_contracts"] = {p.parent.name: json.loads(p.read_text())["contract"] for p in sorted((tmp / "skims" / "mt").glob("*/manifest.json"))}
+    (out / "mini_golden.json").write_text(json.dumps(golden, indent=0, sort_keys=True))
+    print({name: len(value) for name, value in golden.items()})
+
+
+if __name__ == "__main__":
+    main(Path(sys.argv[1]))
diff --git a/tests/golden/mini_golden.json b/tests/golden/mini_golden.json
new file mode 100644
index 0000000..c2ddcd9
--- /dev/null
+++ b/tests/golden/mini_golden.json
@@ -0,0 +1,13844 @@
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+"mt_bkg/ZL#anti_iso#CMS_puDown#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+0.38088998931575524,
+1.3054898270300042
+],
+[
+0.07863882340196186,
+0.31870766260202193
+]
+],
+"mt_bkg/ZL#anti_iso#CMS_puUp#score": [
+[
+0.0,
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+1.0
+],
+[
+0.4209836736789353,
+1.4429098334882098
+],
+[
+0.09606571051776405,
+0.38933540797645605
+]
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+"mt_bkg/ZL#anti_iso#Nominal#score": [
+[
+0.0,
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+1.0
+],
+[
+0.4009368446124316,
+1.374199860401709
+],
+[
+0.0871344365877246,
+0.35313870096297384
+]
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+"mt_bkg/ZL#nominal#CMS_puDown#score": [
+[
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+[
+10.31139839812493,
+9.500740241647476
+],
+[
+12.16188947108262,
+9.073759951329517
+]
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+[
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+11.396808502317977,
+10.50081783316461
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+11.084564777924376
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+10.000779155209484
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+10.054027540675467
+]
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+[
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+[
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+2.8718258715840808
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+1.088717593300315
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+3.022974655048804
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+1.206335330435302
+]
+],
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+[
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+[
+42.25250792465663,
+33.45390825950118
+],
+[
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+35.305212782750296
+]
+],
+"mt_bkg/ZTT#nominal#CMS_puUp#score": [
+[
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+],
+[
+46.7001386541662,
+36.97537120327292
+],
+[
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+43.1290801188675
+]
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+"mt_bkg/ZTT#nominal#Nominal#score": [
+[
+0.0,
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+],
+[
+44.47632472984159,
+35.21464067883315
+],
+[
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+39.119350256126964
+]
+],
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+[
+0.0,
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+],
+[
+5.0738091096282005,
+6.439230136573315
+],
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+1.4572472373507246
+]
+],
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+[
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+1.0
+],
+[
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+],
+[
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+]
+],
+"mt_sig/HH#nominal#CMS_puDown#score": [
+[
+0.0,
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+],
+[
+7.718603349377448,
+7.566579239979687
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+[
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+1.749422936213198
+]
+],
+"mt_sig/HH#nominal#CMS_puUp#score": [
+[
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+8.363061055929263
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+]
+],
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+7.964820356634302
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+[
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+],
+[
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+[
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+],
+[
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+2.2705986164231877
+],
+[
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+0.5029138532685167
+]
+],
+"mt_sig/ZL#nominal#CMS_puDown#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+8.93370816024295,
+9.16586168068974
+],
+[
+8.963968911606639,
+8.456406936897288
+]
+],
+"mt_sig/ZL#nominal#CMS_puUp#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+9.874098217815092,
+10.130688910982421
+],
+[
+10.950443371969932,
+10.330402289351593
+]
+],
+"mt_sig/ZL#nominal#Nominal#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+9.403903486436324,
+9.648275618619088
+],
+[
+9.932375852647471,
+9.369980308152385
+]
+],
+"mt_sig/ZTT#anti_iso#CMS_puDown#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+3.5946955235149574,
+4.625252672049072
+],
+[
+0.8739460516338361,
+0.9874055356828372
+]
+],
+"mt_sig/ZTT#anti_iso#CMS_puUp#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+3.9730843862585625,
+5.112121183693464
+],
+[
+1.06761822179822,
+1.2062210709023422
+]
+],
+"mt_sig/ZTT#anti_iso#Nominal#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+3.783890055649397,
+4.868687132517687
+],
+[
+0.9683612945330983,
+1.0940782020763677
+]
+],
+"mt_sig/ZTT#nominal#CMS_puDown#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+33.68730443179881,
+24.93868536076957
+],
+[
+44.69843455338169,
+33.116321946826176
+]
+],
+"mt_sig/ZTT#nominal#CMS_puUp#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+37.2333350982851,
+27.563809012490815
+],
+[
+54.60390123475606,
+40.45511599568244
+]
+],
+"mt_sig/ZTT#nominal#Nominal#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+35.460320868912405,
+26.251248169169948
+],
+[
+49.527352367675455,
+36.69398646031606
+]
+],
+"mt_sig/data#anti_iso#Nominal#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+5.324695855379105,
+8.20532651990652
+],
+[
+1.1092630220570188,
+1.786915519225281
+]
+],
+"mt_sig/data#nominal#Nominal#score": [
+[
+0.0,
+0.5,
+1.0
+],
+[
+63.0,
+55.0
+],
+[
+63.0,
+55.0
+]
+]
+},
+"skim_contracts": {
+"DATA_A": {
+"normalisation": {
+"generator_weight": 1.0,
+"kind": "data",
+"nevents": 1,
+"xsec": 1.0
+},
+"required_columns": [
+"cls",
+"event",
+"fake_factor",
+"genWeight",
+"gen_match",
+"id_loose",
+"id_tight",
+"m_vis",
+"puweight",
+"puweight_down",
+"puweight_up",
+"q_1",
+"q_2",
+"score",
+"trg_wgt",
+"veto"
+],
+"selection": {
+"iso_loose": "id_loose > 0.5",
+"veto": "veto < 0.5"
+}
+},
+"SIG_1": {
+"normalisation": {
+"generator_weight": 1.0,
+"kind": "mc",
+"nevents": 50,
+"xsec": 1.0
+},
+"required_columns": [
+"cls",
+"event",
+"fake_factor",
+"genWeight",
+"gen_match",
+"id_loose",
+"id_tight",
+"m_vis",
+"puweight",
+"puweight_down",
+"puweight_up",
+"q_1",
+"q_2",
+"score",
+"trg_wgt",
+"veto"
+],
+"selection": {
+"iso_loose": "id_loose > 0.5",
+"veto": "veto < 0.5"
+}
+},
+"ZTT_1": {
+"normalisation": {
+"generator_weight": 1.0,
+"kind": "mc",
+"nevents": 100,
+"xsec": 10.0
+},
+"required_columns": [
+"cls",
+"event",
+"fake_factor",
+"genWeight",
+"gen_match",
+"id_loose",
+"id_tight",
+"m_vis",
+"puweight",
+"puweight_down",
+"puweight_up",
+"q_1",
+"q_2",
+"score",
+"trg_wgt",
+"veto"
+],
+"selection": {
+"iso_loose": "id_loose > 0.5",
+"veto": "veto < 0.5"
+}
+}
+}
+}
\ No newline at end of file
diff --git a/tests/test_golden.py b/tests/test_golden.py
new file mode 100644
index 0000000..bc691ce
--- /dev/null
+++ b/tests/test_golden.py
@@ -0,0 +1,109 @@
+"""G2: the mini-dataset outputs must stay bitwise identical to the ones frozen with core aa5daa5.
+
+The reference files in tests/golden/ were written once by make_golden_aa5daa5.py against that core; the script is
+kept for the record and does not run against the current API.
+"""
+import dataclasses
+import json
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+import uproot
+
+from shapesmith.config import FriendConfig, NtupleConfig, RunConfig
+from shapesmith.datacards import run_datacards
+from shapesmith.estimators import run_estimate
+from shapesmith.histograms import run_hist
+from shapesmith.ml_export import run_ml_export
+from shapesmith.model import Estimator, Region
+from shapesmith.skim import run_skim
+from shapesmith.testing import make_mini_dataset
+from tests.mini_analysis import build
+
+GOLDEN = Path(__file__).parent / "golden"
+
+
+def _digest(hset) -> dict:
+    return {key.path: [h.edges.tolist(), h.values.tolist(), h.variances.tolist()] for key, h in hset.items()}
+
+
+def _abcd_variant(analysis):
+    channel = analysis.channel("mt")
+    anti = "(id_tight < 0.5) & (id_loose > 0.5)"
+    regions = channel.regions + (
+        Region("abcd_anti_iso", replace_cuts={"tau_iso": anti}),
+        Region("abcd_same_sign_anti_iso", replace_cuts={"os": "(q_1 * q_2) > 0", "tau_iso": anti}),
+    )
+    estimator = Estimator("abcd", {"B": "abcd_anti_iso", "C": "same_sign", "D": "abcd_same_sign_anti_iso"}, ("ZTT", "ZL"), "QCD")
+    return dataclasses.replace(analysis, channels={"mt": dataclasses.replace(channel, regions=regions)}, estimator=estimator)
+
+
+def _produce(tmp: Path) -> tuple[dict, str]:
+    make_mini_dataset(tmp / "data", n_files=2, n_events=400)
+    config = RunConfig(
+        analysis="tests.mini_analysis:build", era="2018", channels=["mt"], switches={"jet_fakes": "ff", "embedding": False},
+        ntuples=NtupleConfig(base=str(tmp / "data" / "CROWNRun"), friends=[FriendConfig(base=str(tmp / "data" / "CROWNFriends" / "nn"))]),
+        skim_dir=tmp / "skims", output_dir=tmp / "out", ml_dir=tmp / "ml", workers=1,
+    )
+    analysis = build(config)
+    run_skim(config, analysis)
+    result = {}
+    control = run_hist(config, analysis, ["mt"], True, None, True, None, tmp / "out" / "control_shapes.root", force=True)
+    result["control_hist"] = _digest(control)
+    run_estimate(control, analysis, ["mt"])
+    result["control_estimate"] = _digest(control)
+    regions = run_hist(config, analysis, ["mt"], True, None, True, None, tmp / "out" / "regions.root", force=True, regions=["all"])
+    result["control_regions_all"] = _digest(regions)
+    shapes = run_hist(config, analysis, ["mt"], False, None, True, None, tmp / "out" / "shapes.root", force=True)
+    result["shapes_hist"] = _digest(shapes)
+    run_estimate(shapes, analysis, ["mt"])
+    result["shapes_estimate"] = _digest(shapes)
+    card = run_datacards(shapes, analysis, {"mt": ["mt"]}, tmp / "out" / "datacards", True, 1.0)["mt"].read_text()
+    with uproot.open(tmp / "out" / "datacards" / "mt" / "common" / "htt_input_2018.root") as f:
+        result["datacard_shapes"] = {
+            name: [f[name].axis().edges().tolist(), f[name].values().tolist(), f[name].variances().tolist()]
+            for name, cls in sorted(f.classnames(recursive=True, cycle=False).items())
+            if cls.startswith("TH1")
+        }
+    abcd = _abcd_variant(analysis)
+    abcd_hset = run_hist(config, abcd, ["mt"], True, None, False, None, tmp / "out" / "abcd.root", force=True)
+    run_estimate(abcd_hset, abcd, ["mt"])
+    result["abcd_control_estimate"] = _digest(abcd_hset)
+    result["ml_export"] = {}
+    for path in run_ml_export(config, analysis, ["mt"]):
+        frame = pd.read_feather(path)
+        result["ml_export"][path.name] = {"|".join(column): np.asarray(frame[column]).astype(np.float64).tolist() for column in frame.columns}
+    return result, card
+
+
+def _bitwise_equal(a, b) -> bool:
+    return np.asarray(a, dtype=np.float64).tobytes() == np.asarray(b, dtype=np.float64).tobytes()
+
+
+@pytest.fixture(scope="module")
+def produced(tmp_path_factory):
+    return _produce(tmp_path_factory.mktemp("golden"))
+
+
+@pytest.mark.parametrize("part", ["control_hist", "control_estimate", "control_regions_all", "shapes_hist", "shapes_estimate", "datacard_shapes", "abcd_control_estimate"])
+def test_histograms_match_the_golden_outputs(produced, part):
+    golden = json.loads((GOLDEN / "mini_golden.json").read_text())[part]
+    result = produced[0][part]
+    assert sorted(result) == sorted(golden)
+    different = [name for name in golden if not all(_bitwise_equal(r, g) for r, g in zip(result[name], golden[name]))]
+    assert not different
+
+
+def test_datacard_text_matches_the_golden_card(produced):
+    assert produced[1] == (GOLDEN / "datacard_mt.txt").read_text()
+
+
+def test_ml_export_matches_the_golden_frames(produced):
+    golden = json.loads((GOLDEN / "mini_golden.json").read_text())["ml_export"]
+    result = produced[0]["ml_export"]
+    assert sorted(result) == sorted(golden)
+    for fold, columns in golden.items():
+        assert sorted(result[fold]) == sorted(columns)
+        assert all(_bitwise_equal(result[fold][name], values) for name, values in columns.items()), fold

From 3d895f9c17b90a2f04eae19977585f4190fe9930 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 01:07:27 +0200
Subject: [PATCH 02/33] feat(samples)!: sample lists are verbatim KingMaker
 lists, normalisation by nick

read_sample_list reads one nick per line and rejects anything else (the former
DBS column included) and duplicates. normalisation(database, nicks) looks up by
nick only and reports every missing nick at once. Removed: InventoryEntry,
read_inventory, write_inventory, the DBS fallback, database_nick and the
inventory command.
---
 README.md                 |  1 -
 src/shapesmith/cli.py     | 10 -----
 src/shapesmith/samples.py | 92 ++++++++++++++-------------------------
 tests/test_samples.py     | 82 +++++++++++++++++-----------------
 4 files changed, 73 insertions(+), 112 deletions(-)

diff --git a/README.md b/README.md
index 690d918..44e9106 100644
--- a/README.md
+++ b/README.md
@@ -32,7 +32,6 @@ shapesmith datacards -c run.yaml [--min-background 1.0] [--no-systematics]
 shapesmith fit       -c run.yaml [--final-states mt,all] [--skip-combine]
 shapesmith ml-export -c run.yaml              # Feather training folds
 shapesmith inspect   output/shapes.root       # what a histogram file contains
-shapesmith inventory datasets.json nicks.txt inventory.txt   # production inventory (nick + DBS path)
 ```
 
 Two stages: `skim` reads the ntuples once (loose selection, all needed columns, normalisation
diff --git a/src/shapesmith/cli.py b/src/shapesmith/cli.py
index 455bb38..fca5abd 100644
--- a/src/shapesmith/cli.py
+++ b/src/shapesmith/cli.py
@@ -53,16 +53,6 @@ def example_config(path: Path = typer.Argument(..., help="where to write the exa
     typer.echo(f"wrote {path}")
 
 
-@app.command()
-def inventory(database: Path = typer.Argument(..., help="KingMaker datasets.json that still knows the nicks by name"), nicks: Path = typer.Argument(..., help="text file, one produced nick per line"), output: Path = typer.Argument(..., help="inventory to write: nick and DBS path per line")):
-    """Write a production inventory: for every produced nick its DBS dataset path, the key that survives database renames."""
-    from shapesmith.samples import write_inventory
-
-    names = [line.strip() for line in nicks.read_text().splitlines() if line.strip()]
-    entries = write_inventory(output, database, names)
-    typer.echo(f"wrote {len(entries)} entries to {output}")
-
-
 @app.command()
 def validate(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption):
     """Load and validate the analysis; report the columns every channel needs."""
diff --git a/src/shapesmith/samples.py b/src/shapesmith/samples.py
index 260c7fb..54eeb31 100644
--- a/src/shapesmith/samples.py
+++ b/src/shapesmith/samples.py
@@ -1,77 +1,49 @@
-"""Production inventories and the normalisation lookup in the KingMaker sample database (datasets.json).
+"""Sample lists and the normalisation lookup in the KingMaker sample database (datasets.json).
 
-The database renames nicks now and then (2026-08: the 2018 v15 nicks gained the campaign suffix
-`_mc2018_realistic_v1-v2`) while CROWN output directories keep the nick used at production time. The DBS
-dataset path is stable, so an inventory lists `nick dbs` pairs and the lookup goes by nick first, by DBS second.
+A sample list is an unchanged copy of the KingMaker sample list a production ran with: one nick per line. The
+normalisation of every nick comes from the configured database, looked up by nick. The database renames nicks now
+and then, so an older production may need the database checkout it was produced with.
 """
 from __future__ import annotations
 
 import json
-from dataclasses import dataclass
 from pathlib import Path
 
-FIELDS = ("sample_type", "xsec", "nevents", "generator_weight")
+from shapesmith.model import AnalysisError
 
-
-@dataclass(frozen=True)
-class InventoryEntry:
-    nick: str  # directory name in the CROWN output
-    dbs: str  # DBS dataset path, stable across database versions
+FIELDS = ("xsec", "nevents", "generator_weight")
 
 
 def kind_of(sample_type: str) -> str:
-    if sample_type == "data":
-        return "data"
-    if "embedding" in sample_type:
-        return "embedding"
-    return "mc"
+    """The sample kind of a database `sample_type`: data, embedding, or mc for every simulated type."""
+    return sample_type if sample_type in ("data", "embedding") else "mc"
 
 
-def read_inventory(path: Path) -> list[InventoryEntry]:
-    """`<nick> <dbs>` per line; blank lines and `#` comments are ignored."""
-    entries = []
-    for line in Path(path).read_text().splitlines():
-        line = line.split("#", 1)[0].strip()
-        if not line:
+def read_sample_list(path: Path) -> tuple[str, ...]:
+    """The nicks of a sample list in file order; blank lines are skipped, every other line is exactly one nick."""
+    nicks: list[str] = []
+    for number, line in enumerate(Path(path).read_text().splitlines(), start=1):
+        tokens = line.split()
+        if not tokens:
             continue
-        parts = line.split()
-        if len(parts) != 2:
-            raise ValueError(f"{path}: expected '<nick> <dbs>', got {line!r}")
-        entries.append(InventoryEntry(*parts))
-    return entries
-
-
-def write_inventory(path: Path, database: Path, nicks: list[str]) -> list[InventoryEntry]:
-    """Create an inventory for nicks that the given database version still knows by name."""
+        if len(tokens) != 1:
+            raise ValueError(
+                f"{path}:{number}: expected one nick per line (an unchanged KingMaker sample list), got {line!r};"
+                " the former DBS column is no longer supported"
+            )
+        if tokens[0] in nicks:
+            raise ValueError(f"{path}:{number}: duplicate nick {tokens[0]}")
+        nicks.append(tokens[0])
+    return tuple(nicks)
+
+
+def normalisation(database: Path, nicks: tuple[str, ...] | list[str]) -> dict[str, dict]:
+    """nick -> {kind, xsec, nevents, generator_weight} from the database; every missing nick is reported at once."""
     db = json.loads(Path(database).read_text())
     missing = [nick for nick in nicks if nick not in db]
     if missing:
-        raise KeyError(f"nicks not in {database}: {', '.join(missing)}")
-    entries = [InventoryEntry(nick, db[nick]["dbs"]) for nick in nicks]
-    Path(path).parent.mkdir(parents=True, exist_ok=True)
-    Path(path).write_text("".join(f"{entry.nick} {entry.dbs}\n" for entry in entries))
-    return entries
-
-
-def normalisation(database: Path, inventory: list[InventoryEntry]) -> dict[str, dict]:
-    """nick -> {kind, sample_type, xsec, nevents, generator_weight, database_nick} for every inventory entry."""
-    db = json.loads(Path(database).read_text())
-    by_dbs: dict[str, list[str]] = {}
-    for key, entry in db.items():
-        by_dbs.setdefault(entry["dbs"], []).append(key)
-    result, unresolved = {}, []
-    for item in inventory:
-        if item.nick in db:
-            key = item.nick
-        else:
-            candidates = by_dbs.get(item.dbs, [])
-            values = {tuple(db[c][field] for field in FIELDS) for c in candidates}
-            if len(values) != 1:
-                unresolved.append(f"{item.nick} ({len(candidates)} entries for {item.dbs})")
-                continue
-            key = candidates[0]
-        entry = db[key]
-        result[item.nick] = {"kind": kind_of(entry["sample_type"]), "database_nick": key, **{field: entry[field] for field in FIELDS}}
-    if unresolved:
-        raise KeyError(f"not resolvable in {database}: " + "; ".join(unresolved))
-    return result
+        raise AnalysisError(
+            f"{len(missing)} nicks are not in {database} (the database may have renamed them, see the git log of the"
+            " sample database):\n" + "\n".join(missing)
+        )
+    return {nick: {"kind": kind_of(db[nick]["sample_type"]), **{field: db[nick][field] for field in FIELDS}} for nick in nicks}
diff --git a/tests/test_samples.py b/tests/test_samples.py
index b378bfe..7d630d8 100644
--- a/tests/test_samples.py
+++ b/tests/test_samples.py
@@ -2,54 +2,54 @@
 
 import pytest
 
-from shapesmith.samples import InventoryEntry, kind_of, normalisation, read_inventory, write_inventory
+from shapesmith.model import AnalysisError
+from shapesmith.samples import kind_of, normalisation, read_sample_list
 
 DB = {
-    "TT_old": {"nick": "TT_old", "dbs": "/TT/campaign-v2/NANOAODSIM", "sample_type": "ttbar", "xsec": 88.5, "nevents": 1000, "generator_weight": 0.99, "era": "2018"},
+    "TT_1": {"nick": "TT_1", "dbs": "/TT/campaign-v2/NANOAODSIM", "sample_type": "ttbar", "xsec": 88.5, "nevents": 1000, "generator_weight": 0.99, "era": "2018"},
     "DATA_A": {"nick": "DATA_A", "dbs": "/SingleMuon/Run2018A/NANOAOD", "sample_type": "data", "xsec": 1.0, "nevents": 1, "generator_weight": 1.0, "era": "2018"},
+    "EMB_A": {"nick": "EMB_A", "dbs": "/EmbeddingRun2018A/MuTau/USER", "sample_type": "embedding", "xsec": 1.0, "nevents": 5000, "generator_weight": 1.0, "era": "2018"},
 }
-RENAMED = {
-    "TT_new": {**DB["TT_old"], "nick": "TT_new"},
-    "DATA_A_v2": {**DB["DATA_A"], "nick": "DATA_A_v2"},
-    "DATA_A": DB["DATA_A"],  # the old name kept as a duplicate with identical values
-}
 
 
-def _write(tmp_path, name, db):
-    path = tmp_path / name
-    path.write_text(json.dumps(db))
+@pytest.fixture
+def database(tmp_path):
+    path = tmp_path / "datasets.json"
+    path.write_text(json.dumps(DB))
+    return path
+
+
+def _list(tmp_path, text):
+    path = tmp_path / "sample_list.txt"
+    path.write_text(text)
     return path
 
 
 def test_kind_of():
-    assert kind_of("data") == "data" and kind_of("embedding") == "embedding" and kind_of("ttbar") == "mc"
-
-
-def test_inventory_roundtrip(tmp_path):
-    database = _write(tmp_path, "old.json", DB)
-    entries = write_inventory(tmp_path / "inv.txt", database, ["TT_old", "DATA_A"])
-    assert entries == [InventoryEntry("TT_old", "/TT/campaign-v2/NANOAODSIM"), InventoryEntry("DATA_A", "/SingleMuon/Run2018A/NANOAOD")]
-    (tmp_path / "inv.txt").write_text("# comment\n" + (tmp_path / "inv.txt").read_text() + "\n")
-    assert read_inventory(tmp_path / "inv.txt") == entries
-    with pytest.raises(KeyError, match="NOPE"):
-        write_inventory(tmp_path / "x.txt", database, ["NOPE"])
-    (tmp_path / "bad.txt").write_text("only_one_column\n")
-    with pytest.raises(ValueError, match="only_one_column"):
-        read_inventory(tmp_path / "bad.txt")
-
-
-def test_normalisation_by_nick_and_by_dbs(tmp_path):
-    inventory = [InventoryEntry("TT_old", "/TT/campaign-v2/NANOAODSIM"), InventoryEntry("DATA_A", "/SingleMuon/Run2018A/NANOAOD")]
-    same = normalisation(_write(tmp_path, "old.json", DB), inventory)
-    assert same["TT_old"] == {"kind": "mc", "database_nick": "TT_old", "sample_type": "ttbar", "xsec": 88.5, "nevents": 1000, "generator_weight": 0.99}
-    renamed = normalisation(_write(tmp_path, "new.json", RENAMED), inventory)
-    assert renamed["TT_old"]["database_nick"] == "TT_new" and renamed["TT_old"]["xsec"] == 88.5
-    assert renamed["DATA_A"]["database_nick"] == "DATA_A" and renamed["DATA_A"]["kind"] == "data"
-
-
-def test_normalisation_rejects_ambiguous_and_missing(tmp_path):
-    conflicting = {"TT_a": {**DB["TT_old"], "nick": "TT_a"}, "TT_b": {**DB["TT_old"], "nick": "TT_b", "nevents": 2000}}
-    with pytest.raises(KeyError, match="TT_old"):
-        normalisation(_write(tmp_path, "conflict.json", conflicting), [InventoryEntry("TT_old", "/TT/campaign-v2/NANOAODSIM")])
-    with pytest.raises(KeyError, match="GONE"):
-        normalisation(_write(tmp_path, "old.json", DB), [InventoryEntry("GONE", "/nowhere")])
+    assert (kind_of("data"), kind_of("embedding"), kind_of("ttbar"), kind_of("dy")) == ("data", "embedding", "mc", "mc")
+
+
+def test_sample_list_is_read_in_order_and_blank_lines_are_skipped(tmp_path):
+    assert read_sample_list(_list(tmp_path, "TT_1\n\nDATA_A\n  \nEMB_A")) == ("TT_1", "DATA_A", "EMB_A")
+
+
+@pytest.mark.parametrize("line", ["TT_1 /TT/campaign-v2/NANOAODSIM", "# a comment"])
+def test_a_line_with_more_than_one_token_raises(tmp_path, line):
+    with pytest.raises(ValueError, match=r"sample_list.txt:2: expected one nick per line.*DBS column"):
+        read_sample_list(_list(tmp_path, f"DATA_A\n{line}\n"))
+
+
+def test_a_duplicate_nick_raises(tmp_path):
+    with pytest.raises(ValueError, match="sample_list.txt:3: duplicate nick TT_1"):
+        read_sample_list(_list(tmp_path, "TT_1\nDATA_A\nTT_1\n"))
+
+
+def test_normalisation_by_nick(database):
+    values = normalisation(database, ("TT_1", "DATA_A", "EMB_A"))
+    assert values["TT_1"] == {"kind": "mc", "xsec": 88.5, "nevents": 1000, "generator_weight": 0.99}
+    assert values["DATA_A"]["kind"] == "data" and values["EMB_A"] == {"kind": "embedding", "xsec": 1.0, "nevents": 5000, "generator_weight": 1.0}
+
+
+def test_normalisation_lists_every_missing_nick(database):
+    with pytest.raises(AnalysisError, match=r"2 nicks are not in .*renamed.*\nGONE_1\nGONE_2"):
+        normalisation(database, ("GONE_1", "TT_1", "GONE_2"))

From 69ad361608a002ca6ac7151b57552d80262002a6 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 01:31:53 +0200
Subject: [PATCH 03/33] refactor!: rebuild the core around Channel and one
 event rule

Everything that differs by channel lives in Channel (samples, cuts, processes,
regions, categories, variables, variations, estimators). A process has a role
(data, signal, background, auxiliary) and owns its whole weight set; the core
weight and cut rule has no branches on sample kind and lives in one place,
events.select. Regions replace cuts, replace weights where a process carries
them and add weights.

Column variations (SW core):
- ColumnVariation(name, suffix | derived, applies_to, regions): a CROWN shift
  read from `c + suffix`, or a shift computed from nominal columns (derived).
  A name ending in Up/Down needs its partner; other names are templates that
  datacards never turn into shape lines.
- skim: shifted branches are read, an event is kept if the skim cuts pass
  nominally or under any variation of its kind, and per file every declared
  shift must have a branch and every shifted branch of a needed column must be
  declared (checked for kinds that declare shifts).
- skim reuse: rows and columns must be a superset of the needed ones (skim
  cuts, normalisation, sample cut equal; recorded column variations and friends
  contain the current ones; the Parquet schema holds the needed columns). The
  v4 manifests stay reusable. Incompatible skims are listed together.
- fill: a variation is filled where it applies; a column variation only where
  it changes an expression of the histogram. Hist re-applies the (varied) skim
  cuts and always refills the requested scopes (no --force).
- estimates: DataMinus propagates every column variation of its inputs with a
  nominal fallback; ABCD stays nominal; TemplateShift replaces the hard-coded
  embedding ttbar contamination and reads an auxiliary process.

Module layout: validate, events, fill, estimates, shapes (sync), store,
ntuples, provenance (versions/<analysis>.json), cmssw + limits (was fit).
Deleted: io/, estimators/, histograms.py, sync.py, the kind taxonomy,
Process.key, Sample.channels, the Selection-or-map, Channel.baseline/skim
weights, example-config.

The mini-dataset outputs stay bitwise identical to aa5daa5 (G2).
---
 src/shapesmith/cli.py                     | 114 ++++---
 src/shapesmith/cmssw.py                   |  33 +++
 src/shapesmith/config.py                  |  69 +----
 src/shapesmith/datacards.py               | 169 ++++++-----
 src/shapesmith/estimates.py               | 116 ++++++++
 src/shapesmith/estimators/__init__.py     |  42 ---
 src/shapesmith/estimators/abcd.py         |  51 ----
 src/shapesmith/estimators/embedding.py    |  27 --
 src/shapesmith/estimators/fake_factors.py |  20 --
 src/shapesmith/events.py                  |  74 +++++
 src/shapesmith/expressions.py             |  69 ++---
 src/shapesmith/fill.py                    | 167 +++++++++++
 src/shapesmith/histogram.py               |  80 ++++-
 src/shapesmith/histograms.py              | 273 -----------------
 src/shapesmith/io/__init__.py             |   0
 src/shapesmith/io/discovery.py            |  61 ----
 src/shapesmith/io/ntuples.py              |  80 -----
 src/shapesmith/{fit.py => limits.py}      |  39 +--
 src/shapesmith/ml_export.py               |  47 +--
 src/shapesmith/model.py                   | 213 +++++++-------
 src/shapesmith/ntuples.py                 | 113 +++++++
 src/shapesmith/plotting/stack.py          |  34 +--
 src/shapesmith/plotting/style.py          |  14 +-
 src/shapesmith/provenance.py              |  45 +++
 src/shapesmith/shapes.py                  |  44 +++
 src/shapesmith/skim.py                    | 343 +++++++++++-----------
 src/shapesmith/{io/skims.py => store.py}  |  32 +-
 src/shapesmith/sync.py                    |  34 ---
 src/shapesmith/testing.py                 |  35 ++-
 src/shapesmith/validate.py                |  97 ++++++
 tests/mini_analysis.py                    |  86 ++++--
 tests/test_cli.py                         |   8 +-
 tests/test_config.py                      |  11 +-
 tests/test_datacards.py                   |  42 +--
 tests/test_estimates.py                   |  87 ++++++
 tests/test_estimators.py                  |  65 ----
 tests/test_events.py                      |  55 ++++
 tests/test_expressions.py                 |  37 +--
 tests/test_fill.py                        | 151 ++++++++++
 tests/test_golden.py                      |  42 ++-
 tests/test_histograms.py                  | 157 ----------
 tests/{test_fit.py => test_limits.py}     |  26 +-
 tests/test_model.py                       |  62 ----
 tests/{test_io.py => test_ntuples.py}     |  24 +-
 tests/test_plotting.py                    |  26 +-
 tests/test_shapes.py                      |  35 +++
 tests/test_skim.py                        | 143 ++++-----
 tests/test_sync.py                        |  34 ---
 tests/test_validate.py                    |  97 ++++++
 tests/test_variations.py                  | 171 +++++++++++
 50 files changed, 2167 insertions(+), 1727 deletions(-)
 create mode 100644 src/shapesmith/cmssw.py
 create mode 100644 src/shapesmith/estimates.py
 delete mode 100644 src/shapesmith/estimators/__init__.py
 delete mode 100644 src/shapesmith/estimators/abcd.py
 delete mode 100644 src/shapesmith/estimators/embedding.py
 delete mode 100644 src/shapesmith/estimators/fake_factors.py
 create mode 100644 src/shapesmith/events.py
 create mode 100644 src/shapesmith/fill.py
 delete mode 100644 src/shapesmith/histograms.py
 delete mode 100644 src/shapesmith/io/__init__.py
 delete mode 100644 src/shapesmith/io/discovery.py
 delete mode 100644 src/shapesmith/io/ntuples.py
 rename src/shapesmith/{fit.py => limits.py} (72%)
 create mode 100644 src/shapesmith/ntuples.py
 create mode 100644 src/shapesmith/provenance.py
 create mode 100644 src/shapesmith/shapes.py
 rename src/shapesmith/{io/skims.py => store.py} (61%)
 delete mode 100644 src/shapesmith/sync.py
 create mode 100644 src/shapesmith/validate.py
 create mode 100644 tests/test_estimates.py
 delete mode 100644 tests/test_estimators.py
 create mode 100644 tests/test_events.py
 create mode 100644 tests/test_fill.py
 delete mode 100644 tests/test_histograms.py
 rename tests/{test_fit.py => test_limits.py} (66%)
 delete mode 100644 tests/test_model.py
 rename tests/{test_io.py => test_ntuples.py} (76%)
 create mode 100644 tests/test_shapes.py
 delete mode 100644 tests/test_sync.py
 create mode 100644 tests/test_validate.py
 create mode 100644 tests/test_variations.py

diff --git a/src/shapesmith/cli.py b/src/shapesmith/cli.py
index fca5abd..3554b5e 100644
--- a/src/shapesmith/cli.py
+++ b/src/shapesmith/cli.py
@@ -1,4 +1,4 @@
-"""Command-line interface: one sub-command per analysis step (Spec §13)."""
+"""Command-line interface: one sub-command per analysis step."""
 from __future__ import annotations
 
 import logging
@@ -11,13 +11,13 @@
 from rich.table import Table
 
 from shapesmith import __version__
-from shapesmith.config import RunConfig, load_analysis, load_config, write_example_config, write_versions
+from shapesmith.config import RunConfig, load_analysis, load_config
+from shapesmith.provenance import write_versions
 
-app = typer.Typer(help=f"ShapeSmith {__version__}: CROWN ntuples -> skims -> histograms -> datacards, fits, plots, ML folds.", no_args_is_help=True)
+app = typer.Typer(help=f"ShapeSmith {__version__}: CROWN ntuples -> skims -> histograms -> datacards, fits, plots, ML folds, measurements.", no_args_is_help=True)
 
 ConfigOption = typer.Option(..., "--config", "-c", help="run configuration YAML")
 ChannelsOption = typer.Option(None, "--channels", help="comma separated subset of the configured channels")
-ForceOption = typer.Option(False, "--force", help="recreate existing outputs")
 WorkersOption = typer.Option(None, "--workers", help="override `workers` of the configuration (1 = inline, best for debugging)")
 SetOption = typer.Option(None, "--set", "-s", help="override a configuration entry, e.g. -s switches.jet_fakes=ff -s workers=4 (repeatable, values parsed as YAML)")
 
@@ -46,81 +46,69 @@ def _final_states(channels: list[str]) -> dict[str, list[str]]:
     return states
 
 
-@app.command("example-config")
-def example_config(path: Path = typer.Argument(..., help="where to write the example YAML")):
-    """Write an example run configuration."""
-    write_example_config(path)
-    typer.echo(f"wrote {path}")
+def _split(value: Optional[str]) -> Optional[list[str]]:
+    return value.split(",") if value else None
 
 
 @app.command()
 def validate(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption):
-    """Load and validate the analysis; report the columns every channel needs."""
-    from shapesmith.skim import required_columns
+    """Load and validate the analysis; report processes, selections and the columns every channel needs."""
+    from shapesmith.skim import needed_columns
 
     cfg, analysis, selected = _setup(config, channels, overrides=overrides)
     console = Console()
     console.print(f"[bold]{analysis.name}[/bold]  era {analysis.era}, {analysis.lumi_pb:g} pb^-1, switches {dict(cfg.switches)}")
-    console.print(f"channels: {', '.join(selected)}   samples: {len(analysis.samples)} ({', '.join(f'{kind} {sum(s.kind == kind for s in analysis.samples)}' for kind in sorted({s.kind for s in analysis.samples}))})")
-    table = Table(title="processes", show_edge=False)
-    for column in ("key", "name", "kind", "group", "plot group", *[f"samples {c}" for c in selected]):
-        table.add_column(column)
-    for process in analysis.processes:
-        table.add_row(process.key, process.name, process.kind, process.group, process.plot_group, *[str(len(analysis.samples_for(process.group, c))) for c in selected])
-    console.print(table)
-    for channel in selected:
-        ch = analysis.channel(channel)
-        console.print(f"{channel}: baseline cuts {list(ch.baseline.cuts)}, weights {list(ch.baseline.weights)}, regions {[r.name for r in ch.regions]}")
-    console.print(f"categories: {[c.name for c in analysis.categories] or 'none'}   control variables: {len(analysis.control_variables)}")
-    console.print(f"systematics: {len(analysis.weight_variations)} weight variations, {len(analysis.lnn)} lnN   estimator: {analysis.estimator.name + ' -> ' + analysis.estimator.output if analysis.estimator else 'none'}")
-    for channel in selected:
-        typer.echo(f"{channel}: {len(required_columns(analysis, channel))} columns required")
+    for name in selected:
+        channel = analysis.channel(name)
+        kinds = sorted({s.kind for s in channel.samples})
+        console.print(f"[bold]{name}[/bold]: samples {len(channel.samples)} ({', '.join(f'{k} {sum(s.kind == k for s in channel.samples)}' for k in kinds)})")
+        table = Table(show_edge=False)
+        for column in ("process", "role", "group", "plot group", "samples"):
+            table.add_column(column)
+        for process in channel.processes:
+            table.add_row(process.name, process.role, process.group, process.plot_group, str(len(channel.samples_of(process.group))))
+        console.print(table)
+        console.print(f"  cuts {list(channel.cuts)}, regions {[r.name for r in channel.regions]}")
+        console.print(f"  categories {[c.name for c in channel.categories] or 'none'}, control variables {len(channel.variables)}, variations {len(channel.variations)}")
+        console.print(f"  estimators {[type(e).__name__ for e in channel.estimators] or 'none'}, backgrounds {list(channel.backgrounds())}")
+        columns = set().union(*(needed_columns(channel, s) for s in channel.samples))
+        typer.echo(f"{name}: {len(columns)} columns required")
     typer.echo(f"analysis {analysis.name} is valid")
 
 
 @app.command()
-def skim(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption, force: bool = ForceOption, samples: Optional[str] = typer.Option(None, "--samples", help="comma separated sample groups or nick prefixes (default: all)"), workers: Optional[int] = WorkersOption):
+def skim(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption, force: bool = typer.Option(False, "--force", help="skim again, ignoring stored skims"), samples: Optional[str] = typer.Option(None, "--samples", help="comma separated sample groups or nick prefixes (default: all)"), workers: Optional[int] = WorkersOption):
     """Stage 1: ntuples (+ friends) -> Parquet skims."""
     from shapesmith.skim import run_skim
 
     cfg, analysis, selected = _setup(config, channels, workers=workers, overrides=overrides)
-    write_versions(cfg, cfg.skim_dir)
-    results = run_skim(cfg, analysis, selected, force=force, samples=samples.split(",") if samples else None)
+    write_versions(cfg, analysis.name, cfg.skim_dir)
+    results = run_skim(cfg, analysis, selected, force=force, samples=_split(samples))
     typer.echo(f"{sum(1 for r in results if not r.skipped)} files skimmed, {sum(1 for r in results if r.skipped)} reused, {sum(r.n_out for r in results)} events kept")
 
 
 @app.command()
-def hist(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption, control: bool = typer.Option(False, "--control", help="control variables instead of NN categories"), variables: Optional[str] = typer.Option(None, help="comma separated control variables"), regions: Optional[str] = typer.Option(None, "--regions", help="comma separated regions, or 'all' (default: nominal plus estimator regions)"), skip_systematics: bool = typer.Option(False, "--skip-systematics"), processes: Optional[str] = typer.Option(None, help="comma separated process keys"), force: bool = ForceOption, workers: Optional[int] = WorkersOption):
-    """Stage 2: skims -> histograms (control_shapes.root or shapes.root)."""
-    from shapesmith.histograms import run_hist
+def hist(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption, control: bool = typer.Option(False, "--control", help="control variables instead of NN categories"), variables: Optional[str] = typer.Option(None, help="comma separated control variables"), regions: Optional[str] = typer.Option(None, "--regions", help="comma separated regions, or 'all' (default: nominal plus estimator regions)"), skip_systematics: bool = typer.Option(False, "--skip-systematics", help="no weight or column variations"), processes: Optional[str] = typer.Option(None, help="comma separated process names"), workers: Optional[int] = WorkersOption):
+    """Stage 2: skims -> histograms (control_shapes.root or shapes.root); other histograms in the file are kept."""
+    from shapesmith.fill import run_hist
 
     cfg, analysis, selected = _setup(config, channels, workers=workers, overrides=overrides)
-    write_versions(cfg, cfg.output_dir)
-    hset = run_hist(
-        cfg,
-        analysis,
-        selected,
-        control,
-        variables.split(",") if variables else None,
-        not skip_systematics,
-        processes.split(",") if processes else None,
-        _shapes_path(cfg, control),
-        force,
-        regions.split(",") if regions else None,
-    )
-    typer.echo(f"{len(hset)} histograms in {_shapes_path(cfg, control)}")
+    write_versions(cfg, analysis.name, cfg.output_dir)
+    output = _shapes_path(cfg, control)
+    hset = run_hist(cfg, analysis, selected, control, _split(variables), not skip_systematics, _split(processes), output, _split(regions))
+    typer.echo(f"{len(hset)} histograms in {output}")
 
 
 @app.command()
 def estimate(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption, control: bool = typer.Option(False, "--control")):
-    """Add the estimated jet-fake process (and embedding variations) to the histogram file."""
-    from shapesmith.estimators import run_estimate
-    from shapesmith.histograms import HistogramSet
+    """Stage 3: add the estimated processes and variations of every channel to the histogram file."""
+    from shapesmith.estimates import run_estimates
+    from shapesmith.histogram import HistogramSet
 
     cfg, analysis, selected = _setup(config, channels, overrides=overrides)
     path = _shapes_path(cfg, control)
     hset = HistogramSet.load(path)
-    added = run_estimate(hset, analysis, selected)
+    added = run_estimates(hset, analysis, selected)
     hset.save(path)
     typer.echo(f"{len(added)} histograms added to {path}")
 
@@ -128,8 +116,8 @@ def estimate(config: Path = ConfigOption, channels: Optional[str] = ChannelsOpti
 @app.command()
 def sync(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption):
     """Write combine-style shape files per channel."""
-    from shapesmith.histograms import HistogramSet
-    from shapesmith.sync import run_sync
+    from shapesmith.histogram import HistogramSet
+    from shapesmith.shapes import run_sync
 
     cfg, analysis, selected = _setup(config, channels, overrides=overrides)
     for path in run_sync(HistogramSet.load(_shapes_path(cfg, False)), analysis, selected, cfg.output_dir):
@@ -140,7 +128,7 @@ def sync(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption,
 def datacards(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption, systematics: bool = typer.Option(True, "--systematics/--no-systematics"), min_background: Optional[float] = typer.Option(1.0, help="merge bins below this background yield; negative disables")):
     """Write text datacards and their shapes file per final state (each channel and the combination)."""
     from shapesmith.datacards import run_datacards
-    from shapesmith.histograms import HistogramSet
+    from shapesmith.histogram import HistogramSet
 
     cfg, analysis, selected = _setup(config, channels, overrides=overrides)
     threshold = None if min_background is None or min_background < 0 else min_background
@@ -151,11 +139,11 @@ def datacards(config: Path = ConfigOption, channels: Optional[str] = ChannelsOpt
 @app.command()
 def fit(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption, final_states: Optional[str] = typer.Option(None, help="comma separated; default: each channel and 'all'"), skip_combine: bool = typer.Option(False, "--skip-combine", help="only collect existing combine outputs")):
     """Run combine (expected limits, significance, best fit) and summarise the results."""
-    from shapesmith.fit import run_fit
+    from shapesmith.limits import run_limits
 
     cfg, _, selected = _setup(config, channels, overrides=overrides)
-    states = final_states.split(",") if final_states else list(_final_states(selected))
-    results = run_fit(cfg, cfg.output_dir / "datacards", states, skip_combine)
+    states = _split(final_states) or list(_final_states(selected))
+    results = run_limits(cfg, cfg.output_dir / "datacards", states, skip_combine)
     typer.echo((cfg.output_dir / "datacards" / "limits.md").read_text())
     typer.echo(f"{len(results)} final states summarised")
 
@@ -163,11 +151,11 @@ def fit(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, o
 @app.command()
 def plot(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption, control: bool = typer.Option(False, "--control"), category: Optional[str] = typer.Option(None), variables: Optional[str] = typer.Option(None), region: str = typer.Option("nominal", "--region", help="histogram region to plot"), blind: bool = typer.Option(False, "--blind"), log: bool = typer.Option(False, "--log"), signal_scale: Optional[float] = typer.Option(None), normalize_by_bin_width: bool = typer.Option(False, "--normalize-by-bin-width")):
     """Prefit stack plots of control variables (--control) or NN categories."""
-    from shapesmith.histograms import HistogramSet
+    from shapesmith.histogram import HistogramSet
     from shapesmith.plotting.stack import run_plot
 
     cfg, analysis, selected = _setup(config, channels, overrides=overrides)
-    files = run_plot(HistogramSet.load(_shapes_path(cfg, control)), analysis, selected, control, category, variables.split(",") if variables else None, cfg.output_dir / "plots", region=region, blind=blind, log=log, signal_scale=signal_scale, normalize_by_bin_width=normalize_by_bin_width)
+    files = run_plot(HistogramSet.load(_shapes_path(cfg, control)), analysis, selected, control, category, _split(variables), cfg.output_dir / "plots", region=region, blind=blind, log=log, signal_scale=signal_scale, normalize_by_bin_width=normalize_by_bin_width)
     typer.echo(f"{len(files)} files written to {cfg.output_dir / 'plots'}")
 
 
@@ -178,19 +166,23 @@ def ml_export(config: Path = ConfigOption, channels: Optional[str] = ChannelsOpt
 
     cfg, analysis, selected = _setup(config, channels, workers=workers, overrides=overrides)
     if cfg.ml_dir is not None:
-        write_versions(cfg, cfg.ml_dir)
+        write_versions(cfg, analysis.name, cfg.ml_dir)
     for path in run_ml_export(cfg, analysis, selected):
         typer.echo(f"wrote {path}")
 
 
 @app.command()
-def inspect(shapes: Path = typer.Argument(..., help="a shapes ROOT file written by `hist`")):
+def inspect(shapes: Path = typer.Argument(..., help="a shapes ROOT file written by `hist`"), unchanged: bool = typer.Option(False, "--unchanged", help="list the variations that are bitwise equal to their nominal")):
     """List the processes, regions, variations and variables in a histogram file."""
-    from shapesmith.histograms import HistogramSet
+    from shapesmith.histogram import HistogramSet, unchanged_variations
 
     hset = HistogramSet.load(shapes)
+    if unchanged:
+        for key in unchanged_variations(hset):
+            typer.echo(key.path)
+        return
     for field in ("channel", "category", "process", "region", "variation", "variable"):
-        values = sorted({getattr(key, field) for key in hset.keys()})
+        values = sorted({getattr(key, field) for key in hset})
         typer.echo(f"{field:10s} ({len(values)}): {', '.join(values[:40])}{' ...' if len(values) > 40 else ''}")
     typer.echo(f"{len(hset)} histograms")
 
diff --git a/src/shapesmith/cmssw.py b/src/shapesmith/cmssw.py
new file mode 100644
index 0000000..f1d40d7
--- /dev/null
+++ b/src/shapesmith/cmssw.py
@@ -0,0 +1,33 @@
+"""Run commands inside a CMSSW environment (combine, CombineHarvester) in a bash subshell."""
+from __future__ import annotations
+
+import logging
+import subprocess
+from pathlib import Path
+from typing import Sequence
+
+from shapesmith.config import CombineConfig
+
+logger = logging.getLogger(__name__)
+
+
+def script(combine: CombineConfig, cwd: Path, commands: Sequence[str]) -> str:
+    """The bash script: CMSSW environment of `combine.cmssw_dir`, then the commands in `cwd`, stopping at the first failure."""
+    return "\n".join(
+        [
+            "set -e",
+            f"export SCRAM_ARCH={combine.scram_arch}",
+            "source /cvmfs/cms.cern.ch/cmsset_default.sh",
+            f"cd {combine.cmssw_dir}/src",
+            "eval $(scramv1 runtime -sh)",
+            f"cd {Path(cwd).resolve()}",
+            *commands,
+        ]
+    )
+
+
+def run(commands: Sequence[str], combine: CombineConfig | None, cwd: Path) -> None:
+    if combine is None:
+        raise ValueError("the run configuration needs `combine` (cmssw_dir) to run CMSSW commands")
+    logger.info(f"running {len(commands)} CMSSW commands in {cwd}")
+    subprocess.run(["bash", "-c", script(combine, cwd, commands)], check=True)
diff --git a/src/shapesmith/config.py b/src/shapesmith/config.py
index a727fa3..7182d53 100644
--- a/src/shapesmith/config.py
+++ b/src/shapesmith/config.py
@@ -1,22 +1,16 @@
-"""Run configuration (YAML -> pydantic) and loading of the analysis object (Spec §13)."""
+"""Run configuration (YAML -> pydantic) and loading of the analysis object."""
 from __future__ import annotations
 
 import importlib
-import json
 import os
-import subprocess
-from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Iterable, Literal
+from typing import Any, Iterable
 
 import yaml
-
-from shapesmith import __version__
 from pydantic import BaseModel, ConfigDict, Field
 
-from shapesmith.model import Analysis
-
-SampleKind = Literal["data", "mc", "embedding"]
+from shapesmith.model import Analysis, SampleKind
+from shapesmith.validate import validate
 
 
 class FriendConfig(BaseModel):
@@ -88,58 +82,7 @@ def load_analysis(config: RunConfig) -> Analysis:
     module_name, _, function_name = config.analysis.partition(":")
     if not function_name:
         raise ValueError(f"analysis must be 'module:function', got {config.analysis!r}")
-    module = importlib.import_module(module_name)
-    build = getattr(module, function_name)
+    build = getattr(importlib.import_module(module_name), function_name)
     analysis = build(config)
-    analysis.validate()
+    validate(analysis)
     return analysis
-
-
-def git_hash(path: Path) -> str | None:
-    """HEAD commit of the git checkout containing `path`, or None if there is none (or git is missing)."""
-    path = Path(path)
-    directory = path.parent if path.is_file() else path
-    try:
-        return subprocess.run(["git", "-C", str(directory), "rev-parse", "HEAD"], capture_output=True, text=True, check=True).stdout.strip()
-    except (OSError, subprocess.CalledProcessError):
-        return None
-
-
-def write_versions(config: RunConfig, directory: Path) -> Path:
-    """Record what produced the contents of `directory`: versions, git hashes and the full run configuration."""
-    module = importlib.import_module(config.analysis.partition(":")[0])
-    record = {
-        "written": datetime.now(timezone.utc).isoformat(timespec="seconds"),
-        "shapesmith": {"version": __version__, "git": git_hash(Path(__file__))},
-        "analysis": {"module": config.analysis, "git": git_hash(Path(module.__file__))},
-        "sample_database": {"path": str(config.sample_database), "git": git_hash(config.sample_database)} if config.sample_database else None,
-        "config": json.loads(config.model_dump_json()),
-    }
-    directory = Path(directory)
-    directory.mkdir(parents=True, exist_ok=True)
-    path = directory / "versions.json"
-    path.write_text(json.dumps(record, indent=2))
-    return path
-
-
-EXAMPLE = """\
-analysis: my_analysis.analysis:build     # module:function returning an Analysis
-era: "2018"
-channels: [et, mt, tt]
-switches: {jet_fakes: mc, embedding: false}
-ntuples:
-  server: root://cmsdcache-kit-disk.gridka.de
-  base: /store/user/USER/CROWN/ntuples/TAG/CROWNRun
-  friends:
-    - {base: /store/user/USER/CROWN/ntuples/TAG/CROWNFriends/nn_v1, applies_to: [mc, data, embedding]}
-skim_dir: /ceph/USER/shapesmith/TAG
-output_dir: output/TAG_v1
-ml_dir: /ceph/USER/shapesmith_ml/v1
-sample_database: ../../KingMaker_sample_database/nanoAOD_v15/datasets.json   # relative paths: relative to this file
-workers: 16
-combine: {cmssw_dir: /work/USER/CMSSW_14_1_9, scram_arch: el9_amd64_gcc12}
-"""
-
-
-def write_example_config(path: str | Path) -> None:
-    Path(path).write_text(EXAMPLE)
diff --git a/src/shapesmith/datacards.py b/src/shapesmith/datacards.py
index 9fdbfa0..80596cd 100644
--- a/src/shapesmith/datacards.py
+++ b/src/shapesmith/datacards.py
@@ -1,31 +1,32 @@
-"""Native combine datacards: text card + shapes file, optional rebinning (Spec §10.2).
+"""Native combine datacards: text card + shapes file, optional rebinning.
 
-One card per final state lists every bin (channel x category); processes with a non-positive
-rate in a bin are left out of that bin. Systematics: lnN from Analysis.lnn, `shape` for every
-weight variation present, `* autoMCStats 0`.
+One card per final state lists every bin (channel x category); processes with a non-positive rate in a bin are left
+out of that bin. Systematics: lnN from Analysis.lnn, `shape` for every Up/Down pair of variations present (names
+without a direction are templates and never become shape lines), `* autoMCStats 0`.
 """
 from __future__ import annotations
 
+from dataclasses import dataclass
 from pathlib import Path
 
-import uproot
+from shapesmith.histogram import NOMINAL_VARIATION, HistKey, Histogram, HistogramSet
+from shapesmith.model import NOMINAL, Analysis, Category, LnN
+from shapesmith.shapes import shape_name, write_shapes
 
-from shapesmith.histogram import Histogram
-from shapesmith.histograms import NOMINAL_REGION, NOMINAL_VARIATION, HistKey, HistogramSet
-from shapesmith.model import Analysis, LnN
+
+@dataclass
+class Bin:
+    channel: str
+    name: str
+    data: Histogram
+    processes: dict[str, Histogram]  # the processes with a positive rate, rebinned, in card order
+    variations: dict[str, dict[str, Histogram]]  # process -> variation -> rebinned histogram
 
 
 def bin_name(analysis: Analysis, channel: str, category_index: int) -> str:
     return f"htt_{channel}_{category_index + 1}_{analysis.era}"
 
 
-def shape_processes(analysis: Analysis) -> tuple[str, ...]:
-    """Processes that carry shape systematics: simulated backgrounds and the signal."""
-    excluded = {analysis.estimator.output} if analysis.estimator else set()
-    excluded |= {p.name for p in analysis.processes_of_kind("embedding")}
-    return (*(p for p in analysis.backgrounds() if p not in excluded), analysis.signal)
-
-
 def rebin_edges(total_background: Histogram, min_background: float | None) -> list[float]:
     """Merge bins from the right until every bin holds at least `min_background`."""
     edges = list(total_background.edges)
@@ -44,6 +45,33 @@ def rebin_edges(total_background: Histogram, min_background: float | None) -> li
     return edges
 
 
+def collect_bin(hset: HistogramSet, analysis: Analysis, channel_name: str, index: int, category: Category, min_background: float | None) -> Bin | None:
+    """The rebinned data, processes and variations of one bin; None without data or processes."""
+    channel = analysis.channel(channel_name)
+    variable = category.variable.name
+
+    def key_of(process: str, variation: str = NOMINAL_VARIATION) -> HistKey:
+        return HistKey(channel_name, category.name, process, NOMINAL, variation, variable)
+
+    nominal = {p: hset[key_of(p)] for p in (analysis.signal, *channel.backgrounds()) if key_of(p) in hset}
+    if not nominal or key_of(channel.data()) not in hset:
+        return None
+    total = None
+    for process, h in nominal.items():
+        if process != analysis.signal:
+            total = h.copy() if total is None else total.add(h)
+    edges = rebin_edges(total, min_background) if total is not None else list(hset[key_of(channel.data())].edges)
+    result = Bin(channel_name, bin_name(analysis, channel_name, index), hset[key_of(channel.data())].rebin(edges), {}, {})
+    for process, h in nominal.items():
+        rebinned = h.rebin(edges)
+        if rebinned.sum() <= 0.0:
+            continue
+        result.processes[process] = rebinned
+        keys = hset.select(channel=channel_name, category=category.name, process=process, region=NOMINAL, variable=variable)
+        result.variations[process] = {key.variation: hset[key].rebin(edges) for key in keys if key.variation != NOMINAL_VARIATION}
+    return result
+
+
 def _value(lnn: LnN) -> str:
     if isinstance(lnn.value, tuple):
         down, up = lnn.value
@@ -51,91 +79,72 @@ def _value(lnn: LnN) -> str:
     return f"{lnn.value:g}"
 
 
-def lnn_lines(analysis: Analysis, bins: list[tuple[str, str]], processes_per_bin: dict[str, list[str]]) -> list[str]:
+def lnn_lines(analysis: Analysis, bins: list[Bin]) -> list[str]:
     """One `<name> lnN ...` line per concrete nuisance name ($CHANNEL/$ERA expanded), columns = (bin, process) pairs."""
     lines = []
     for lnn in analysis.lnn:
-        concrete_names = sorted({lnn.name.replace("$CHANNEL", channel).replace("$ERA", analysis.era) for channel, _ in bins})
+        concrete_names = sorted({lnn.name.replace("$CHANNEL", b.channel).replace("$ERA", analysis.era) for b in bins})
         for name in concrete_names:
             columns = []
-            for channel, bin_ in bins:
-                this_name = lnn.name.replace("$CHANNEL", channel).replace("$ERA", analysis.era)
-                for process in processes_per_bin[bin_]:
-                    applies = this_name == name and (lnn.channels is None or channel in lnn.channels) and ("*" in lnn.processes or process in lnn.processes)
+            for b in bins:
+                this_name = lnn.name.replace("$CHANNEL", b.channel).replace("$ERA", analysis.era)
+                for process in b.processes:
+                    applies = this_name == name and (lnn.channels is None or b.channel in lnn.channels) and ("*" in lnn.processes or process in lnn.processes)
                     columns.append(_value(lnn) if applies else "-")
             if any(column != "-" for column in columns):
                 lines.append(f"{name} lnN " + " ".join(columns))
     return lines
 
 
-def write_datacard(hset: HistogramSet, analysis: Analysis, channels: list[str], output_dir: Path, systematics: bool = True, min_background: float | None = 1.0) -> Path:
-    output_dir = Path(output_dir)
-    (output_dir / "common").mkdir(parents=True, exist_ok=True)
-    shapes_file = output_dir / "common" / f"htt_input_{analysis.era}.root"
-    processes = (analysis.signal, *analysis.backgrounds())
-    bins: list[tuple[str, str]] = []
-    processes_per_bin: dict[str, list[str]] = {}
-    observations: dict[str, float] = {}
-    variations_per_bin: dict[str, dict[str, set[str]]] = {}
-    with uproot.recreate(shapes_file) as f:
-        for channel in channels:
-            for index, category in enumerate(analysis.categories):
-                bin_ = bin_name(analysis, channel, index)
-                variable = category.variable.name
-
-                def key_of(process, variation=NOMINAL_VARIATION):
-                    return HistKey(channel, category.name, process, NOMINAL_REGION, variation, variable)
-
-                nominal = {p: hset.get(key_of(p)) for p in processes if hset.has(key_of(p))}
-                if not nominal or not hset.has(key_of("data")):
-                    continue
-                total = None
-                for process, h in nominal.items():
-                    if process == analysis.signal:
-                        continue
-                    total = h.copy() if total is None else total.add(h)
-                edges = rebin_edges(total, min_background) if total is not None else list(hset.get(key_of("data")).edges)
-                data = hset.get(key_of("data")).rebin(edges)
-                f[f"{bin_}/data_obs"] = data.to_root("data_obs")
-                observations[bin_] = data.sum()
-                kept = []
-                variations_per_bin[bin_] = {}
-                for process, h in nominal.items():
-                    rebinned = h.rebin(edges)
-                    if rebinned.sum() <= 0.0:
-                        continue
-                    kept.append(process)
-                    f[f"{bin_}/{process}"] = rebinned.to_root(process)
-                    variations_per_bin[bin_][process] = set()
-                    for key in hset.keys(channel=channel, category=category.name, process=process, region=NOMINAL_REGION, variable=variable):
-                        if key.variation != NOMINAL_VARIATION:
-                            f[f"{bin_}/{process}_{key.variation}"] = hset.get(key).rebin(edges).to_root(f"{process}_{key.variation}")
-                            variations_per_bin[bin_][process].add(key.variation)
-                bins.append((channel, bin_))
-                processes_per_bin[bin_] = kept
+def shape_lines(bins: list[Bin]) -> list[str]:
+    columns = [(b, process) for b in bins for process in b.processes]
+    names = sorted({v[: -len("Up")] for b in bins for variations in b.variations.values() for v in variations if v.endswith("Up")})
+    lines = []
+    for name in names:
+        entries = ["1" if {f"{name}Up", f"{name}Down"} <= set(b.variations[process]) else "-" for b, process in columns]
+        if "1" in entries:
+            lines.append(f"{name} shape " + " ".join(entries))
+    return lines
+
+
+def card_text(analysis: Analysis, bins: list[Bin], systematics: bool) -> str:
+    backgrounds = list(dict.fromkeys(p for b in bins for p in analysis.channel(b.channel).backgrounds()))
+    index = {name: i for i, name in enumerate((analysis.signal, *backgrounds))}
+    columns = [(b.name, process) for b in bins for process in b.processes]
     lines = ["imax * number of bins", "jmax * number of processes minus 1", "kmax * number of nuisance parameters", "-" * 60]
-    for _, bin_ in bins:
-        lines.append(f"shapes * {bin_} common/htt_input_{analysis.era}.root {bin_}/$PROCESS {bin_}/$PROCESS_$SYSTEMATIC")
+    lines += [f"shapes * {b.name} common/htt_input_{analysis.era}.root {b.name}/$PROCESS {b.name}/$PROCESS_$SYSTEMATIC" for b in bins]
     lines.append("-" * 60)
-    lines.append("bin " + " ".join(bin_ for _, bin_ in bins))
-    lines.append("observation " + " ".join(f"{observations[bin_]:.1f}" for _, bin_ in bins))
+    lines.append("bin " + " ".join(b.name for b in bins))
+    lines.append("observation " + " ".join(f"{b.data.sum():.1f}" for b in bins))
     lines.append("-" * 60)
-    columns = [(bin_, process) for _, bin_ in bins for process in processes_per_bin[bin_]]
-    lines.append("bin " + " ".join(bin_ for bin_, _ in columns))
+    lines.append("bin " + " ".join(name for name, _ in columns))
     lines.append("process " + " ".join(process for _, process in columns))
-    lines.append("process " + " ".join("0" if process == analysis.signal else str(processes.index(process)) for _, process in columns))
+    lines.append("process " + " ".join(str(index[process]) for _, process in columns))
     lines.append("rate " + " ".join("-1" for _ in columns))
     lines.append("-" * 60)
     if systematics:
-        lines += lnn_lines(analysis, bins, processes_per_bin)
-        systematic_names = sorted({v[: -len("Up")] for per_bin in variations_per_bin.values() for vs in per_bin.values() for v in vs if v.endswith("Up")})
-        for name in systematic_names:
-            entries = ["1" if {f"{name}Up", f"{name}Down"} <= variations_per_bin[bin_].get(process, set()) else "-" for bin_, process in columns]
-            if any(entry == "1" for entry in entries):
-                lines.append(f"{name} shape " + " ".join(entries))
+        lines += lnn_lines(analysis, bins) + shape_lines(bins)
     lines.append("* autoMCStats 0")
+    return "\n".join(lines) + "\n"
+
+
+def write_datacard(hset: HistogramSet, analysis: Analysis, channels: list[str], output_dir: Path, systematics: bool = True, min_background: float | None = 1.0) -> Path:
+    output_dir = Path(output_dir)
+    bins = [
+        b
+        for channel in channels
+        for index, category in enumerate(analysis.channel(channel).categories)
+        if (b := collect_bin(hset, analysis, channel, index, category, min_background)) is not None
+    ]
+    entries = []
+    for b in bins:
+        entries.append((b.name, "data_obs", b.data))
+        for process, h in b.processes.items():
+            entries.append((b.name, process, h))
+            entries += [(b.name, shape_name(process, variation), varied) for variation, varied in b.variations[process].items()]
+    write_shapes(output_dir / "common" / f"htt_input_{analysis.era}.root", entries)
     card = output_dir / "combined.txt"
-    card.write_text("\n".join(lines) + "\n")
+    card.write_text(card_text(analysis, bins, systematics))
     return card
 
 
diff --git a/src/shapesmith/estimates.py b/src/shapesmith/estimates.py
new file mode 100644
index 0000000..548ded7
--- /dev/null
+++ b/src/shapesmith/estimates.py
@@ -0,0 +1,116 @@
+"""Stage 3: processes estimated from histograms, run per channel in the order of `Channel.estimators`.
+
+DataMinus: data minus the subtracted processes in its region, for the nominal and every column variation filled on
+data or a subtracted process there; an input without that variation contributes its nominal (under the fill rule it
+is unchanged). Weight variations are not propagated.
+ABCD: (data - MC)(B) * (data - MC)(C).sum() / (data - MC)(D).sum(), nominal only, negative bins clipped.
+TemplateShift: `process` +/- `fraction` * `template`, e.g. the ttbar contamination of the embedded sample.
+"""
+from __future__ import annotations
+
+import logging
+
+from shapesmith.histogram import NOMINAL_VARIATION, HistKey, Histogram, HistogramSet
+from shapesmith.model import ABCD, NOMINAL, Analysis, Channel, ColumnVariation, DataMinus, TemplateShift
+
+logger = logging.getLogger(__name__)
+
+
+def data_minus(hset: HistogramSet, channel: Channel, category: str, variable: str, region: str, subtract: tuple[str, ...], variation: str = NOMINAL_VARIATION) -> Histogram | None:
+    """data[region] - sum of the given processes[region] under `variation` (nominal where an input lacks it);
+    None if the data histogram is missing."""
+
+    def get(process: str) -> Histogram | None:
+        key = HistKey(channel.name, category, process, region, variation, variable)
+        if key not in hset:
+            key = HistKey(channel.name, category, process, region, NOMINAL_VARIATION, variable)
+        return hset.get(key)
+
+    data = get(channel.data())
+    if data is None:
+        return None
+    result = data.copy()
+    for process in subtract:
+        h = get(process)
+        if h is None:
+            logger.warning(f"{channel.name}_{category}/{process}#{region}#{variable} missing, not subtracted")
+            continue
+        result.add(h, -1.0)
+    return result
+
+
+def categories_and_variables(hset: HistogramSet, channel: Channel, region: str) -> list[tuple[str, str]]:
+    return sorted({(k.category, k.variable) for k in hset.select(channel=channel.name, process=channel.data(), region=region, variation=NOMINAL_VARIATION)})
+
+
+def estimate_data_minus(hset: HistogramSet, channel: Channel, estimator: DataMinus) -> list[HistKey]:
+    column_variations = {v.name for v in channel.variations if isinstance(v, ColumnVariation)}
+    inputs = (channel.data(), *estimator.subtract)
+    added = []
+    for category, variable in categories_and_variables(hset, channel, estimator.region):
+        present = {k.variation for process in inputs for k in hset.select(channel=channel.name, category=category, process=process, region=estimator.region, variable=variable)}
+        for variation in (NOMINAL_VARIATION, *sorted(present & column_variations)):
+            h = data_minus(hset, channel, category, variable, estimator.region, estimator.subtract, variation)
+            key = HistKey(channel.name, category, estimator.output, NOMINAL, variation, variable)
+            hset[key] = h.scale(estimator.scale)
+            added.append(key)
+    return added
+
+
+def clip_negative_bins(h: Histogram) -> Histogram:
+    """Set negative bins to zero and rescale to the original integral (zero everything if the integral is <= 0)."""
+    total = h.sum()
+    positive = float(h.values[h.values > 0].sum())
+    h.values[h.values < 0] = 0.0
+    if total <= 0.0:
+        logger.warning(f"integral {total:.3f} <= 0 after subtraction, histogram set to zero")
+        h.values[:] = 0.0
+        h.variances[:] = 0.0
+    elif positive > 0.0 and positive != total:
+        h.scale(total / positive)
+    return h
+
+
+def estimate_abcd(hset: HistogramSet, channel: Channel, estimator: ABCD) -> list[HistKey]:
+    added = []
+    for category, variable in categories_and_variables(hset, channel, estimator.b):
+        b, c, d = (data_minus(hset, channel, category, variable, region, estimator.subtract) for region in (estimator.b, estimator.c, estimator.d))
+        if b is None or c is None or d is None:
+            logger.warning(f"{channel.name}/{category}/{variable}: ABCD control region missing, skipped")
+            continue
+        yield_c, yield_d = c.sum(), d.sum()
+        if yield_c <= 0.0 or yield_d <= 0.0:
+            logger.warning(f"{channel.name}/{category}/{variable}: C={yield_c:.2f}, D={yield_d:.2f}, {estimator.output} set to zero")
+            factor = 0.0
+        else:
+            factor = yield_c / yield_d
+        key = HistKey(channel.name, category, estimator.output, NOMINAL, NOMINAL_VARIATION, variable)
+        hset[key] = clip_negative_bins(b.scale(factor))
+        added.append(key)
+    return added
+
+
+def estimate_template_shift(hset: HistogramSet, channel: Channel, estimator: TemplateShift) -> list[HistKey]:
+    added = []
+    for key in hset.select(channel=channel.name, process=estimator.process, region=NOMINAL, variation=NOMINAL_VARIATION):
+        template = HistKey(channel.name, key.category, estimator.template, NOMINAL, NOMINAL_VARIATION, key.variable)
+        if template not in hset:
+            logger.warning(f"{template.path} missing, no {estimator.name} variation")
+            continue
+        for direction, sign in (("Up", 1.0), ("Down", -1.0)):
+            varied = HistKey(channel.name, key.category, estimator.process, NOMINAL, f"{estimator.name}{direction}", key.variable)
+            hset[varied] = hset[key].copy().add(hset[template], sign * estimator.fraction)
+            added.append(varied)
+    return added
+
+
+ESTIMATES = {DataMinus: estimate_data_minus, ABCD: estimate_abcd, TemplateShift: estimate_template_shift}
+
+
+def run_estimates(hset: HistogramSet, analysis: Analysis, channels: list[str]) -> list[HistKey]:
+    added: list[HistKey] = []
+    for name in channels:
+        channel = analysis.channel(name)
+        for estimator in channel.estimators:
+            added += ESTIMATES[type(estimator)](hset, channel, estimator)
+    return added
diff --git a/src/shapesmith/estimators/__init__.py b/src/shapesmith/estimators/__init__.py
deleted file mode 100644
index 3bc6420..0000000
--- a/src/shapesmith/estimators/__init__.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""Derived processes computed from histograms of the estimation regions (Spec §9)."""
-from __future__ import annotations
-
-import logging
-
-from shapesmith.histogram import Histogram
-from shapesmith.histograms import HistKey, HistogramSet
-from shapesmith.model import Analysis
-
-logger = logging.getLogger(__name__)
-
-
-def data_minus(hset: HistogramSet, channel: str, category: str, variable: str, region: str, subtract: tuple[str, ...]) -> Histogram | None:
-    """data[region] - sum of the given processes[region]; None if the data histogram is missing."""
-    data_key = HistKey(channel, category, "data", region, "Nominal", variable)
-    if not hset.has(data_key):
-        return None
-    result = hset.get(data_key).copy()
-    for process in subtract:
-        key = HistKey(channel, category, process, region, "Nominal", variable)
-        if not hset.has(key):
-            logger.warning(f"{key.path} missing, not subtracted")
-            continue
-        result.add(hset.get(key), -1.0)
-    return result
-
-
-def categories_and_variables(hset: HistogramSet, channel: str, region: str) -> list[tuple[str, str]]:
-    return sorted({(k.category, k.variable) for k in hset.keys(channel=channel, process="data", region=region)})
-
-
-def run_estimate(hset: HistogramSet, analysis: Analysis, channels: list[str]) -> list[HistKey]:
-    from shapesmith.estimators import abcd, embedding, fake_factors  # local import: the modules import this package
-
-    added: list[HistKey] = []
-    for channel in channels:
-        if analysis.estimator is not None:
-            estimate = {"fake_factors": fake_factors.estimate, "abcd": abcd.estimate}[analysis.estimator.name]
-            added += estimate(hset, analysis, channel)
-        if analysis.processes_of_kind("embedding"):
-            added += embedding.ttbar_contamination(hset, analysis, channel)
-    return added
diff --git a/src/shapesmith/estimators/abcd.py b/src/shapesmith/estimators/abcd.py
deleted file mode 100644
index 744c4b0..0000000
--- a/src/shapesmith/estimators/abcd.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""ABCD method: QCD(A) = (data - MC)(B) * (data - MC)(C).sum() / (data - MC)(D).sum().
-
-A = signal region (OS, isolated), B = OS anti-isolated, C = SS isolated, D = SS anti-isolated.
-"""
-from __future__ import annotations
-
-import logging
-
-from shapesmith.estimators import categories_and_variables, data_minus
-from shapesmith.histogram import Histogram
-from shapesmith.histograms import NOMINAL_REGION, NOMINAL_VARIATION, HistKey, HistogramSet
-from shapesmith.model import Analysis
-
-logger = logging.getLogger(__name__)
-
-
-def clip_negative_bins(h: Histogram) -> Histogram:
-    """Set negative bins to zero and rescale to the original integral (zero everything if the integral is <= 0)."""
-    total = h.sum()
-    positive = float(h.values[h.values > 0].sum())
-    h.values[h.values < 0] = 0.0
-    if total <= 0.0:
-        logger.warning(f"integral {total:.3f} <= 0 after subtraction, histogram set to zero")
-        h.values[:] = 0.0
-        h.variances[:] = 0.0
-    elif positive > 0.0 and positive != total:
-        h.scale(total / positive)
-    return h
-
-
-def estimate(hset: HistogramSet, analysis: Analysis, channel: str) -> list[HistKey]:
-    estimator = analysis.estimator
-    regions = estimator.regions
-    added = []
-    for category, variable in categories_and_variables(hset, channel, regions["B"]):
-        b = data_minus(hset, channel, category, variable, regions["B"], estimator.subtract)
-        c = data_minus(hset, channel, category, variable, regions["C"], estimator.subtract)
-        d = data_minus(hset, channel, category, variable, regions["D"], estimator.subtract)
-        if b is None or c is None or d is None:
-            logger.warning(f"{channel}/{category}/{variable}: ABCD control region missing, skipped")
-            continue
-        yield_c, yield_d = c.sum(), d.sum()
-        if yield_c <= 0.0 or yield_d <= 0.0:
-            logger.warning(f"{channel}/{category}/{variable}: C={yield_c:.2f}, D={yield_d:.2f}, QCD set to zero")
-            factor = 0.0
-        else:
-            factor = yield_c / yield_d
-        key = HistKey(channel, category, estimator.output, NOMINAL_REGION, NOMINAL_VARIATION, variable)
-        hset.add(key, clip_negative_bins(b.scale(factor)))
-        added.append(key)
-    return added
diff --git a/src/shapesmith/estimators/embedding.py b/src/shapesmith/estimators/embedding.py
deleted file mode 100644
index 199f353..0000000
--- a/src/shapesmith/estimators/embedding.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""ttbar contamination variation of the embedded sample: EMB -/+ 10 % of the genuine-tau ttbar histogram."""
-from __future__ import annotations
-
-import logging
-
-from shapesmith.histograms import NOMINAL_REGION, NOMINAL_VARIATION, HistKey, HistogramSet
-from shapesmith.model import Analysis
-
-logger = logging.getLogger(__name__)
-TTBAR_GENUINE = "TTT"
-FRACTION = 0.1
-
-
-def ttbar_contamination(hset: HistogramSet, analysis: Analysis, channel: str) -> list[HistKey]:
-    added = []
-    for emb in analysis.processes_of_kind("embedding"):
-        for key in hset.keys(channel=channel, process=emb.name, region=NOMINAL_REGION, variation=NOMINAL_VARIATION):
-            ttbar_key = HistKey(channel, key.category, TTBAR_GENUINE, NOMINAL_REGION, NOMINAL_VARIATION, key.variable)
-            if not hset.has(ttbar_key):
-                logger.warning(f"{ttbar_key.path} missing, no embedding ttbar variation")
-                continue
-            for direction, sign in (("Down", -1.0), ("Up", +1.0)):
-                varied = hset.get(key).copy().add(hset.get(ttbar_key), sign * FRACTION)
-                new_key = HistKey(channel, key.category, emb.name, NOMINAL_REGION, f"CMS_htt_emb_ttbar_{analysis.era}{direction}", key.variable)
-                hset.add(new_key, varied)
-                added.append(new_key)
-    return added
diff --git a/src/shapesmith/estimators/fake_factors.py b/src/shapesmith/estimators/fake_factors.py
deleted file mode 100644
index c4ae1ef..0000000
--- a/src/shapesmith/estimators/fake_factors.py
+++ /dev/null
@@ -1,20 +0,0 @@
-"""Fake-factor method: jetFakes = FF-weighted anti-isolated data minus genuine-tau and lepton-fake MC."""
-from __future__ import annotations
-
-from shapesmith.estimators import categories_and_variables, data_minus
-from shapesmith.histograms import NOMINAL_REGION, NOMINAL_VARIATION, HistKey, HistogramSet
-from shapesmith.model import Analysis
-
-
-def estimate(hset: HistogramSet, analysis: Analysis, channel: str) -> list[HistKey]:
-    estimator = analysis.estimator
-    region = estimator.regions["anti_iso"]
-    added = []
-    for category, variable in categories_and_variables(hset, channel, region):
-        h = data_minus(hset, channel, category, variable, region, estimator.subtract)
-        if h is None:
-            continue
-        key = HistKey(channel, category, estimator.output, NOMINAL_REGION, NOMINAL_VARIATION, variable)
-        hset.add(key, h)
-        added.append(key)
-    return added
diff --git a/src/shapesmith/events.py b/src/shapesmith/events.py
new file mode 100644
index 0000000..da89cb3
--- /dev/null
+++ b/src/shapesmith/events.py
@@ -0,0 +1,74 @@
+"""The one rule that turns a process in a region under a variation into cuts and weights, and loads its events.
+
+Cuts: the channel cuts with the region's replacements, the process cuts and the skim cuts. The skim cuts are a no-op
+on nominal rows; under a variation they keep out the events a wider skim stored for other variations.
+Weights, in order: the process weights, with the region's replacements where the process carries the name, then the
+region's added weights. A weight variation replaces weights in place and does not apply to a process lacking one of
+them; a column variation rewrites every cut and weight (expressions.shift).
+Event weight: (norm_weight * lumi) * product of the weights, with lumi only for simulated (mc) processes.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Iterable
+
+import numpy as np
+import pandas as pd
+
+from shapesmith.config import RunConfig
+from shapesmith.expressions import columns_of, mask, product, shift
+from shapesmith.model import NOMINAL, Analysis, Channel, ColumnVariation, Process, Region, Variation, WeightVariation
+from shapesmith.store import SKIM_COLUMNS, read_skims, schema
+
+
+@dataclass(frozen=True)
+class Query:
+    channel: str
+    process: str
+    region: str = NOMINAL
+    variation: Variation | None = None
+
+
+@dataclass
+class Events:
+    frame: pd.DataFrame
+    weights: np.ndarray
+
+
+def select(channel: Channel, process: Process, region: Region, variation: Variation | None = None, available: set[str] = frozenset()) -> tuple[list[str], list[str]] | None:
+    """Cut and weight expressions of `process` in `region` under `variation`; None if the variation does not apply."""
+    weights = {name: region.replace_weights.get(name, expr) for name, expr in process.selection.weights.items()}
+    weights.update(region.add_weights)
+    if isinstance(variation, WeightVariation):
+        if any(name not in weights for name in variation.replace_weights):
+            return None
+        weights.update(variation.replace_weights)
+    cuts = [*{**channel.cuts, **region.replace_cuts}.values(), *process.selection.cuts.values(), *channel.skim.values()]
+    weights = list(weights.values())
+    if isinstance(variation, ColumnVariation):
+        cuts = [shift(expr, variation, available) for expr in cuts]
+        weights = [shift(expr, variation, available) for expr in weights]
+    return cuts, weights
+
+
+def lumi(analysis: Analysis, channel: Channel, process: Process) -> float:
+    return analysis.lumi_pb if channel.kind_of(process) == "mc" else 1.0
+
+
+def event_weights(frame: pd.DataFrame, weights: Iterable[str], lumi_pb: float) -> np.ndarray:
+    return frame["norm_weight"].to_numpy() * lumi_pb * product(frame, weights)
+
+
+def load(config: RunConfig, analysis: Analysis, query: Query, columns: Iterable[str] = ()) -> Events:
+    """The selected events of a query (one frame, in sample order) with their weights; `columns` are read in addition."""
+    channel = analysis.channel(query.channel)
+    process = channel.process(query.process)
+    nicks = [s.nick for s in channel.samples_of(process.group)]
+    available = schema(config.skim_dir, channel.name, nicks) if isinstance(query.variation, ColumnVariation) else set()
+    selection = select(channel, process, channel.region(query.region), query.variation, available)
+    if selection is None:
+        raise ValueError(f"{query.channel}/{query.process}: variation {query.variation.name} does not apply")
+    cuts, weights = selection
+    frame = read_skims(config.skim_dir, channel.name, nicks, set(SKIM_COLUMNS) | columns_of(cuts + weights) | set(columns))
+    selected = frame[mask(frame, cuts)].reset_index(drop=True)
+    return Events(selected, event_weights(selected, weights, lumi(analysis, channel, process)))
diff --git a/src/shapesmith/expressions.py b/src/shapesmith/expressions.py
index cd5c79b..9ec17f0 100644
--- a/src/shapesmith/expressions.py
+++ b/src/shapesmith/expressions.py
@@ -1,15 +1,14 @@
-"""Evaluation of cut and weight expressions (pandas.eval syntax, numexpr engine), Spec §6."""
+"""Evaluation of cut and weight expressions (pandas.eval syntax, numexpr engine) and their column variations."""
 from __future__ import annotations
 
-import re
-from typing import Mapping
-
 import logging
+import re
+from typing import Iterable
 
 import numpy as np
 import pandas as pd
 
-from shapesmith.model import Region, Selection, WeightVariation
+from shapesmith.model import ColumnVariation
 
 FUNCTIONS = frozenset(
     "abs sqrt exp log log10 sin cos tan arctan2 arcsin arccos arctan sinh cosh tanh expm1 log1p".split()
@@ -17,7 +16,6 @@
 KEYWORDS = frozenset({"and", "or", "not", "True", "False", "inf", "nan"})
 _IDENTIFIER = re.compile(r"(?<![\w.])[A-Za-z_][A-Za-z0-9_]*")
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -30,6 +28,13 @@ def columns_in(expr: str) -> set[str]:
     return {token for token in _IDENTIFIER.findall(expr) if token not in FUNCTIONS and token not in KEYWORDS}
 
 
+def columns_of(exprs: Iterable[str]) -> set[str]:
+    columns: set[str] = set()
+    for expr in exprs:
+        columns |= columns_in(expr)
+    return columns
+
+
 def evaluate(frame: pd.DataFrame, expr: str) -> np.ndarray:
     """Evaluate `expr` on `frame`; scalars are broadcast to one value per row."""
     missing = sorted(columns_in(expr) - set(frame.columns))
@@ -50,57 +55,31 @@ def evaluate(frame: pd.DataFrame, expr: str) -> np.ndarray:
     return np.asarray(result)
 
 
-def mask(frame: pd.DataFrame, cuts: Mapping[str, str]) -> np.ndarray:
-    """Logical AND of all cuts (all True for an empty mapping)."""
+def mask(frame: pd.DataFrame, cuts: Iterable[str]) -> np.ndarray:
+    """Logical AND of all cuts (all True for none)."""
     result = np.ones(len(frame), dtype=bool)
-    for expr in cuts.values():
+    for expr in cuts:
         result &= evaluate(frame, expr).astype(bool)
     return result
 
 
-def weight(frame: pd.DataFrame, weights: Mapping[str, str]) -> np.ndarray:
-    """Product of all weights as float64 (all 1.0 for an empty mapping)."""
+def product(frame: pd.DataFrame, weights: Iterable[str]) -> np.ndarray:
+    """Product of all weights as float64, in the given order (all 1.0 for none)."""
     result = np.ones(len(frame), dtype=np.float64)
-    for expr in weights.values():
+    for expr in weights:
         result *= evaluate(frame, expr).astype(np.float64)
     return result
 
 
-def apply_region(selection: Selection, region: Region) -> Selection:
-    """Replace the named cuts of `selection` and append the region weights."""
-    cuts = dict(selection.cuts)
-    for name, expr in region.replace_cuts.items():
-        if name not in cuts:
-            raise KeyError(f"region {region.name}: cut {name!r} not in selection {sorted(cuts)}")
-        cuts[name] = expr
-    weights = dict(selection.weights)
-    weights.update(region.add_weights)
-    return Selection(cuts=cuts, weights=weights)
-
-
-def apply_weight_variation(selection: Selection, variation: WeightVariation) -> Selection:
-    """Replace the named weights of `selection` (weights not present are an error)."""
-    weights = dict(selection.weights)
-    for name, expr in variation.replace_weights.items():
-        if name not in weights:
-            raise KeyError(f"variation {variation.name}: weight {name!r} not in selection {sorted(weights)}")
-        weights[name] = expr
-    return Selection(cuts=dict(selection.cuts), weights=weights)
-
-
-def apply_column_variation(expr: str, suffix: str, available: set[str]) -> str:
-    """Rename every column `c` in `expr` to `c + suffix` if that shifted column exists."""
+def shift(expr: str, variation: ColumnVariation, available: set[str]) -> str:
+    """`expr` under a column variation: a derived column becomes its expression, a column `c` whose shifted branch
+    `c + suffix` is in `available` is read shifted, and every other column stays nominal."""
 
     def rename(match: re.Match) -> str:
         name = match.group(0)
-        shifted = name + suffix
-        return shifted if name in available and shifted in available else name
+        if name in variation.derived:
+            return f"({variation.derived[name]})"
+        shifted = name + variation.suffix
+        return shifted if variation.suffix and shifted in available else name
 
     return _IDENTIFIER.sub(rename, expr)
-
-
-def selection_columns(selection: Selection) -> set[str]:
-    columns: set[str] = set()
-    for expr in list(selection.cuts.values()) + list(selection.weights.values()):
-        columns |= columns_in(expr)
-    return columns
diff --git a/src/shapesmith/fill.py b/src/shapesmith/fill.py
new file mode 100644
index 0000000..0494ebe
--- /dev/null
+++ b/src/shapesmith/fill.py
@@ -0,0 +1,167 @@
+"""Stage 2: skims -> histograms per process, region, category, variable and variation.
+
+Bookings: the signal in the nominal region, auxiliary processes in the nominal region without variations, every
+other process in the nominal region and the estimator regions (or the explicitly requested regions). A variation is
+filled for the processes whose sample kind it applies to, in its regions; a column variation only where it changes
+an expression of the histogram (elsewhere it equals the nominal, and consumers fall back to that).
+"""
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+
+from shapesmith.config import RunConfig
+from shapesmith.events import event_weights, lumi, select
+from shapesmith.expressions import columns_of, evaluate, mask, shift
+from shapesmith.histogram import INCLUSIVE, NOMINAL_VARIATION, HistKey, Histogram, HistogramSet
+from shapesmith.model import ABCD, NOMINAL, Analysis, Channel, ColumnVariation, DataMinus, Process, Variable, Variation
+from shapesmith.parallel import run_jobs
+from shapesmith.store import SKIM_COLUMNS, read_skims, schema
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True)
+class Booking:
+    process: Process
+    regions: tuple[str, ...]
+    variations: tuple[Variation, ...]
+
+
+@dataclass(frozen=True)
+class Target:
+    category: str
+    cut: str | None
+    variable: Variable
+
+
+def estimator_regions(channel: Channel) -> tuple[str, ...]:
+    regions = []
+    for estimator in channel.estimators:
+        if isinstance(estimator, DataMinus):
+            regions.append(estimator.region)
+        elif isinstance(estimator, ABCD):
+            regions += [estimator.b, estimator.c, estimator.d]
+    return tuple(dict.fromkeys(regions))
+
+
+def resolve_regions(channel: Channel, regions: list[str] | None) -> tuple[str, ...] | None:
+    """An explicit region request; `all` expands to the nominal and every region of the channel."""
+    if regions is None:
+        return None
+    names = (NOMINAL, *(region.name for region in channel.regions)) if "all" in regions else tuple(regions)
+    resolved = tuple(dict.fromkeys(names))
+    for name in resolved:
+        channel.region(name)  # validates with a channel-specific error
+    return resolved
+
+
+def bookings(channel: Channel, systematics: bool, regions: list[str] | None = None) -> list[Booking]:
+    requested = resolve_regions(channel, regions)
+    result = []
+    for process in channel.processes:
+        if process.role == "auxiliary":
+            if requested is None or NOMINAL in requested:
+                result.append(Booking(process, (NOMINAL,), ()))
+            continue
+        if requested is not None:
+            process_regions = requested
+        elif process.role == "signal":
+            process_regions = (NOMINAL,)
+        else:
+            process_regions = tuple(dict.fromkeys((NOMINAL, *estimator_regions(channel))))
+        kind = channel.kind_of(process)
+        variations = tuple(v for v in channel.variations if kind in v.applies_to) if systematics else ()
+        result.append(Booking(process, process_regions, variations))
+    return result
+
+
+def targets(channel: Channel, control: bool, variables: list[str] | None) -> list[Target]:
+    if control:
+        names = variables or list(channel.variables)
+        return [Target(INCLUSIVE, None, channel.variables[name]) for name in names]  # KeyError for unknown variables
+    return [Target(c.name, c.cut, c.variable) for c in channel.categories]
+
+
+def _target_exprs(target: Target, variation: Variation | None, available: set[str]) -> tuple[str | None, str]:
+    if not isinstance(variation, ColumnVariation):
+        return target.cut, target.variable.expr
+    return (shift(target.cut, variation, available) if target.cut else None), shift(target.variable.expr, variation, available)
+
+
+def fill_booking(config: RunConfig, analysis: Analysis, channel_name: str, booking: Booking, targets_: list[Target]) -> dict[HistKey, Histogram]:
+    """All histograms of one booking, from one frame of the process's skims."""
+    channel = analysis.channel(channel_name)
+    process = booking.process
+    nicks = [s.nick for s in channel.samples_of(process.group)]
+    available = schema(config.skim_dir, channel_name, nicks)
+    plan = []  # (region, variation, cuts, weights, [(target, cut, expression)])
+    for region_name in booking.regions:
+        region = channel.region(region_name)
+        nominal = select(channel, process, region)
+        for variation in (None, *(v for v in booking.variations if v.regions is None or region_name in v.regions)):
+            selection = select(channel, process, region, variation, available)
+            if selection is None:
+                logger.debug(f"{process.name}: variation {variation.name} not applicable, skipped")
+                continue
+            exprs = [(target, *_target_exprs(target, variation, available)) for target in targets_]
+            if isinstance(variation, ColumnVariation):
+                unchanged = selection == nominal
+                exprs = [(target, cut, expr) for target, cut, expr in exprs if not unchanged or (cut, expr) != (target.cut, target.variable.expr)]
+            if exprs:
+                plan.append((region_name, variation, *selection, exprs))
+    read = []
+    for _, _, cuts, weights, exprs in plan:
+        read += cuts + weights + [e for _, cut, expr in exprs for e in (cut, expr) if e]
+    frame = read_skims(config.skim_dir, channel_name, nicks, set(SKIM_COLUMNS) | columns_of(read))
+    lumi_pb = lumi(analysis, channel, process)
+    result: dict[HistKey, Histogram] = {}
+    for region_name, variation, cuts, weights, exprs in plan:
+        selected = frame[mask(frame, cuts)]
+        w = event_weights(selected, weights, lumi_pb)
+        for target, cut, expr in exprs:
+            in_category = mask(selected, [cut]) if cut else np.ones(len(selected), dtype=bool)
+            values = evaluate(selected, expr).astype(np.float64)
+            finite = np.isfinite(values) & np.isfinite(w)
+            if (in_category & ~finite).any():
+                logger.warning(f"{process.name}/{region_name}/{target.variable.name}: {(in_category & ~finite).sum()} events with NaN/inf skipped")
+            keep = in_category & finite
+            key = HistKey(channel_name, target.category, process.name, region_name, variation.name if variation else NOMINAL_VARIATION, target.variable.name)
+            result[key] = Histogram.fill(target.variable.edges, values[keep], w[keep])
+    return result
+
+
+def _fill_job(args) -> dict[HistKey, Histogram]:
+    return fill_booking(*args)
+
+
+def run_hist(config: RunConfig, analysis: Analysis, channels: list[str] | None, control: bool, variables: list[str] | None, systematics: bool, processes: list[str] | None, output: Path, regions: list[str] | None = None) -> HistogramSet:
+    """Fill every booked histogram of the requested channels into `output` (+ .json index).
+
+    The requested scopes (channel, process, region, category, variable) are always filled; histograms of other
+    scopes already in `output` are kept."""
+    output = Path(output)
+    jobs, scopes = [], set()
+    for channel_name in channels or config.channels:
+        channel = analysis.channel(channel_name)
+        targets_ = targets(channel, control, variables)
+        for booking in bookings(channel, systematics, regions):
+            if processes and booking.process.name not in processes:
+                continue
+            jobs.append((config, analysis, channel_name, booking, targets_))
+            scopes |= {(channel_name, booking.process.name, region, t.category, t.variable.name) for region in booking.regions for t in targets_}
+    hset = HistogramSet()
+    if output.exists():
+        previous = HistogramSet.load(output)
+        hset.update((key, h) for key, h in previous.items() if (key.channel, key.process, key.region, key.category, key.variable) not in scopes)
+        logger.info(f"{output}: filling {len(scopes)} histogram scopes, keeping {len(hset)} other histograms")
+    logger.info(f"filling {len(jobs)} process bookings with {config.workers} workers")
+    for job, result, error in run_jobs(_fill_job, jobs, config.workers):
+        if error is not None:
+            raise RuntimeError(f"{job[2]}/{job[3].process.name}: {error}") from error
+        hset.update(result)
+    hset.save(output)
+    return hset
diff --git a/src/shapesmith/histogram.py b/src/shapesmith/histogram.py
index 991b1bc..d051ddf 100644
--- a/src/shapesmith/histogram.py
+++ b/src/shapesmith/histogram.py
@@ -1,4 +1,5 @@
-"""A minimal weighted 1D histogram on numpy arrays with ROOT TH1D conversion through uproot.
+"""A minimal weighted 1D histogram on numpy arrays with ROOT TH1D conversion through uproot, and the keyed set
+of histograms the stages exchange (one ROOT file plus a JSON index).
 
 boost-histogram/hist are deliberately not used: in LCG_108 (boost_histogram 1.3.2, numpy 2.1) the axis
 `edges` property returns constant values, and uproot writes exactly those corrupted edges. numpy plus
@@ -6,11 +7,17 @@
 """
 from __future__ import annotations
 
-from dataclasses import dataclass
+import json
+from dataclasses import asdict, dataclass
+from pathlib import Path
 
 import numpy as np
+import uproot
 from uproot.writing.identify import to_TAxis, to_TH1x
 
+NOMINAL_VARIATION = "Nominal"
+INCLUSIVE = "inclusive"  # the category of control-variable histograms
+
 
 @dataclass
 class Histogram:
@@ -76,3 +83,72 @@ def to_root(self, name: str):
     @classmethod
     def from_root(cls, th) -> "Histogram":
         return cls(th.axis().edges(), th.values(), th.variances())
+
+
+@dataclass(frozen=True, order=True)
+class HistKey:
+    channel: str
+    category: str
+    process: str
+    region: str
+    variation: str
+    variable: str
+
+    @property
+    def directory(self) -> str:
+        return f"{self.channel}_{self.category}"
+
+    @property
+    def object_name(self) -> str:
+        return f"{self.process}#{self.region}#{self.variation}#{self.variable}"
+
+    @property
+    def path(self) -> str:
+        return f"{self.directory}/{self.object_name}"
+
+    @classmethod
+    def parse(cls, path: str) -> "HistKey":
+        directory, name = path.split("/", 1)
+        channel, category = directory.split("_", 1)
+        process, region, variation, variable = name.split("#")
+        return cls(channel, category, process, region, variation, variable)
+
+
+class HistogramSet(dict):
+    """HistKey -> Histogram, saved as one ROOT file (`<channel>_<category>/<process>#<region>#<variation>#<variable>`)
+    plus a JSON index next to it."""
+
+    def select(self, **fields) -> list[HistKey]:
+        """The keys whose fields equal the given values, sorted."""
+        return sorted(key for key in self if all(getattr(key, name) == value for name, value in fields.items()))
+
+    def save(self, path: Path) -> None:
+        path = Path(path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        items = sorted(self.items())
+        with uproot.recreate(path) as f:
+            for key, h in items:
+                f[key.path] = h.to_root(key.object_name)
+        index = [{**asdict(key), "sum": h.sum(), "bins": len(h.values)} for key, h in items]
+        path.with_suffix(".json").write_text(json.dumps(index, indent=1))
+
+    @classmethod
+    def load(cls, path: Path) -> "HistogramSet":
+        hset = cls()
+        with uproot.open(path) as f:
+            for name, classname in f.classnames(recursive=True, cycle=False).items():
+                if classname.startswith("TH1"):
+                    hset[HistKey.parse(name)] = Histogram.from_root(f[name])
+        return hset
+
+
+def unchanged_variations(hset: HistogramSet) -> list[HistKey]:
+    """The variation histograms that are bitwise equal to their nominal (a declared variation that has no effect)."""
+    result = []
+    for key in hset.select():
+        if key.variation == NOMINAL_VARIATION:
+            continue
+        nominal = hset.get(HistKey(key.channel, key.category, key.process, key.region, NOMINAL_VARIATION, key.variable))
+        if nominal is not None and np.array_equal(hset[key].values, nominal.values) and np.array_equal(hset[key].variances, nominal.variances):
+            result.append(key)
+    return result
diff --git a/src/shapesmith/histograms.py b/src/shapesmith/histograms.py
deleted file mode 100644
index 29f5d02..0000000
--- a/src/shapesmith/histograms.py
+++ /dev/null
@@ -1,273 +0,0 @@
-"""Stage 2: skims -> histograms per process, region, category, variable and variation (Spec §8).
-
-Weight rules:
-  * MC:        norm_weight * lumi_pb * baseline weights * process weights * region weights
-  * embedding: norm_weight (=1) * process weights * region weights
-  * data:      region weights only (e.g. the fake factor in the anti-isolated region)
-A weight variation naming a weight the process does not carry is skipped for that process.
-"""
-from __future__ import annotations
-
-import json
-import logging
-from dataclasses import asdict, dataclass
-from pathlib import Path
-from typing import Iterator
-
-import numpy as np
-import uproot
-
-from shapesmith.config import RunConfig
-from shapesmith.expressions import apply_region, columns_in, evaluate, mask, weight
-from shapesmith.histogram import Histogram
-from shapesmith.io.skims import read_skims
-from shapesmith.parallel import run_jobs
-from shapesmith.model import Analysis, Process, Variable, WeightVariation
-from shapesmith.skim import SKIM_COLUMNS
-
-logger = logging.getLogger(__name__)
-
-NOMINAL_REGION = "nominal"
-NOMINAL_VARIATION = "Nominal"
-INCLUSIVE = "inclusive"
-
-
-@dataclass(frozen=True, order=True)
-class HistKey:
-    channel: str
-    category: str
-    process: str
-    region: str
-    variation: str
-    variable: str
-
-    @property
-    def directory(self) -> str:
-        return f"{self.channel}_{self.category}"
-
-    @property
-    def object_name(self) -> str:
-        return f"{self.process}#{self.region}#{self.variation}#{self.variable}"
-
-    @property
-    def path(self) -> str:
-        return f"{self.directory}/{self.object_name}"
-
-    @classmethod
-    def parse(cls, path: str) -> "HistKey":
-        directory, name = path.split("/", 1)
-        channel, category = directory.split("_", 1)
-        process, region, variation, variable = name.split("#")
-        return cls(channel, category, process, region, variation, variable)
-
-
-class HistogramSet:
-    """HistKey -> Histogram with ROOT persistence (uproot) and a JSON index."""
-
-    def __init__(self):
-        self._hists: dict[HistKey, Histogram] = {}
-
-    def add(self, key: HistKey, h: Histogram) -> None:
-        self._hists[key] = h
-
-    def get(self, key: HistKey) -> Histogram:
-        return self._hists[key]
-
-    def has(self, key: HistKey) -> bool:
-        return key in self._hists
-
-    def keys(self, **filters) -> list[HistKey]:
-        return sorted(k for k in self._hists if all(getattr(k, name) == value for name, value in filters.items()))
-
-    def items(self) -> Iterator[tuple[HistKey, Histogram]]:
-        return iter(sorted(self._hists.items(), key=lambda kv: kv[0]))
-
-    def update(self, other: "HistogramSet") -> None:
-        self._hists.update(other._hists)
-
-    def __len__(self) -> int:
-        return len(self._hists)
-
-    def save(self, path: Path) -> None:
-        path = Path(path)
-        path.parent.mkdir(parents=True, exist_ok=True)
-        with uproot.recreate(path) as f:
-            for key, h in self.items():
-                f[key.path] = h.to_root(key.object_name)
-        index = [{**asdict(key), "sum": h.sum(), "bins": len(h.values)} for key, h in self.items()]
-        path.with_suffix(".json").write_text(json.dumps(index, indent=1))
-
-    @classmethod
-    def load(cls, path: Path) -> "HistogramSet":
-        hset = cls()
-        with uproot.open(path) as f:
-            for name, classname in f.classnames(recursive=True, cycle=False).items():
-                if classname.startswith("TH1"):
-                    hset.add(HistKey.parse(name), Histogram.from_root(f[name]))
-        return hset
-
-
-@dataclass(frozen=True)
-class Booking:
-    process: Process
-    regions: tuple[str, ...]
-    variations: tuple[WeightVariation, ...]
-
-
-@dataclass(frozen=True)
-class Target:
-    category: str
-    cut: str | None
-    variable: Variable
-
-
-def resolve_regions(analysis: Analysis, channel_name: str, regions: list[str] | None) -> tuple[str, ...] | None:
-    """Resolve an explicit region request for one channel.
-
-    ``None`` retains the historical process-dependent booking.  ``all`` is
-    expanded per channel because channels need not define the same regions.
-    """
-    if regions is None:
-        return None
-    channel = analysis.channel(channel_name)
-    names = (NOMINAL_REGION, *(region.name for region in channel.regions)) if "all" in regions else tuple(regions)
-    resolved = tuple(dict.fromkeys(names))
-    for name in resolved:
-        channel.region(name)  # validates with a channel-specific error
-    return resolved
-
-
-def bookings(analysis: Analysis, channel_name: str, systematics: bool, regions: list[str] | None = None) -> list[Booking]:
-    requested_regions = resolve_regions(analysis, channel_name, regions)
-    estimator_regions = tuple(analysis.estimator.regions.values()) if analysis.estimator else ()
-    result = []
-    for process in analysis.processes:
-        if not analysis.samples_for(process.group, channel_name):
-            continue
-        process_regions = requested_regions
-        if process_regions is None:
-            process_regions = (NOMINAL_REGION,) if process.kind == "signal" else tuple(dict.fromkeys((NOMINAL_REGION, *estimator_regions)))
-        variations = analysis.weight_variations if systematics and process.kind not in ("data", "embedding") else ()
-        result.append(Booking(process, process_regions, tuple(variations)))
-    return result
-
-
-def targets(analysis: Analysis, control: bool, variables: list[str] | None) -> list[Target]:
-    if control:
-        names = variables or list(analysis.control_variables)
-        return [Target(INCLUSIVE, None, analysis.control_variables[name]) for name in names]  # KeyError for unknown variables
-    return [Target(c.name, c.cut, c.variable) for c in analysis.categories]
-
-
-def _needed_columns(analysis: Analysis, channel_name: str, booking: Booking, targets_: list[Target], kinds: set[str]) -> set[str]:
-    """Columns to read for this process: cuts always, weights only for the sample kinds that use them."""
-    channel = analysis.channel(channel_name)
-    process_selection = booking.process.selection_for(channel_name)
-    columns = set(SKIM_COLUMNS)
-    for expr in list(channel.baseline.cuts.values()) + list(process_selection.cuts.values()):
-        columns |= columns_in(expr)
-    if kinds == {"mc"}:
-        for expr in channel.baseline.weights.values():
-            columns |= columns_in(expr)
-        for variation in booking.variations:
-            for expr in variation.replace_weights.values():
-                columns |= columns_in(expr)
-    if kinds <= {"mc", "embedding"}:
-        for expr in process_selection.weights.values():
-            columns |= columns_in(expr)
-    for region_name in booking.regions:
-        region = channel.region(region_name)
-        region_weights = [expr for name, expr in region.add_weights.items() if kinds == {"mc"} or name not in channel.baseline.weights]
-        for expr in list(region.replace_cuts.values()) + region_weights:
-            columns |= columns_in(expr)
-    for target in targets_:
-        columns |= columns_in(target.variable.expr) | (columns_in(target.cut) if target.cut else set())
-    return columns
-
-
-def _varied(weights: dict[str, str], variation: WeightVariation | None) -> dict[str, str] | None:
-    """Weights with the variation applied, or None if the variation names a weight not present."""
-    if variation is None:
-        return weights
-    if any(name not in weights for name in variation.replace_weights):
-        return None
-    return {**weights, **variation.replace_weights}
-
-
-def fill_booking(config: RunConfig, analysis: Analysis, channel_name: str, booking: Booking, targets_: list[Target]) -> dict[HistKey, Histogram]:
-    channel = analysis.channel(channel_name)
-    process = booking.process
-    samples = analysis.samples_for(process.group, channel_name)
-    kinds = {s.kind for s in samples}
-    frame = read_skims(config.skim_dir, channel_name, [s.nick for s in samples], _needed_columns(analysis, channel_name, booking, targets_, kinds))
-    lumi = analysis.lumi_pb if kinds == {"mc"} else 1.0
-    process_selection = process.selection_for(channel_name)
-    process_mask = mask(frame, process_selection.cuts)
-    result: dict[HistKey, Histogram] = {}
-    for region_name in booking.regions:
-        region = channel.region(region_name)
-        cuts = apply_region(channel.baseline, region).cuts
-        selected = frame[process_mask & mask(frame, cuts)]
-        weights: dict[str, str] = {}
-        if kinds == {"mc"}:
-            weights.update(channel.baseline.weights)
-        if kinds <= {"mc", "embedding"}:
-            weights.update(process_selection.weights)
-        weights.update({name: expr for name, expr in region.add_weights.items() if kinds == {"mc"} or name not in channel.baseline.weights})
-        for variation in (None, *booking.variations):
-            varied = _varied(weights, variation)
-            if varied is None:
-                logger.debug(f"{process.name}: variation {variation.name} not applicable, skipped")
-                continue
-            w = selected["norm_weight"].to_numpy() * lumi * weight(selected, varied)
-            for target in targets_:
-                in_category = mask(selected, {"category": target.cut}) if target.cut else np.ones(len(selected), dtype=bool)
-                values = evaluate(selected, target.variable.expr).astype(np.float64)
-                finite = np.isfinite(values) & np.isfinite(w)
-                if (in_category & ~finite).any():
-                    logger.warning(f"{process.name}/{region_name}/{target.variable.name}: {(in_category & ~finite).sum()} events with NaN/inf skipped")
-                keep = in_category & finite
-                key = HistKey(channel_name, target.category, process.name, region_name, variation.name if variation else NOMINAL_VARIATION, target.variable.name)
-                result[key] = Histogram.fill(target.variable.edges, values[keep], w[keep])
-    return result
-
-
-def _fill_job(args) -> dict[HistKey, Histogram]:
-    return fill_booking(*args)
-
-
-def run_hist(config: RunConfig, analysis: Analysis, channels: list[str] | None, control: bool, variables: list[str] | None, systematics: bool, processes: list[str] | None, output: Path, force: bool = False, regions: list[str] | None = None) -> HistogramSet:
-    """Fill every booked histogram of the requested channels and save them to `output` (+ .json index)."""
-    output = Path(output)
-    selected_channels = list(channels or config.channels)
-    targets_ = targets(analysis, control, variables)
-    jobs = []
-    refill_scope: set[tuple[str, str, str, str, str]] = set()
-    for channel_name in selected_channels:
-        for booking in bookings(analysis, channel_name, systematics, regions):
-            if processes and booking.process.key not in processes:
-                continue
-            jobs.append((config, analysis, channel_name, booking, targets_))
-            refill_scope.update(
-                (channel_name, booking.process.name, region, target.category, target.variable.name)
-                for region in booking.regions
-                for target in targets_
-            )
-    hset = HistogramSet()
-    if output.exists():
-        if not force:
-            logger.info(f"{output} exists, loading (use --force to refill)")
-            return HistogramSet.load(output)
-        for key, h in HistogramSet.load(output).items():  # keep what this call does not refill
-            scope = (key.channel, key.process, key.region, key.category, key.variable)
-            if scope not in refill_scope:
-                hset.add(key, h)
-        logger.info(f"{output}: refilling {len(refill_scope)} histogram scopes, keeping {len(hset)} unrelated histograms")
-    logger.info(f"filling {len(jobs)} process bookings with {config.workers} workers")
-    for job, result, error in run_jobs(_fill_job, jobs, config.workers):
-        if error is not None:
-            raise RuntimeError(f"{job[2]}/{job[3].process.name}: {error}") from error
-        for key, h in result.items():
-            hset.add(key, h)
-    hset.save(output)
-    return hset
diff --git a/src/shapesmith/io/__init__.py b/src/shapesmith/io/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/shapesmith/io/discovery.py b/src/shapesmith/io/discovery.py
deleted file mode 100644
index 8a956a5..0000000
--- a/src/shapesmith/io/discovery.py
+++ /dev/null
@@ -1,61 +0,0 @@
-"""Locate CROWN ntuples and their friend trees (Spec §7 step 1)."""
-from __future__ import annotations
-
-import os
-from dataclasses import dataclass
-
-from shapesmith.config import NtupleConfig
-
-
-@dataclass(frozen=True)
-class NtupleFile:
-    path: str               # full path or xrootd URL of the main ntuple
-    friends: tuple[str, ...]  # full paths/URLs of the friend files, same order as the config
-    nick: str
-    channel: str
-    basename: str
-
-
-def join_url(server: str, path: str) -> str:
-    """'root://server' + '/store/...' -> 'root://server//store/...'; local paths pass through."""
-    if not server:
-        return path
-    return f"{server.rstrip('/')}/{path}"
-
-
-def list_root_files(server: str, directory: str) -> list[str]:
-    """Sorted basenames of the .root files in `directory` (xrootd when `server` is set)."""
-    if server:
-        from XRootD import client  # optional dependency, only needed for remote access
-        from XRootD.client.flags import DirListFlags
-
-        status, listing = client.FileSystem(server).dirlist(directory, DirListFlags.STAT)
-        if not status.ok or listing is None:
-            raise FileNotFoundError(f"cannot list {join_url(server, directory)}: {status.message}")
-        names = [entry.name for entry in listing]
-    else:
-        if not os.path.isdir(directory):
-            raise FileNotFoundError(f"cannot list {directory}")
-        names = os.listdir(directory)
-    return sorted(name for name in names if name.endswith(".root"))
-
-
-def discover(ntuples: NtupleConfig, era: str, nick: str, channel: str, kind: str) -> list[NtupleFile]:
-    """Main files of one sample and channel with their friend files; missing friends are an error."""
-    main_dir = f"{ntuples.base.rstrip('/')}/{era}/{nick}/{channel}"
-    basenames = list_root_files(ntuples.server, main_dir)
-    friend_dirs = [f"{f.base.rstrip('/')}/{era}/{nick}/{channel}" for f in ntuples.friends if kind in f.applies_to]
-    friend_listings = [set(list_root_files(ntuples.server, directory)) for directory in friend_dirs]
-    missing = [f"{directory}/{name}" for directory, names in zip(friend_dirs, friend_listings) for name in basenames if name not in names]
-    if missing:
-        raise FileNotFoundError("missing friend files:\n" + "\n".join(missing))
-    return [
-        NtupleFile(
-            path=join_url(ntuples.server, f"{main_dir}/{name}"),
-            friends=tuple(join_url(ntuples.server, f"{directory}/{name}") for directory in friend_dirs),
-            nick=nick,
-            channel=channel,
-            basename=name,
-        )
-        for name in basenames
-    ]
diff --git a/src/shapesmith/io/ntuples.py b/src/shapesmith/io/ntuples.py
deleted file mode 100644
index aaff31a..0000000
--- a/src/shapesmith/io/ntuples.py
+++ /dev/null
@@ -1,80 +0,0 @@
-"""Read columns from a CROWN ntuple and its friend trees with uproot (Spec §7 steps 2-3)."""
-from __future__ import annotations
-
-import json
-
-import pandas as pd
-import uproot
-
-from shapesmith.io.discovery import NtupleFile
-
-TREE = "ntuple"
-
-
-class MissingColumnsError(ValueError):
-    def __init__(self, missing: list[str], path: str):
-        self.missing = sorted(missing)
-        self.path = path
-        super().__init__(f"{path}: missing columns: {', '.join(self.missing)}")
-
-    def __reduce__(self):  # picklable with its two arguments, so that worker processes can hand it back
-        return (MissingColumnsError, (self.missing, self.path))
-
-
-def available_columns(ntuple: NtupleFile) -> dict[str, str]:
-    """column name -> file that provides it (the main file wins over friends)."""
-    result: dict[str, str] = {}
-    for path in (*ntuple.friends, ntuple.path):  # main file last so that it overrides
-        with uproot.open(path) as handle:
-            for name in handle[TREE].keys():
-                result[name] = path
-    return result
-
-
-def read_ntuple(ntuple: NtupleFile, columns: set[str], optional: set[str] = frozenset()) -> tuple[pd.DataFrame, dict]:
-    """The requested columns of one ntuple (+ friends) and the CROWN metadata of the main file, opening every file once.
-
-    Remote opens cost seconds, so columns are listed and read from the same handle. Columns in `optional` may be
-    absent (they are left out); any other missing column raises MissingColumnsError.
-    """
-    paths = (*ntuple.friends, ntuple.path)  # main file last so that it overrides friends
-    handles = [uproot.open(path) for path in paths]
-    try:
-        sources: dict[str, int] = {}
-        for index, handle in enumerate(handles):
-            for name in handle[TREE].keys():
-                sources[name] = index
-        missing = set(columns) - set(sources)
-        if missing - set(optional):
-            raise MissingColumnsError(sorted(missing - set(optional)), ntuple.path)
-        by_handle: dict[int, list[str]] = {}
-        for column in sorted(set(columns) & set(sources)):
-            by_handle.setdefault(sources[column], []).append(column)
-        frames = [pd.DataFrame(handles[index][TREE].arrays(names, library="np")) for index, names in by_handle.items()]
-        main = handles[-1]
-        metadata = json.loads(str(main["metadata"])) if "metadata" in main else {}
-    finally:
-        for handle in handles:
-            handle.close()
-    lengths = {len(frame) for frame in frames}
-    if len(lengths) > 1:
-        raise ValueError(f"{ntuple.path}: friend trees have different numbers of entries {sorted(lengths)}")
-    return (pd.concat(frames, axis=1) if frames else pd.DataFrame()), metadata
-
-
-def read_columns(ntuple: NtupleFile, columns: set[str]) -> pd.DataFrame:
-    """The requested columns of one ntuple (+ friends) as a DataFrame; dtypes as stored."""
-    return read_ntuple(ntuple, columns)[0]
-
-
-def read_metadata(path: str) -> dict:
-    """The CROWN `metadata` JSON block (empty dict when absent)."""
-    with uproot.open(path) as handle:
-        if "metadata" not in handle:
-            return {}
-        return json.loads(str(handle["metadata"]))
-
-
-def num_entries(path: str) -> int:
-    with uproot.open(path) as handle:
-        return handle[TREE].num_entries
diff --git a/src/shapesmith/fit.py b/src/shapesmith/limits.py
similarity index 72%
rename from src/shapesmith/fit.py
rename to src/shapesmith/limits.py
index 5dd4b4a..3557029 100644
--- a/src/shapesmith/fit.py
+++ b/src/shapesmith/limits.py
@@ -1,9 +1,8 @@
-"""Run combine inside the CMSSW environment and collect expected limits, significance and best-fit r (Spec §10.3)."""
+"""The limit chain: combine on the datacards (expected limits, significance, best-fit r) and its summary."""
 from __future__ import annotations
 
 import json
 import logging
-import subprocess
 from pathlib import Path
 
 import matplotlib
@@ -12,6 +11,7 @@
 import matplotlib.pyplot as plt  # noqa: E402
 import uproot  # noqa: E402
 
+from shapesmith import cmssw  # noqa: E402
 from shapesmith.config import RunConfig  # noqa: E402
 
 logger = logging.getLogger(__name__)
@@ -20,29 +20,14 @@
 COMMON = "-m {mass} --setParameterRanges r=-40,40 -t -1"
 
 
-def combine_script(cmssw_dir: str, scram_arch: str, card_dir: Path, mass: str = "125") -> str:
-    return "\n".join(
-        [
-            "set -e",
-            f"export SCRAM_ARCH={scram_arch}",
-            "source /cvmfs/cms.cern.ch/cmsset_default.sh",
-            f"cd {cmssw_dir}/src",
-            "eval $(scramv1 runtime -sh)",
-            f"cd {Path(card_dir).resolve()}",
-            f"text2workspace.py combined.txt -o workspace.root -m {mass}",
-            f"combine -M AsymptoticLimits -d workspace.root {COMMON.format(mass=mass)} --expectSignal 0 -n .Limit",
-            f"combine -M Significance -d workspace.root {COMMON.format(mass=mass)} --expectSignal 1 -n .Significance",
-            f"combine -M MultiDimFit --algo singles -d workspace.root {COMMON.format(mass=mass)} --expectSignal 1 -n .Fit",
-        ]
-    )
-
-
-def run_combine(config: RunConfig, card_dir: Path) -> None:
-    if config.combine is None:
-        raise ValueError("RunConfig.combine (cmssw_dir) is required to run combine")
-    script = combine_script(config.combine.cmssw_dir, config.combine.scram_arch, card_dir)
-    logger.info(f"running combine in {card_dir}")
-    subprocess.run(["bash", "-c", script], check=True)
+def limit_commands(mass: str = "125") -> list[str]:
+    common = COMMON.format(mass=mass)
+    return [
+        f"text2workspace.py combined.txt -o workspace.root -m {mass}",
+        f"combine -M AsymptoticLimits -d workspace.root {common} --expectSignal 0 -n .Limit",
+        f"combine -M Significance -d workspace.root {common} --expectSignal 1 -n .Significance",
+        f"combine -M MultiDimFit --algo singles -d workspace.root {common} --expectSignal 1 -n .Fit",
+    ]
 
 
 def _read(path: Path) -> list[tuple[float, float, float | None]]:
@@ -107,13 +92,13 @@ def write_summary(results: dict[str, dict], output_dir: Path) -> tuple[Path, Pat
     return json_path, md_path, pdf_path
 
 
-def run_fit(config: RunConfig, datacard_dir: Path, final_states: list[str], skip_combine: bool = False) -> dict:
+def run_limits(config: RunConfig, datacard_dir: Path, final_states: list[str], skip_combine: bool = False) -> dict:
     datacard_dir = Path(datacard_dir)
     results = {}
     for name in final_states:
         card_dir = datacard_dir / name
         if not skip_combine:
-            run_combine(config, card_dir)
+            cmssw.run(limit_commands(), config.combine, card_dir)
         results[name] = collect(card_dir)
     write_summary(results, datacard_dir)
     return results
diff --git a/src/shapesmith/ml_export.py b/src/shapesmith/ml_export.py
index 2a2953f..bbf43ea 100644
--- a/src/shapesmith/ml_export.py
+++ b/src/shapesmith/ml_export.py
@@ -1,4 +1,4 @@
-"""Training folds from the skims, in the Feather layout of the previous smhtt_ul export (Spec §12).
+"""Training folds from the skims, in the Feather layout of the previous smhtt_ul export.
 
 Columns are 5-level tuples: ("Event","id"), ("Event","event"), ("Labels",<label>),
 ("Nominal","variables",<var>), ("Nominal","weight"), ("Nominal","cut"), ("Nominal","class_weight").
@@ -16,10 +16,9 @@
 import pyarrow.feather as feather
 
 from shapesmith.config import RunConfig
-from shapesmith.expressions import apply_region, columns_in, evaluate, mask, weight
-from shapesmith.io.skims import read_skims
-from shapesmith.model import Analysis
-from shapesmith.skim import SKIM_COLUMNS
+from shapesmith.events import Query, load
+from shapesmith.expressions import columns_of, evaluate
+from shapesmith.model import NOMINAL, Analysis, Channel
 
 logger = logging.getLogger(__name__)
 PATTERN = np.array([True, True, False, False])
@@ -50,41 +49,23 @@ def class_weights(weights: np.ndarray, labels: np.ndarray) -> np.ndarray:
     return result * weights
 
 
-def _variable_expr(analysis: Analysis, name: str) -> str:
-    if name in analysis.control_variables:
-        return analysis.control_variables[name].expr
-    for category in analysis.categories:
+def _variable_expr(channel: Channel, name: str) -> str:
+    if name in channel.variables:
+        return channel.variables[name].expr
+    for category in channel.categories:
         if category.variable.name == name:
             return category.variable.expr
     return name
 
 
 def export_process(config: RunConfig, analysis: Analysis, channel_name: str, name: str) -> pd.DataFrame:
+    """The events of one exported process; an estimator output is exported as the data of its region."""
     ml = analysis.ml
     channel = analysis.channel(channel_name)
-    region = channel.region(ml.region_of.get(name, "nominal"))
-    if analysis.estimator is not None and name == analysis.estimator.output:
-        samples = analysis.samples_for("data", channel_name)
-        cuts = apply_region(channel.baseline, region).cuts
-        weights = dict(region.add_weights)
-        lumi = 1.0
-    else:
-        process = analysis.process(name)
-        process_selection = process.selection_for(channel_name)
-        samples = analysis.samples_for(process.group, channel_name)
-        kinds = {s.kind for s in samples}
-        cuts = {**apply_region(channel.baseline, region).cuts, **process_selection.cuts}
-        weights = {}
-        if kinds == {"mc"}:
-            weights.update(channel.baseline.weights)
-        if kinds <= {"mc", "embedding"}:
-            weights.update(process_selection.weights)
-        weights.update(region.add_weights)
-        lumi = analysis.lumi_pb if kinds == {"mc"} else 1.0
-    exprs = {v: _variable_expr(analysis, v) for v in ml.variables}
-    columns = set(SKIM_COLUMNS) | {ml.fold_column} | set().union(*(columns_in(e) for e in list(cuts.values()) + list(weights.values()) + list(exprs.values())))
-    frame = read_skims(config.skim_dir, channel_name, [s.nick for s in samples], columns)
-    selected = frame[mask(frame, cuts)].reset_index(drop=True)
+    process = name if name in {p.name for p in channel.processes} else channel.data()  # an estimator output: data in its region
+    exprs = {v: _variable_expr(channel, v) for v in ml.variables}
+    events = load(config, analysis, Query(channel_name, process, ml.region_of.get(name, NOMINAL)), {ml.fold_column} | columns_of(exprs.values()))
+    selected = events.frame
     out = pd.DataFrame(index=selected.index)
     out[tuple_column("Event", "id")] = np.arange(len(selected))
     out[tuple_column("Event", ml.fold_column)] = selected[ml.fold_column].to_numpy()
@@ -92,7 +73,7 @@ def export_process(config: RunConfig, analysis: Analysis, channel_name: str, nam
         out[tuple_column("Labels", label)] = np.int32(label == ml.label_of[name])
     for variable, expr in exprs.items():
         out[tuple_column("Nominal", "variables", variable)] = evaluate(selected, expr).astype(np.float32)
-    out[tuple_column("Nominal", "weight")] = (selected["norm_weight"].to_numpy() * lumi * weight(selected, weights)).astype(np.float32)
+    out[tuple_column("Nominal", "weight")] = events.weights.astype(np.float32)
     out[tuple_column("Nominal", "cut")] = np.float32(1.0)
     out.columns = pd.MultiIndex.from_tuples(out.columns)
     return out
diff --git a/src/shapesmith/model.py b/src/shapesmith/model.py
index fcba65f..043dfb3 100644
--- a/src/shapesmith/model.py
+++ b/src/shapesmith/model.py
@@ -1,21 +1,27 @@
-"""The analysis data model (Spec §5).
+"""The analysis data model.
 
-An analysis repository builds one `Analysis` object; every ShapeSmith step reads it. Expressions
-are strings in pandas.eval syntax (see expressions.py). Cut and weight *names* are the handles that
-regions and variations replace, so they must be unique within a selection.
+An analysis repository builds one `Analysis`; every ShapeSmith stage reads it. Everything that differs by channel
+(samples, processes, regions, categories, variables, variations, estimators) lives in its `Channel`. Expressions are
+strings in pandas.eval syntax (see expressions.py). Cut and weight *names* are the handles that regions and
+variations replace, so they are unique within a selection.
 """
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import Literal, Mapping
+from typing import TYPE_CHECKING, Literal, Mapping
+
+if TYPE_CHECKING:
+    from shapesmith.measurements import Measurement
 
-Kind = Literal["data", "signal", "true_tau", "lepton_fake", "jet_fake", "single_higgs", "other", "embedding"]
 SampleKind = Literal["data", "mc", "embedding"]
-KINDS = ("data", "signal", "true_tau", "lepton_fake", "jet_fake", "single_higgs", "other", "embedding")
+Role = Literal["data", "signal", "background", "auxiliary"]  # auxiliary: booked for an estimator, never a datacard process
+SAMPLE_KINDS = ("data", "mc", "embedding")
+ROLES = ("data", "signal", "background", "auxiliary")
+NOMINAL = "nominal"
 
 
 class AnalysisError(ValueError):
-    """Raised by Analysis.validate() with every problem found, one per line."""
+    """An inconsistent analysis; validate() lists every problem found, one per line."""
 
 
 def _frozen(mapping: Mapping | None) -> dict:
@@ -31,7 +37,6 @@ class Sample:
     xsec: float = 1.0
     nevents: int = 1
     generator_weight: float = 1.0
-    channels: tuple[str, ...] | None = None  # None = all channels
     cut: str | None = None  # per-sample event selection applied at skim time, e.g. to keep one generator-level part of a sample
 
     @property
@@ -54,32 +59,31 @@ def __post_init__(self):
 
 @dataclass(frozen=True)
 class Process:
-    key: str
-    group: str
+    """A process owns its whole weight set: the analysis puts the common weights first, then the specific ones."""
+
     name: str
-    kind: Kind
+    group: str  # the sample group it is made of
+    role: Role
     plot_group: str
-    selection: Selection | Mapping[str, Selection] = field(default_factory=Selection)  # one Selection or {channel: Selection}
-
-    def selection_for(self, channel: str) -> Selection:
-        """The process selection in `channel` (a single Selection applies to every channel)."""
-        if isinstance(self.selection, Selection):
-            return self.selection
-        return self.selection[channel]
+    selection: Selection = field(default_factory=Selection)
 
 
 @dataclass(frozen=True)
 class Region:
+    """A selection derived from the nominal one: cuts replaced by name, weights replaced where a process carries
+    them, weights added to every process."""
+
     name: str
     replace_cuts: Mapping[str, str] = field(default_factory=dict)
     add_weights: Mapping[str, str] = field(default_factory=dict)
+    replace_weights: Mapping[str, str] = field(default_factory=dict)
 
     def __post_init__(self):
-        object.__setattr__(self, "replace_cuts", _frozen(self.replace_cuts))
-        object.__setattr__(self, "add_weights", _frozen(self.add_weights))
+        for name in ("replace_cuts", "add_weights", "replace_weights"):
+            object.__setattr__(self, name, _frozen(getattr(self, name)))
 
 
-NOMINAL = Region("nominal")
+NOMINAL_REGION = Region(NOMINAL)
 
 
 @dataclass(frozen=True)
@@ -98,8 +102,12 @@ class Category:
 
 @dataclass(frozen=True)
 class WeightVariation:
+    """Weights replaced by name; a process lacking one of them does not get the variation."""
+
     name: str
     replace_weights: Mapping[str, str]
+    applies_to: tuple[SampleKind, ...] = ("mc",)
+    regions: tuple[str, ...] | None = None  # None: every booked region
 
     def __post_init__(self):
         object.__setattr__(self, "replace_weights", _frozen(self.replace_weights))
@@ -107,8 +115,55 @@ def __post_init__(self):
 
 @dataclass(frozen=True)
 class ColumnVariation:
+    """Columns read shifted: `c + suffix` wherever that branch exists (a CROWN shift), or `derived[c]`, an expression
+    of nominal columns (a shift computed in ShapeSmith). A name ending in Up/Down is one side of a shape nuisance;
+    any other name is a template that datacards never turn into a shape line."""
+
+    name: str
+    suffix: str = ""
+    derived: Mapping[str, str] = field(default_factory=dict)
+    applies_to: tuple[SampleKind, ...] = ("mc",)
+    regions: tuple[str, ...] | None = None  # None: every booked region where the rewrite changes an expression
+
+    def __post_init__(self):
+        object.__setattr__(self, "derived", _frozen(self.derived))
+
+
+Variation = WeightVariation | ColumnVariation
+
+
+@dataclass(frozen=True)
+class DataMinus:
+    """output = scale * (data - sum of `subtract`) in `region`, per column variation of its inputs."""
+
+    output: str
+    region: str
+    subtract: tuple[str, ...]
+    scale: float = 1.0
+
+
+@dataclass(frozen=True)
+class ABCD:
+    """output = (data - MC)(b) * (data - MC)(c).sum() / (data - MC)(d).sum(), nominal only."""
+
+    output: str
+    b: str
+    c: str
+    d: str
+    subtract: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class TemplateShift:
+    """Variation `name`Up/Down of `process`: its nominal plus/minus `fraction` times the `template` process."""
+
     name: str
-    suffix: str
+    process: str
+    template: str
+    fraction: float
+
+
+Estimator = DataMinus | ABCD | TemplateShift
 
 
 @dataclass(frozen=True)
@@ -122,29 +177,51 @@ class LnN:
 @dataclass(frozen=True)
 class Channel:
     name: str
-    skim: Selection
-    baseline: Selection
+    samples: tuple[Sample, ...]
+    skim: Mapping[str, str]  # cuts applied when skimming; re-applied (under each variation) when filling
+    cuts: Mapping[str, str]  # the nominal selection
+    processes: tuple[Process, ...]
     regions: tuple[Region, ...] = ()
+    categories: tuple[Category, ...] = ()
+    variables: Mapping[str, Variable] = field(default_factory=dict)  # control variables
+    variations: tuple[Variation, ...] = ()
+    estimators: tuple[Estimator, ...] = ()
     keep_columns: tuple[str, ...] = ()
 
+    def __post_init__(self):
+        for name in ("skim", "cuts", "variables"):
+            object.__setattr__(self, name, _frozen(getattr(self, name)))
+
     def region(self, name: str) -> Region:
-        if name == NOMINAL.name:
-            return NOMINAL
+        if name == NOMINAL:
+            return NOMINAL_REGION
         for region in self.regions:
             if region.name == name:
                 return region
         raise KeyError(f"channel {self.name}: unknown region {name!r}")
 
+    def process(self, name: str) -> Process:
+        for process in self.processes:
+            if process.name == name:
+                return process
+        raise KeyError(f"channel {self.name}: unknown process {name!r}")
 
-@dataclass(frozen=True)
-class Estimator:
-    name: Literal["fake_factors", "abcd"]
-    regions: Mapping[str, str]
-    subtract: tuple[str, ...]
-    output: str
+    def samples_of(self, group: str) -> tuple[Sample, ...]:
+        return tuple(s for s in self.samples if s.group == group)
 
-    def __post_init__(self):
-        object.__setattr__(self, "regions", _frozen(self.regions))
+    def kind_of(self, process: Process) -> str:
+        """The sample kind of a process (validate() makes sure its samples share one)."""
+        return self.samples_of(process.group)[0].kind
+
+    def data(self) -> str:
+        """Name of the data process."""
+        return next(p.name for p in self.processes if p.role == "data")
+
+    def backgrounds(self) -> tuple[str, ...]:
+        """The background processes followed by the estimator outputs: plots, sync, datacards and the ML export."""
+        names = [p.name for p in self.processes if p.role == "background"]
+        names += [e.output for e in self.estimators if isinstance(e, (DataMinus, ABCD))]
+        return tuple(names)
 
 
 @dataclass(frozen=True)
@@ -173,74 +250,12 @@ class Analysis:
     name: str
     era: str
     lumi_pb: float
+    signal: str  # the signal process of datacards and plots
     channels: Mapping[str, Channel]
-    samples: tuple[Sample, ...]
-    processes: tuple[Process, ...]
-    signal: str
-    categories: tuple[Category, ...] = ()
-    control_variables: Mapping[str, Variable] = field(default_factory=dict)
-    weight_variations: tuple[WeightVariation, ...] = ()
-    column_variations: tuple[ColumnVariation, ...] = ()
     lnn: tuple[LnN, ...] = ()
-    estimator: Estimator | None = None
     style: Style | None = None
     ml: MLExportConfig | None = None
+    measurement: Measurement | None = None  # run by `shapesmith measure`
 
     def channel(self, name: str) -> Channel:
         return self.channels[name]
-
-    def process(self, name: str) -> Process:
-        for process in self.processes:
-            if process.name == name:
-                return process
-        raise KeyError(f"unknown process {name!r}")
-
-    def processes_of_kind(self, *kinds: str) -> tuple[Process, ...]:
-        return tuple(p for p in self.processes if p.kind in kinds)
-
-    def backgrounds(self) -> tuple[str, ...]:
-        names = [p.name for p in self.processes if p.kind not in ("data", "signal")]
-        if self.estimator is not None:
-            names.append(self.estimator.output)
-        return tuple(names)
-
-    def samples_for(self, group: str, channel: str) -> tuple[Sample, ...]:
-        return tuple(s for s in self.samples if s.group == group and (s.channels is None or channel in s.channels))
-
-    def validate(self) -> None:
-        problems: list[str] = []
-        names = [p.name for p in self.processes]
-        problems += [f"duplicate process name {n}" for n in sorted({n for n in names if names.count(n) > 1})]
-        keys = [p.key for p in self.processes]
-        problems += [f"duplicate process key {k}" for k in sorted({k for k in keys if keys.count(k) > 1})]
-        groups = {s.group for s in self.samples}
-        problems += [f"process {p.name}: no samples for group {p.group}" for p in self.processes if p.group not in groups]
-        if self.signal not in names:
-            problems.append(f"signal {self.signal} is not a process")
-        for kind in {p.kind for p in self.processes}:
-            if kind not in KINDS:
-                problems.append(f"unknown process kind {kind}")
-        category_names = [c.name for c in self.categories]
-        problems += [f"duplicate category {n}" for n in sorted({n for n in category_names if category_names.count(n) > 1})]
-        for channel in self.channels.values():
-            cut_names, weight_names = set(channel.baseline.cuts), set(channel.baseline.weights)
-            for region in channel.regions:
-                for cut in region.replace_cuts:
-                    if cut not in cut_names:
-                        problems.append(f"channel {channel.name}, region {region.name}: replaces unknown cut {cut}")
-            if self.estimator is not None:
-                region_names = {r.name for r in channel.regions}
-                for region in self.estimator.regions.values():
-                    if region not in region_names:
-                        problems.append(f"channel {channel.name}: estimator region {region} does not exist")
-            all_weights = weight_names | {w for p in self.processes for w in p.selection_for(channel.name).weights}
-            for variation in self.weight_variations:
-                for weight in variation.replace_weights:
-                    if weight not in all_weights:
-                        problems.append(f"variation {variation.name}: replaces unknown weight {weight} (channel {channel.name})")
-        if self.estimator is not None:
-            for name in self.estimator.subtract:
-                if name not in names:
-                    problems.append(f"estimator subtracts unknown process {name}")
-        if problems:
-            raise AnalysisError("\n".join(sorted(set(problems))))
diff --git a/src/shapesmith/ntuples.py b/src/shapesmith/ntuples.py
new file mode 100644
index 0000000..7426e5d
--- /dev/null
+++ b/src/shapesmith/ntuples.py
@@ -0,0 +1,113 @@
+"""Locate CROWN ntuples and their friend trees, and read columns from them with uproot."""
+from __future__ import annotations
+
+import json
+import os
+from dataclasses import dataclass
+
+import pandas as pd
+import uproot
+
+from shapesmith.config import NtupleConfig
+
+TREE = "ntuple"
+
+
+@dataclass(frozen=True)
+class NtupleFile:
+    path: str               # full path or xrootd URL of the main ntuple
+    friends: tuple[str, ...]  # full paths/URLs of the friend files, same order as the config
+    nick: str
+    channel: str
+    basename: str
+
+
+class MissingColumnsError(ValueError):
+    def __init__(self, missing: list[str], path: str):
+        self.missing = sorted(missing)
+        self.path = path
+        super().__init__(f"{path}: missing columns: {', '.join(self.missing)}")
+
+    def __reduce__(self):  # picklable with its two arguments, so that worker processes can hand it back
+        return (MissingColumnsError, (self.missing, self.path))
+
+
+def join_url(server: str, path: str) -> str:
+    """'root://server' + '/store/...' -> 'root://server//store/...'; local paths pass through."""
+    if not server:
+        return path
+    return f"{server.rstrip('/')}/{path}"
+
+
+def list_root_files(server: str, directory: str) -> list[str]:
+    """Sorted basenames of the .root files in `directory` (xrootd when `server` is set)."""
+    if server:
+        from XRootD import client  # optional dependency, only needed for remote access
+        from XRootD.client.flags import DirListFlags
+
+        status, listing = client.FileSystem(server).dirlist(directory, DirListFlags.STAT)
+        if not status.ok or listing is None:
+            raise FileNotFoundError(f"cannot list {join_url(server, directory)}: {status.message}")
+        names = [entry.name for entry in listing]
+    else:
+        if not os.path.isdir(directory):
+            raise FileNotFoundError(f"cannot list {directory}")
+        names = os.listdir(directory)
+    return sorted(name for name in names if name.endswith(".root"))
+
+
+def friend_bases(ntuples: NtupleConfig, kind: str) -> list[str]:
+    """The friend bases that apply to a sample kind, in configured order (later friends override earlier ones)."""
+    return [friend.base for friend in ntuples.friends if kind in friend.applies_to]
+
+
+def discover(ntuples: NtupleConfig, era: str, nick: str, channel: str, kind: str) -> list[NtupleFile]:
+    """Main files of one sample and channel with their friend files; missing friends are an error."""
+    main_dir = f"{ntuples.base.rstrip('/')}/{era}/{nick}/{channel}"
+    basenames = list_root_files(ntuples.server, main_dir)
+    friend_dirs = [f"{base.rstrip('/')}/{era}/{nick}/{channel}" for base in friend_bases(ntuples, kind)]
+    friend_listings = [set(list_root_files(ntuples.server, directory)) for directory in friend_dirs]
+    missing = [f"{directory}/{name}" for directory, names in zip(friend_dirs, friend_listings) for name in basenames if name not in names]
+    if missing:
+        raise FileNotFoundError("missing friend files:\n" + "\n".join(missing))
+    return [
+        NtupleFile(
+            path=join_url(ntuples.server, f"{main_dir}/{name}"),
+            friends=tuple(join_url(ntuples.server, f"{directory}/{name}") for directory in friend_dirs),
+            nick=nick,
+            channel=channel,
+            basename=name,
+        )
+        for name in basenames
+    ]
+
+
+def read_ntuple(ntuple: NtupleFile, columns: set[str], optional: set[str] = frozenset()) -> tuple[pd.DataFrame, dict, set[str]]:
+    """The requested columns of one ntuple (+ friends), the CROWN metadata of the main file and the names of all
+    branches, opening every file once (remote opens cost seconds).
+
+    Columns in `optional` may be absent (they are left out); any other missing column raises MissingColumnsError.
+    """
+    paths = (*ntuple.friends, ntuple.path)  # main file last so that it overrides friends
+    handles = [uproot.open(path) for path in paths]
+    try:
+        sources: dict[str, int] = {}
+        for index, handle in enumerate(handles):
+            for name in handle[TREE].keys():
+                sources[name] = index
+        missing = set(columns) - set(sources)
+        if missing - set(optional):
+            raise MissingColumnsError(sorted(missing - set(optional)), ntuple.path)
+        by_handle: dict[int, list[str]] = {}
+        for column in sorted(set(columns) & set(sources)):
+            by_handle.setdefault(sources[column], []).append(column)
+        frames = [pd.DataFrame(handles[index][TREE].arrays(names, library="np")) for index, names in by_handle.items()]
+        main = handles[-1]
+        metadata = json.loads(str(main["metadata"])) if "metadata" in main else {}
+    finally:
+        for handle in handles:
+            handle.close()
+    lengths = {len(frame) for frame in frames}
+    if len(lengths) > 1:
+        raise ValueError(f"{ntuple.path}: friend trees have different numbers of entries {sorted(lengths)}")
+    return (pd.concat(frames, axis=1) if frames else pd.DataFrame()), metadata, set(sources)
diff --git a/src/shapesmith/plotting/stack.py b/src/shapesmith/plotting/stack.py
index 4f3fb7a..d55d39f 100644
--- a/src/shapesmith/plotting/stack.py
+++ b/src/shapesmith/plotting/stack.py
@@ -12,20 +12,20 @@
 import numpy as np  # noqa: E402
 
 from shapesmith.histogram import Histogram  # noqa: E402
-from shapesmith.histograms import INCLUSIVE, NOMINAL_REGION, NOMINAL_VARIATION, HistKey, HistogramSet  # noqa: E402
-from shapesmith.model import Analysis  # noqa: E402
+from shapesmith.histogram import INCLUSIVE, NOMINAL_VARIATION, HistKey, HistogramSet  # noqa: E402
+from shapesmith.model import NOMINAL, Analysis  # noqa: E402
 from shapesmith.plotting.style import axis_label, color, grouped_backgrounds, label  # noqa: E402
 
 logger = logging.getLogger(__name__)
 hep.style.use("CMS")
 
 
-def _nominal(hset: HistogramSet, channel: str, category: str, process: str, variable: str, region: str = NOMINAL_REGION) -> Histogram | None:
+def _nominal(hset: HistogramSet, channel: str, category: str, process: str, variable: str, region: str = NOMINAL) -> Histogram | None:
     key = HistKey(channel, category, process, region, NOMINAL_VARIATION, variable)
-    return hset.get(key) if hset.has(key) else None
+    return hset.get(key)
 
 
-def _group_histogram(hset: HistogramSet, channel: str, category: str, members: list[str], variable: str, region: str = NOMINAL_REGION) -> Histogram | None:
+def _group_histogram(hset: HistogramSet, channel: str, category: str, members: list[str], variable: str, region: str = NOMINAL) -> Histogram | None:
     total = None
     for process in members:
         h = _nominal(hset, channel, category, process, variable, region)
@@ -34,11 +34,11 @@ def _group_histogram(hset: HistogramSet, channel: str, category: str, members: l
     return total
 
 
-def default_signal_scale(hset: HistogramSet, analysis: Analysis, channel: str, category: str, variable: str, region: str = NOMINAL_REGION) -> float:
+def default_signal_scale(hset: HistogramSet, analysis: Analysis, channel: str, category: str, variable: str, region: str = NOMINAL) -> float:
     """1, 2 or 5 x 10^n such that the scaled signal maximum is about 30 % of the background maximum."""
     signal = _nominal(hset, channel, category, analysis.signal, variable, region)
     background = 0.0
-    for _, members in grouped_backgrounds(analysis):
+    for _, members in grouped_backgrounds(analysis, channel):
         h = _group_histogram(hset, channel, category, members, variable, region)
         if h is not None:
             background = max(background, float(h.values.max()))
@@ -50,16 +50,16 @@ def default_signal_scale(hset: HistogramSet, analysis: Analysis, channel: str, c
     return float(min((1, 2, 5, 10), key=lambda m: abs(m - mantissa)) * 10**exponent)
 
 
-def plot_stack(hset: HistogramSet, analysis: Analysis, channel: str, category: str, variable: str, output_dir: Path, blind: bool = False, log: bool = False, signal_scale: float | None = None, normalize_by_bin_width: bool = False, region: str = NOMINAL_REGION) -> list[Path]:
-    if region != NOMINAL_REGION and not hset.keys(channel=channel, category=category, region=region, variable=variable):
+def plot_stack(hset: HistogramSet, analysis: Analysis, channel: str, category: str, variable: str, output_dir: Path, blind: bool = False, log: bool = False, signal_scale: float | None = None, normalize_by_bin_width: bool = False, region: str = NOMINAL) -> list[Path]:
+    if region != NOMINAL and not hset.select(channel=channel, category=category, region=region, variable=variable):
         raise ValueError(f"no histograms for {channel}/{category}/{region}/{variable}; fill them with `shapesmith hist --regions {region}`")
     groups = []
-    for group, members in grouped_backgrounds(analysis):
+    for group, members in grouped_backgrounds(analysis, channel):
         h = _group_histogram(hset, channel, category, members, variable, region)
         if h is not None:
             groups.append((group, h))
     if not groups:
-        if region != NOMINAL_REGION:
+        if region != NOMINAL:
             raise ValueError(f"no background histograms for {channel}/{category}/{region}/{variable}; fill them with `shapesmith hist --regions {region}`")
         logger.warning(f"{channel}/{category}/{variable}: no background histograms, no plot")
         return []
@@ -68,7 +68,7 @@ def plot_stack(hset: HistogramSet, analysis: Analysis, channel: str, category: s
     norm = widths if normalize_by_bin_width else np.ones_like(widths)
     background = np.sum([h.values for _, h in groups], axis=0)
     background_error = np.sqrt(np.sum([h.variances for _, h in groups], axis=0))
-    data_hist = _nominal(hset, channel, category, "data", variable, region)
+    data_hist = _nominal(hset, channel, category, analysis.channel(channel).data(), variable, region)
     if data_hist is None and not blind:
         raise ValueError(f"data histogram is missing for {channel}/{category}/{region}/{variable}; fill data with `shapesmith hist --regions {region}` or use --blind")
     data = background.copy() if blind else data_hist.values
@@ -94,7 +94,7 @@ def plot_stack(hset: HistogramSet, analysis: Analysis, channel: str, category: s
     ax.legend(loc="upper right", ncol=2, fontsize=16, frameon=False)
     hep.cms.label(llabel="Private Work", rlabel=style.lumi_label if style else "", ax=ax, fontsize=22)
     channel_label = style.channel_labels.get(channel, channel) if style else channel
-    selection_label = f"{channel_label}, {category}" if region == NOMINAL_REGION else f"{channel_label}, {category}, {region}"
+    selection_label = f"{channel_label}, {category}" if region == NOMINAL else f"{channel_label}, {category}, {region}"
     ax.text(0.04, 0.95, selection_label, transform=ax.transAxes, va="top", ha="left", fontsize=20)
 
     with np.errstate(divide="ignore", invalid="ignore"):
@@ -114,7 +114,7 @@ def plot_stack(hset: HistogramSet, analysis: Analysis, channel: str, category: s
     rax.set_xlim(edges[0], edges[-1])
 
     output_dir = Path(output_dir) / channel
-    if region != NOMINAL_REGION:
+    if region != NOMINAL:
         output_dir = output_dir / region
     output_dir.mkdir(parents=True, exist_ok=True)
     paths = [output_dir / f"{category}_{variable}.{ext}" for ext in ("pdf", "png")]
@@ -124,15 +124,15 @@ def plot_stack(hset: HistogramSet, analysis: Analysis, channel: str, category: s
     return paths
 
 
-def run_plot(hset: HistogramSet, analysis: Analysis, channels: list[str], control: bool, category: str | None, variables: list[str] | None, output_dir: Path, region: str = NOMINAL_REGION, **options) -> list[Path]:
+def run_plot(hset: HistogramSet, analysis: Analysis, channels: list[str], control: bool, category: str | None, variables: list[str] | None, output_dir: Path, region: str = NOMINAL, **options) -> list[Path]:
     written = []
     for channel in channels:
         analysis.channel(channel).region(region)
         if control:
-            for variable in variables or list(analysis.control_variables):
+            for variable in variables or list(analysis.channel(channel).variables):
                 written += plot_stack(hset, analysis, channel, INCLUSIVE, variable, output_dir, region=region, **options)
         else:
-            for cat in analysis.categories:
+            for cat in analysis.channel(channel).categories:
                 if category is None or cat.name == category:
                     written += plot_stack(hset, analysis, channel, cat.name, cat.variable.name, output_dir, region=region, **options)
     return written
diff --git a/src/shapesmith/plotting/style.py b/src/shapesmith/plotting/style.py
index 0aabe64..b5579bb 100644
--- a/src/shapesmith/plotting/style.py
+++ b/src/shapesmith/plotting/style.py
@@ -4,15 +4,13 @@
 from shapesmith.model import Analysis
 
 
-def grouped_backgrounds(analysis: Analysis) -> list[tuple[str, list[str]]]:
-    """[(group, [process names]), ...] in Style.group_order; the estimator output forms its own group."""
+def grouped_backgrounds(analysis: Analysis, channel: str) -> list[tuple[str, list[str]]]:
+    """[(group, [process names]), ...] of the channel's backgrounds in Style.group_order; an estimator output forms its own group."""
+    ch = analysis.channel(channel)
+    estimated = set(ch.backgrounds()) - {p.name for p in ch.processes}
     groups: dict[str, list[str]] = {}
-    for process in analysis.processes:
-        if process.kind in ("data", "signal"):
-            continue
-        groups.setdefault(process.plot_group, []).append(process.name)
-    if analysis.estimator is not None:
-        groups.setdefault(analysis.estimator.output, []).append(analysis.estimator.output)
+    for name in ch.backgrounds():
+        groups.setdefault(name if name in estimated else ch.process(name).plot_group, []).append(name)
     order = list(analysis.style.group_order) if analysis.style else sorted(groups)
     ordered = [(g, groups[g]) for g in order if g in groups]
     ordered += [(g, members) for g, members in groups.items() if g not in order]
diff --git a/src/shapesmith/provenance.py b/src/shapesmith/provenance.py
new file mode 100644
index 0000000..b934d75
--- /dev/null
+++ b/src/shapesmith/provenance.py
@@ -0,0 +1,45 @@
+"""What produced an output: versions, git hashes, the run configuration."""
+from __future__ import annotations
+
+import hashlib
+import importlib
+import json
+import subprocess
+from datetime import datetime, timezone
+from pathlib import Path
+
+from shapesmith import __version__
+from shapesmith.config import RunConfig
+
+
+def git_hash(path: Path) -> str | None:
+    """HEAD commit of the git checkout containing `path`, or None if there is none (or git is missing)."""
+    path = Path(path)
+    directory = path.parent if path.is_file() else path
+    try:
+        return subprocess.run(["git", "-C", str(directory), "rev-parse", "HEAD"], capture_output=True, text=True, check=True).stdout.strip()
+    except (OSError, subprocess.CalledProcessError):
+        return None
+
+
+def record(config: RunConfig) -> dict:
+    """Versions and git hashes of core, analysis and sample database, and the resolved run configuration with its hash."""
+    module = importlib.import_module(config.analysis.partition(":")[0])
+    resolved = json.loads(config.model_dump_json())
+    return {
+        "written": datetime.now(timezone.utc).isoformat(timespec="seconds"),
+        "shapesmith": {"version": __version__, "git": git_hash(Path(__file__))},
+        "analysis": {"module": config.analysis, "git": git_hash(Path(module.__file__))},
+        "sample_database": {"path": str(config.sample_database), "git": git_hash(config.sample_database)} if config.sample_database else None,
+        "ntuples": config.ntuples.base,
+        "config": resolved,
+        "config_sha256": hashlib.sha256(json.dumps(resolved, sort_keys=True).encode()).hexdigest(),
+    }
+
+
+def write_versions(config: RunConfig, analysis_name: str, directory: Path) -> Path:
+    """`<directory>/versions/<analysis_name>.json`: one record per analysis, so analyses sharing a directory keep theirs."""
+    path = Path(directory) / "versions" / f"{analysis_name}.json"
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(record(config), indent=2))
+    return path
diff --git a/src/shapesmith/shapes.py b/src/shapesmith/shapes.py
new file mode 100644
index 0000000..4f51af0
--- /dev/null
+++ b/src/shapesmith/shapes.py
@@ -0,0 +1,44 @@
+"""combine-style shape files: one writer, and the synced layout <channel>_<category>/{data_obs, <process>, <process>_<variation>}."""
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Iterable
+
+import uproot
+
+from shapesmith.histogram import NOMINAL_VARIATION, HistKey, Histogram, HistogramSet
+from shapesmith.model import NOMINAL, Analysis
+
+
+def write_shapes(path: Path, entries: Iterable[tuple[str, str, Histogram]]) -> Path:
+    """Write (folder, name, histogram) entries as TH1D `folder/name` into a new ROOT file."""
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with uproot.recreate(path) as f:
+        for folder, name, h in entries:
+            f[f"{folder}/{name}"] = h.to_root(name)
+    return path
+
+
+def shape_name(process: str, variation: str) -> str:
+    return process if variation == NOMINAL_VARIATION else f"{process}_{variation}"
+
+
+def synced_entries(hset: HistogramSet, analysis: Analysis, channel_name: str) -> list[tuple[str, str, Histogram]]:
+    """data_obs and every nominal-region histogram of the signal and the backgrounds, per category."""
+    channel = analysis.channel(channel_name)
+    entries = []
+    for category in channel.categories:
+        variable = category.variable.name
+        folder = f"{channel_name}_{category.name}"
+        data_key = HistKey(channel_name, category.name, channel.data(), NOMINAL, NOMINAL_VARIATION, variable)
+        if data_key in hset:
+            entries.append((folder, "data_obs", hset[data_key]))
+        for process in (*channel.backgrounds(), analysis.signal):
+            for key in hset.select(channel=channel_name, category=category.name, process=process, region=NOMINAL, variable=variable):
+                entries.append((folder, shape_name(process, key.variation), hset[key]))
+    return entries
+
+
+def run_sync(hset: HistogramSet, analysis: Analysis, channels: list[str], output_dir: Path) -> list[Path]:
+    return [write_shapes(Path(output_dir) / "synced" / f"htt_{channel}.inputs-Run{analysis.era}.root", synced_entries(hset, analysis, channel)) for channel in channels]
diff --git a/src/shapesmith/skim.py b/src/shapesmith/skim.py
index ef0b7d5..912ccc8 100644
--- a/src/shapesmith/skim.py
+++ b/src/shapesmith/skim.py
@@ -1,9 +1,21 @@
-"""Stage 1: ntuples (+ friends) -> Parquet skims with the loose baseline selection (Spec §7)."""
+"""Stage 1: ntuples (+ friends) -> Parquet skims with the loose skim selection and every column the analysis needs.
+
+Columns: per sample, the columns of every expression its processes can use (all regions, categories, variables and
+the variations of its kind), the skim cuts, keep_columns, `event`, `genWeight` for MC and those of its own cut. For
+every CROWN shift of its kind (a ColumnVariation with a suffix) the shifted branches `c + suffix` are read where
+they exist.
+Rows: an event is kept if the skim cuts pass nominally or under a column variation of its kind; then the sample cut
+applies (the normalisation stays that of the whole sample).
+Checks, per file, where the channel declares CROWN shifts for the sample kind: every declared shift has a shifted
+branch, and every shifted branch `c__X` of a needed column has a declared suffix `__X`.
+Reuse: a stored skim serves when its skim cuts, normalisation and sample cut are equal, its column variations and
+friends contain the current ones and its Parquet schema holds the needed columns; see reuse_problems.
+"""
 from __future__ import annotations
 
 import logging
 from concurrent.futures import ThreadPoolExecutor
-from dataclasses import dataclass, field, replace
+from dataclasses import dataclass, field
 from pathlib import Path
 
 import numpy as np
@@ -11,18 +23,16 @@
 
 from shapesmith import __version__
 from shapesmith.config import RunConfig
-from shapesmith.expressions import columns_in, mask
-from shapesmith.io.discovery import NtupleFile, discover
-from shapesmith.io.ntuples import read_ntuple
-from shapesmith.io.skims import manifest_path, read_manifest, skim_path, write_manifest, write_skim
+from shapesmith.events import select
+from shapesmith.expressions import columns_in, columns_of, mask, shift
+from shapesmith.model import NOMINAL_REGION, Analysis, Channel, ColumnVariation, Sample, WeightVariation
+from shapesmith.ntuples import NtupleFile, discover, friend_bases, read_ntuple
 from shapesmith.parallel import run_jobs
-from shapesmith.model import Analysis, Channel, Sample
+from shapesmith.store import SKIM_COLUMNS, manifest_path, read_manifest, schema, skim_path, write_manifest, write_skim
 
 logger = logging.getLogger(__name__)
 
-SKIM_COLUMNS = ("sample_nick", "norm_weight", "is_data", "is_mc", "is_embedding")
-EVENT_COLUMNS = ("event",)
-MC_COLUMNS = ("genWeight",)
+ROW_FIELDS = ("selection", "normalisation", "sample_cut", "column_variations", "friends")  # the contract fields that define the rows
 
 
 @dataclass(frozen=True)
@@ -36,66 +46,60 @@ class SkimResult:
     skipped: bool = False  # reused from a previous run; the counts come from its manifest record
 
 
-def _columns(expressions) -> set[str]:
-    columns: set[str] = set()
-    for expr in expressions:
-        columns |= columns_in(expr)
-    return columns
+def column_variations(channel: Channel, kind: str) -> list[ColumnVariation]:
+    return [v for v in channel.variations if isinstance(v, ColumnVariation) and kind in v.applies_to]
 
 
-def columns_for_sample(analysis: Analysis, channel_name: str, sample: Sample) -> set[str]:
-    """Columns this sample must carry: the common cuts, regions, categories and variables, the cuts (and for
-    MC/embedding the weights) of the processes of its own group; MC additionally the baseline weights, the
-    weight variations and the generator columns. A sample cut adds its own columns."""
-    channel = analysis.channel(channel_name)
-    columns = _columns(channel.skim.cuts.values()) | _columns(channel.baseline.cuts.values()) | set(channel.keep_columns) | set(EVENT_COLUMNS)
-    for region in channel.regions:
-        columns |= _columns(region.replace_cuts.values())
-        region_weights = [expr for name, expr in region.add_weights.items() if sample.kind == "mc" or name not in channel.baseline.weights]
-        columns |= _columns(region_weights)
-    for category in analysis.categories:
-        columns |= columns_in(category.cut) | columns_in(category.variable.expr)
-    for variable in analysis.control_variables.values():
-        columns |= columns_in(variable.expr)
-    if sample.cut is not None:
-        columns |= columns_in(sample.cut)
-    for process in analysis.processes:
+def needed_columns(channel: Channel, sample: Sample) -> set[str]:
+    """Every nominal column the sample's histograms, estimates and exports can read."""
+    weight_variations = [v for v in channel.variations if isinstance(v, WeightVariation) and sample.kind in v.applies_to]
+    exprs = [*channel.skim.values(), *(c.cut for c in channel.categories), *(c.variable.expr for c in channel.categories)]
+    exprs += [variable.expr for variable in channel.variables.values()]
+    exprs += [expr for variation in column_variations(channel, sample.kind) for expr in variation.derived.values()]
+    for process in channel.processes:
         if process.group != sample.group:
             continue
-        selection = process.selection_for(channel_name)
-        columns |= _columns(selection.cuts.values())
-        if sample.kind != "data":
-            columns |= _columns(selection.weights.values())
+        for region in (NOMINAL_REGION, *channel.regions):
+            for variation in (None, *weight_variations):
+                selection = select(channel, process, region, variation)
+                if selection is not None:
+                    exprs += selection[0] + selection[1]
+    columns = columns_of(exprs) | set(channel.keep_columns) | {"event"}
     if sample.kind == "mc":
-        columns |= _columns(channel.skim.weights.values()) | _columns(channel.baseline.weights.values()) | set(MC_COLUMNS)
-        for variation in analysis.weight_variations:
-            columns |= _columns(variation.replace_weights.values())
+        columns.add("genWeight")
+    if sample.cut is not None:
+        columns |= columns_in(sample.cut)
     return columns
 
 
-def required_columns(analysis: Analysis, channel_name: str) -> set[str]:
-    """Every column any sample of the channel needs (the skim reads this union from each file), except the columns of a sample
-    cut: only that sample reads them, so a cut on a new column leaves the skims and contracts of the other samples unchanged."""
-    columns: set[str] = set()
-    for sample in analysis.samples:
-        if sample.channels is None or channel_name in sample.channels:
-            columns |= columns_for_sample(analysis, channel_name, replace(sample, cut=None))
-    return columns
+def check_shifts(channel: Channel, kind: str, columns: set[str], branches: set[str]) -> None:
+    """Both directions of the declared CROWN shifts, on one file; nothing is checked for a kind without any."""
+    declared = {v.suffix: v.name for v in column_variations(channel, kind) if v.suffix}
+    if not declared:
+        return
+    problems = [f"declared shift {name} ({suffix}) has no shifted branch" for suffix, name in declared.items() if not any(c + suffix in branches for c in columns)]
+    found = {"__" + branch.split("__", 1)[1] for branch in branches if "__" in branch and branch.split("__", 1)[0] in columns}
+    problems += [f"undeclared shift {suffix}" for suffix in sorted(found - set(declared))]
+    if problems:
+        raise ValueError("; ".join(problems))
 
 
 def skim_one(ntuple: NtupleFile, sample: Sample, channel: Channel, columns: set[str], out_path: Path, optional: set[str] = frozenset()) -> SkimResult:
-    """Read one file, apply the skim selection and the sample cut, add the bookkeeping columns, write Parquet.
-
-    Every requested column must exist, except the `optional` ones (MC weights in data/embedding files),
-    which are left out and reported at debug level. A missing column is a configuration error, not a warning.
-    """
-    frame, metadata = read_ntuple(ntuple, columns, optional)
-    if len(frame.columns) < len(columns):
-        logger.debug(f"{ntuple.basename} ({sample.nick}): optional columns not in this sample: {sorted(set(columns) - set(frame.columns))}")
+    """Read one file, keep the events that pass the skim cuts under any variation and the sample cut, add the
+    bookkeeping columns and write Parquet. Every column in `columns` must exist; `optional` ones may be absent."""
+    variations = column_variations(channel, sample.kind)
+    shifted = {column + v.suffix for v in variations if v.suffix for column in columns}
+    frame, metadata, branches = read_ntuple(ntuple, columns | shifted, set(optional) | shifted)
+    check_shifts(channel, sample.kind, columns, branches)
     n_in = len(frame)
-    selected = mask(frame, channel.skim.cuts)
+    nominal = list(channel.skim.values())
+    selected = mask(frame, nominal)
+    for variation in variations:
+        cuts = [shift(expr, variation, set(frame.columns)) for expr in nominal]
+        if cuts != nominal:
+            selected |= mask(frame, cuts)
     if sample.cut is not None:
-        selected &= mask(frame, {"sample_cut": sample.cut})  # the normalisation stays that of the whole sample
+        selected &= mask(frame, [sample.cut])
     frame = frame[selected].reset_index(drop=True)
     sign = np.sign(frame["genWeight"].to_numpy()) if sample.kind == "mc" else np.ones(len(frame))
     sign[sign == 0] = 1.0
@@ -111,145 +115,136 @@ def _skim_job(args) -> SkimResult:
     return skim_one(*args)
 
 
-def select_samples(analysis: Analysis, samples: list[str] | None) -> tuple[Sample, ...]:
+def select_samples(channel: Channel, names: list[str] | None) -> tuple[Sample, ...]:
     """All samples, or those selected by name: a sample group (exact) or, for names that are not a group, a nick prefix."""
-    if not samples:
-        return analysis.samples
-    groups = {s.group for s in analysis.samples}
-    prefixes = [name for name in samples if name not in groups]
-    chosen = tuple(s for s in analysis.samples if s.group in samples or any(s.nick.startswith(prefix) for prefix in prefixes))
-    if not chosen:
-        raise ValueError(f"no sample matches {samples}; groups: {sorted({s.group for s in analysis.samples})}")
-    return chosen
+    if not names:
+        return channel.samples
+    groups = {s.group for s in channel.samples}
+    prefixes = [name for name in names if name not in groups]
+    return tuple(s for s in channel.samples if s.group in names or any(s.nick.startswith(prefix) for prefix in prefixes))
 
 
-def _normalisation(sample: Sample) -> dict:
+def normalisation(sample: Sample) -> dict:
     return {"kind": sample.kind, "xsec": sample.xsec, "nevents": sample.nevents, "generator_weight": sample.generator_weight}
 
 
-def _skim_contract(channel: Channel, columns: set[str], sample: Sample) -> dict:
-    contract = {
-        "selection": dict(channel.skim.cuts),
-        "required_columns": sorted(columns),
-        "normalisation": _normalisation(sample),
-    }
-    if sample.cut is not None:  # recorded only when set, so that the skims of samples without a cut stay reusable
-        contract["sample_cut"] = sample.cut
-    return contract
-
-
-def _validate_reuse(path: Path, channel: Channel, columns: set[str], sample: Sample) -> dict:
-    """Reject a stale skim before its Parquet files are silently reused; returns the compatible manifest.
-
-    Legacy manifests predate selection contracts.  Their columns and
-    normalisation can be inspected, but reuse is unsafe because the selection
-    that produced their rows cannot be verified.
-    """
-    if not path.exists():
-        raise ValueError(f"skim {channel.name}/{sample.nick} is incompatible: its manifest is missing; re-run `shapesmith skim --force`")
-    manifest = read_manifest(path)
-    expected = _skim_contract(channel, columns, sample)
-    contract = manifest.get("contract")
-    problems = []
-    if contract is None:
-        problems.append("selection was not recorded in the legacy manifest")
-        stored_columns = set(manifest.get("columns", ()))
-        if not set(expected["required_columns"]) <= stored_columns:
-            problems.append("required columns changed")
-        if manifest.get("normalisation") != expected["normalisation"]:
-            problems.append("normalisation changed")
-        if manifest.get("nick") != sample.nick or manifest.get("channel") != channel.name:
-            problems.append("sample or channel changed")
-    else:
-        if contract.get("selection") != expected["selection"]:
-            problems.append("selection changed")
-        if contract.get("required_columns") != expected["required_columns"]:
-            problems.append("required columns changed")
-        if contract.get("normalisation") != expected["normalisation"]:
-            problems.append("normalisation changed")
-        if contract.get("sample_cut") != expected.get("sample_cut"):
-            problems.append("sample cut changed")
-    if problems:
-        raise ValueError(f"skim {channel.name}/{sample.nick} is incompatible ({', '.join(problems)}); re-run `shapesmith skim --force`")
-    return manifest
-
-
-def _start_manifest(path: Path, channel: Channel, columns: set[str], sample: Sample, files: list[NtupleFile], reuse: bool) -> dict:
-    """The manifest this run of the sample starts from: with `reuse`, a compatible previous manifest keeps its
-    metadata and the records of the files it finished; otherwise nothing counts as finished."""
-    manifest = {
-        "nick": sample.nick,
-        "channel": channel.name,
-        "files": [f.path for f in files],
-        "metadata": {},
-        "normalisation": _normalisation(sample),
-        "contract": _skim_contract(channel, columns, sample),
-        "completed": {},
-    }
-    if reuse:
-        previous = _validate_reuse(path, channel, columns, sample)
-        basenames = {f.basename for f in files}
-        manifest["metadata"] = previous.get("metadata", {})
-        manifest["completed"] = {name: record for name, record in previous.get("completed", {}).items() if name in basenames}
-    return manifest
-
-
-def _write_manifest(path: Path, manifest: dict, columns: set[str]) -> None:
+def contract(config: RunConfig, channel: Channel, sample: Sample, columns: set[str]) -> dict:
+    """What the stored rows and columns depend on; optional fields are recorded only when set, so that skims
+    without them stay reusable."""
+    result = {"selection": dict(channel.skim), "required_columns": sorted(columns), "normalisation": normalisation(sample)}
+    if sample.cut is not None:
+        result["sample_cut"] = sample.cut
+    variations = {v.name: v.suffix or dict(v.derived) for v in column_variations(channel, sample.kind)}
+    if variations:
+        result["column_variations"] = variations
+    friends = friend_bases(config.ntuples, sample.kind)
+    if friends:
+        result["friends"] = friends
+    return result
+
+
+def reuse_problems(stored: dict, expected: dict, stored_columns: set[str]) -> list[str]:
+    """Why a stored skim cannot serve: its rows must be a superset of the needed rows, its columns of the needed columns."""
+    problems = [f"{name} changed" for name in ("selection", "normalisation", "sample_cut") if stored.get(name) != expected.get(name)]
+    recorded = stored.get("column_variations", {})
+    missing = sorted(name for name, spec in expected.get("column_variations", {}).items() if recorded.get(name) != spec)
+    if missing:
+        problems.append(f"column variations missing: {', '.join(missing)}")
+    friends = sorted(set(expected.get("friends", [])) - set(stored.get("friends", [])))
+    if friends:
+        problems.append(f"friends missing: {', '.join(friends)}")
+    columns = sorted(set(expected["required_columns"]) - stored_columns)
+    if columns:
+        problems.append(f"columns missing: {', '.join(columns[:10])}{' ...' if len(columns) > 10 else ''}")
+    return problems
+
+
+def _plan(config: RunConfig, channel: Channel, sample: Sample, files: list[NtupleFile], force: bool) -> tuple[dict, list[tuple], str | None]:
+    """The manifest and the pending jobs of one sample, or the reason why its stored skim is incompatible.
+
+    A complete compatible skim is reused as it is. An incomplete one resumes if it has the same rows: only its
+    unfinished files are skimmed, with the stored columns too, so that the files of a sample share one schema. An
+    incomplete stored superset is skimmed again."""
+    columns = needed_columns(channel, sample)
+    outputs = [(ntuple, skim_path(config.skim_dir, channel.name, sample.nick, ntuple.basename)) for ntuple in files]
+    manifest = {"nick": sample.nick, "channel": channel.name, "files": [f.path for f in files], "metadata": {}, "completed": {}, "normalisation": normalisation(sample)}
+    manifest["contract"] = contract(config, channel, sample, columns)
+    stored_columns: set[str] = set()
+    path = manifest_path(config.skim_dir, channel.name, sample.nick)
+    if not force and any(out.exists() for _, out in outputs):
+        if not path.exists():
+            return manifest, [], "its manifest is missing"
+        previous = read_manifest(path)
+        stored_columns = schema(config.skim_dir, channel.name, [sample.nick]) - set(SKIM_COLUMNS)
+        problems = reuse_problems(previous.get("contract", {}), manifest["contract"], stored_columns)
+        if problems:
+            return manifest, [], ", ".join(problems)
+        completed = {name: record for name, record in previous.get("completed", {}).items() if name in {f.basename for f in files}}
+        if all(ntuple.basename in completed and out.exists() for ntuple, out in outputs):
+            return {**previous, "completed": completed}, [], None
+        if all(previous["contract"].get(name) == manifest["contract"].get(name) for name in ROW_FIELDS):
+            manifest.update(metadata=previous.get("metadata", {}), completed=completed)
+            columns |= stored_columns
+            manifest["contract"]["required_columns"] = sorted(columns)
+        else:
+            logger.info(f"{channel.name}/{sample.nick}: the stored skim holds more variations but is incomplete, skimming it again")
+            stored_columns = set()
+    jobs = [(ntuple, sample, channel, columns, out, stored_columns) for ntuple, out in outputs if not (ntuple.basename in manifest["completed"] and out.exists())]
+    for ntuple, *_ in jobs:
+        manifest["completed"].pop(ntuple.basename, None)  # its Parquet is gone: the record no longer counts
+    return manifest, jobs, None
+
+
+def _write_manifest(config: RunConfig, manifest: dict) -> None:
     completed = manifest["completed"].values()
     manifest.update(
         n_in=sum(record["n_in"] for record in completed),
         n_out=sum(record["n_out"] for record in completed),
-        columns=sorted(columns | set(SKIM_COLUMNS)),
+        columns=sorted(set(manifest["contract"]["required_columns"]) | set(SKIM_COLUMNS)),
         shapesmith_version=__version__,
     )
-    write_manifest(path, manifest)
+    write_manifest(manifest_path(config.skim_dir, manifest["channel"], manifest["nick"]), manifest)
 
 
 def run_skim(config: RunConfig, analysis: Analysis, channels: list[str] | None = None, force: bool = False, samples: list[str] | None = None) -> list[SkimResult]:
     """Skim every sample (or the ones selected by group/nick prefix) of every requested channel.
 
-    A file is reused when its Parquet exists and the sample's manifest records it as completed under the current
-    contract; `force` recomputes everything. The manifest is written before the first file of a sample and after
-    every finished file, so an interrupted run resumes with only the unfinished files.
+    Every incompatible stored skim is reported before anything is written; `force` recomputes everything. The
+    manifest is written before the first file of a sample and after every finished file, so an interrupted run
+    resumes with only the unfinished files.
     """
-    results: list[SkimResult] = []
+    results, jobs, manifests, incompatible = [], [], {}, []
     for channel_name in channels or config.channels:
         channel = analysis.channel(channel_name)
-        shared = required_columns(analysis, channel_name)
-        jobs, manifests = [], {}
-        chosen = [s for s in select_samples(analysis, samples) if s.channels is None or channel_name in s.channels]
+        chosen = select_samples(channel, samples)
         with ThreadPoolExecutor(max_workers=min(8, max(1, len(chosen)))) as listing:  # directory listings are latency bound
             discovered = list(listing.map(lambda s: discover(config.ntuples, config.era, s.nick, channel_name, s.kind), chosen))
         for sample, files in zip(chosen, discovered):
-            own = columns_for_sample(analysis, channel_name, sample)
-            columns = shared | own  # plus the columns of the sample's own cut
-            path = manifest_path(config.skim_dir, channel_name, sample.nick)
-            outputs = [(ntuple, skim_path(config.skim_dir, channel_name, sample.nick, ntuple.basename)) for ntuple in files]
-            reuse = not force and any(out_path.exists() for _, out_path in outputs)
-            manifest = manifests[sample.nick] = _start_manifest(path, channel, columns, sample, files, reuse)
-            pending = 0
-            for ntuple, out_path in outputs:
-                record = manifest["completed"].get(ntuple.basename)
-                if record is not None and out_path.exists():
-                    results.append(SkimResult(sample.nick, ntuple.basename, record["n_in"], record["n_out"], out_path, skipped=True))
-                    continue
-                manifest["completed"].pop(ntuple.basename, None)  # its Parquet is gone: the record no longer counts
-                jobs.append((ntuple, sample, channel, columns, out_path, columns - own))
-                pending += 1
-            if pending:  # record the contract before the first file is written
-                _write_manifest(path, manifest, columns)
-        logger.info(f"{channel_name}: skimming {len(jobs)} files with {config.workers} workers")
-        failures = []
-        for job, result, error in run_jobs(_skim_job, jobs, config.workers):
-            if error is not None:  # collect, report all at the end
-                failures.append(f"{job[0].path}: {error}")
+            manifest, pending, problem = _plan(config, channel, sample, files, force)
+            if problem is not None:
+                incompatible.append(f"{channel_name}/{sample.nick}: {problem}")
                 continue
-            results.append(result)
-            manifest = manifests[result.nick]
-            manifest["completed"][result.basename] = {"n_in": result.n_in, "n_out": result.n_out}
-            if not manifest["metadata"]:
-                manifest["metadata"] = result.metadata
-            _write_manifest(manifest_path(config.skim_dir, channel_name, result.nick), manifest, job[3])  # the columns of this sample
-        if failures:
-            raise RuntimeError("skim failures:\n" + "\n".join(failures))
+            manifests[channel_name, sample.nick] = manifest
+            results += [SkimResult(sample.nick, name, record["n_in"], record["n_out"], skim_path(config.skim_dir, channel_name, sample.nick, name), skipped=True) for name, record in manifest["completed"].items()]
+            jobs += pending
+    if samples and not manifests and not incompatible:
+        raise ValueError(f"no sample matches {samples}")
+    if incompatible:
+        raise ValueError("incompatible skims (re-run `shapesmith skim --force --samples ...` for them):\n" + "\n".join(incompatible))
+    for channel_name, nick in {(job[2].name, job[1].nick) for job in jobs}:
+        _write_manifest(config, manifests[channel_name, nick])  # the contract is recorded before the first file is written
+    logger.info(f"skimming {len(jobs)} files with {config.workers} workers")
+    failures = []
+    for job, result, error in run_jobs(_skim_job, jobs, config.workers):
+        if error is not None:  # collect, report all at the end
+            failures.append(f"{job[0].path}: {error}")
+            continue
+        results.append(result)
+        manifest = manifests[job[2].name, result.nick]
+        manifest["completed"][result.basename] = {"n_in": result.n_in, "n_out": result.n_out}
+        if not manifest["metadata"]:
+            manifest["metadata"] = result.metadata
+        _write_manifest(config, manifest)
+    if failures:
+        raise RuntimeError("skim failures:\n" + "\n".join(failures))
     return results
diff --git a/src/shapesmith/io/skims.py b/src/shapesmith/store.py
similarity index 61%
rename from src/shapesmith/io/skims.py
rename to src/shapesmith/store.py
index 62071d7..4b2e9ee 100644
--- a/src/shapesmith/io/skims.py
+++ b/src/shapesmith/store.py
@@ -1,4 +1,4 @@
-"""Parquet skims and their manifests (Spec §7 steps 5-6)."""
+"""Parquet skims and their manifests: <skim_dir>/<channel>/<nick>/{*.parquet, manifest.json}."""
 from __future__ import annotations
 
 import json
@@ -12,6 +12,9 @@
 import pyarrow.parquet as pq
 
 
+SKIM_COLUMNS = ("sample_nick", "norm_weight", "is_data", "is_mc", "is_embedding")  # added to every skim
+
+
 class SkimMissingError(FileNotFoundError):
     pass
 
@@ -42,19 +45,32 @@ def write_skim(frame: pd.DataFrame, path: Path) -> None:
     _replace(path, lambda temporary: pq.write_table(table, temporary, compression="snappy"))
 
 
+def _files(skim_dir: Path, channel: str, nick: str) -> list[Path]:
+    directory = Path(skim_dir) / channel / nick
+    files = sorted(directory.glob("*.parquet")) if directory.is_dir() else []
+    if not files:
+        raise SkimMissingError(f"no skims for {nick} in {directory} (run `shapesmith skim`)")
+    return files
+
+
+def schema(skim_dir: Path, channel: str, nicks: Iterable[str]) -> set[str]:
+    """The columns every skim of the given nicks holds."""
+    columns = None
+    for nick in nicks:
+        names = set(pq.read_schema(_files(skim_dir, channel, nick)[0]).names)
+        columns = names if columns is None else columns & names
+    return columns or set()
+
+
 def read_skims(skim_dir: Path, channel: str, nicks: Iterable[str], columns: Iterable[str] | None) -> pd.DataFrame:
-    """All skim rows of the given nicks in one DataFrame (only `columns` if given)."""
+    """All skim rows of the given nicks in one DataFrame, in nick and file order (only `columns` if given)."""
     frames = []
     for nick in nicks:
-        directory = Path(skim_dir) / channel / nick
-        files = sorted(directory.glob("*.parquet")) if directory.is_dir() else []
-        if not files:
-            raise SkimMissingError(f"no skims for {nick} in {directory} (run `shapesmith skim`)")
-        dataset = ds.dataset([str(f) for f in files], format="parquet")
+        dataset = ds.dataset([str(f) for f in _files(skim_dir, channel, nick)], format="parquet")
         if columns is not None:
             missing = sorted(set(columns) - set(dataset.schema.names))
             if missing:
-                raise SkimMissingError(f"skims of {nick} in {directory} lack columns {missing} (re-run `shapesmith skim --force` after changing the analysis)")
+                raise SkimMissingError(f"skims of {nick} in {Path(skim_dir) / channel / nick} lack columns {missing} (re-run `shapesmith skim --force` after changing the analysis)")
         table = dataset.to_table(columns=list(columns) if columns is not None else None)
         frames.append(table.to_pandas())
     return pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
diff --git a/src/shapesmith/sync.py b/src/shapesmith/sync.py
deleted file mode 100644
index 569ee33..0000000
--- a/src/shapesmith/sync.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""combine-style shape files per channel: <channel>_<category>/{data_obs, <process>, <process>_<variation>} (Spec §10.1)."""
-from __future__ import annotations
-
-from pathlib import Path
-
-import uproot
-
-from shapesmith.histograms import NOMINAL_REGION, NOMINAL_VARIATION, HistKey, HistogramSet
-from shapesmith.model import Analysis
-
-
-def synced_processes(analysis: Analysis) -> tuple[str, ...]:
-    return (*analysis.backgrounds(), analysis.signal)
-
-
-def run_sync(hset: HistogramSet, analysis: Analysis, channels: list[str], output_dir: Path) -> list[Path]:
-    directory = Path(output_dir) / "synced"
-    directory.mkdir(parents=True, exist_ok=True)
-    written = []
-    for channel in channels:
-        path = directory / f"htt_{channel}.inputs-Run{analysis.era}.root"
-        with uproot.recreate(path) as f:
-            for category in analysis.categories:
-                variable = category.variable.name
-                folder = f"{channel}_{category.name}"
-                data_key = HistKey(channel, category.name, "data", NOMINAL_REGION, NOMINAL_VARIATION, variable)
-                if hset.has(data_key):
-                    f[f"{folder}/data_obs"] = hset.get(data_key).to_root("data_obs")
-                for process in synced_processes(analysis):
-                    for key in hset.keys(channel=channel, category=category.name, process=process, region=NOMINAL_REGION, variable=variable):
-                        name = process if key.variation == NOMINAL_VARIATION else f"{process}_{key.variation}"
-                        f[f"{folder}/{name}"] = hset.get(key).to_root(name)
-        written.append(path)
-    return written
diff --git a/src/shapesmith/testing.py b/src/shapesmith/testing.py
index 77a58cc..a48038b 100644
--- a/src/shapesmith/testing.py
+++ b/src/shapesmith/testing.py
@@ -3,6 +3,7 @@
 The mini dataset mimics the CROWN layout:
     <root>/CROWNRun/<era>/<nick>/<channel>/<nick>_<i>.root          main ntuples
     <root>/CROWNFriends/nn/<era>/<nick>/<channel>/<nick>_<i>.root   friend trees (score, cls, fake_factor)
+With `variations`, it also has an embedded sample and CROWN shifts (`<column>__<shift>`) in main and friend trees.
 """
 from __future__ import annotations
 
@@ -13,6 +14,7 @@
 import uproot
 
 MINI_NICKS = ["DATA_A", "ZTT_1", "SIG_1"]
+EMBEDDING_NICK = "EMB_A"
 
 
 def make_ntuple(path: Path, columns: dict[str, np.ndarray], metadata: dict | None = None, tree: str = "ntuple") -> Path:
@@ -54,14 +56,33 @@ def _friend_columns(rng: np.random.Generator, n: int) -> dict[str, np.ndarray]:
     }
 
 
-def make_mini_dataset(root: Path, era: str = "2018", channel: str = "mt", n_files: int = 2, n_events: int = 1000, seed: int = 1) -> dict:
-    """Create main ntuples and friend trees for three nicks (data, DY, signal) below `root`."""
+def _shifts(columns: dict[str, np.ndarray], column: str, name: str, factors: tuple[float, float]) -> dict[str, np.ndarray]:
+    """CROWN-style shifted copies `<column>__<name>Up/Down` of one column, scaled by the given factors."""
+    return {f"{column}__{name}{direction}": (columns[column] * factor).astype(columns[column].dtype) for direction, factor in zip(("Up", "Down"), factors)}
+
+
+def make_mini_dataset(root: Path, era: str = "2018", channel: str = "mt", n_files: int = 2, n_events: int = 1000, seed: int = 1, variations: bool = False) -> dict:
+    """Create main ntuples and friend trees for three nicks (data, DY, signal) below `root`.
+
+    With `variations`, an embedded nick EMB_A (with `emb_genweight`) is added, the simulated and embedded main trees
+    carry the shift `m_vis__tesUp/Down` and every friend tree `fake_factor__ffStatUp/Down`. The columns of the three
+    nicks without variations stay exactly those of the plain dataset."""
     root = Path(root)
     rng = np.random.default_rng(seed)
-    for nick in MINI_NICKS:
+    nicks = MINI_NICKS + ([EMBEDDING_NICK] if variations else [])
+    for nick in nicks:
+        sample_type = {"DATA_A": "data", EMBEDDING_NICK: "embedding"}.get(nick, "mc")
         for i in range(n_files):
             name = f"{nick}_{i}.root"
-            metadata = {"analysis": "mini", "era": era, "sample_type": "data" if nick == "DATA_A" else "mc"}
-            make_ntuple(root / "CROWNRun" / era / nick / channel / name, _main_columns(rng, n_events, i * n_events, nick == "DATA_A"), metadata)
-            make_ntuple(root / "CROWNFriends" / "nn" / era / nick / channel / name, _friend_columns(rng, n_events))
-    return {"ntuples": root / "CROWNRun", "friends": root / "CROWNFriends" / "nn", "nicks": list(MINI_NICKS)}
+            main = _main_columns(rng, n_events, i * n_events, sample_type == "data")
+            friend = _friend_columns(rng, n_events)
+            if variations:
+                if sample_type == "embedding":
+                    main["emb_genweight"] = np.ones(n_events, dtype=np.float32)
+                if sample_type != "data":
+                    main.update(_shifts(main, "m_vis", "tes", (1.03, 0.97)))
+                friend.update(_shifts(friend, "fake_factor", "ffStat", (1.2, 0.8)))
+            metadata = {"analysis": "mini", "era": era, "sample_type": sample_type}
+            make_ntuple(root / "CROWNRun" / era / nick / channel / name, main, metadata)
+            make_ntuple(root / "CROWNFriends" / "nn" / era / nick / channel / name, friend)
+    return {"ntuples": root / "CROWNRun", "friends": root / "CROWNFriends" / "nn", "nicks": nicks}
diff --git a/src/shapesmith/validate.py b/src/shapesmith/validate.py
new file mode 100644
index 0000000..fe42697
--- /dev/null
+++ b/src/shapesmith/validate.py
@@ -0,0 +1,97 @@
+"""Consistency checks of an Analysis; every problem is collected and reported at once."""
+from __future__ import annotations
+
+from shapesmith.model import NOMINAL, ROLES, SAMPLE_KINDS, Analysis, AnalysisError, Channel, DataMinus, TemplateShift, WeightVariation
+
+
+def _duplicates(what: str, names: list[str]) -> list[str]:
+    return [f"duplicate {what} {name}" for name in sorted({n for n in names if names.count(n) > 1})]
+
+
+def _partner(name: str) -> str | None:
+    """The other side of an Up/Down pair, None for a name without a direction (a template)."""
+    for direction, other in (("Up", "Down"), ("Down", "Up")):
+        if name.endswith(direction):
+            return name[: -len(direction)] + other
+    return None
+
+
+def _process_problems(channel: Channel) -> list[str]:
+    problems = _duplicates("process", [p.name for p in channel.processes])
+    problems += _duplicates("sample", [s.nick for s in channel.samples])
+    problems += [f"sample {s.nick}: unknown kind {s.kind}" for s in channel.samples if s.kind not in SAMPLE_KINDS]
+    for process in channel.processes:
+        kinds = {s.kind for s in channel.samples_of(process.group)}
+        if process.role not in ROLES:
+            problems.append(f"process {process.name}: unknown role {process.role}")
+        if not kinds:
+            problems.append(f"process {process.name}: no samples for group {process.group}")
+        elif len(kinds) > 1:
+            problems.append(f"process {process.name}: samples of different kinds {sorted(kinds)}")
+    return problems
+
+
+def _region_problems(channel: Channel) -> list[str]:
+    weights = {name for p in channel.processes for name in p.selection.weights}
+    problems = _duplicates("region", [NOMINAL, *(r.name for r in channel.regions)])
+    for region in channel.regions:
+        problems += [f"region {region.name}: replaces unknown cut {cut}" for cut in region.replace_cuts if cut not in channel.cuts]
+        problems += [f"region {region.name}: replaces unknown weight {w}" for w in region.replace_weights if w not in weights]
+        problems += [f"region {region.name}: adds weight {w}, which a process carries (use replace_weights)" for w in region.add_weights if w in weights]
+    return problems
+
+
+def _variation_problems(channel: Channel, region_names: set[str]) -> list[str]:
+    weights = {name for p in channel.processes for name in p.selection.weights}
+    names = [v.name for v in channel.variations]
+    problems = _duplicates("variation", names)
+    for variation in channel.variations:
+        partner = _partner(variation.name)
+        if partner is not None and partner not in names:
+            problems.append(f"variation {variation.name}: its partner {partner} is missing")
+        problems += [f"variation {variation.name}: unknown sample kind {k}" for k in variation.applies_to if k not in SAMPLE_KINDS]
+        problems += [f"variation {variation.name}: unknown region {r}" for r in variation.regions or () if r not in region_names]
+        if isinstance(variation, WeightVariation):
+            problems += [f"variation {variation.name}: replaces unknown weight {w}" for w in variation.replace_weights if w not in weights]
+        elif bool(variation.suffix) == bool(variation.derived):
+            problems.append(f"variation {variation.name}: needs exactly one of suffix and derived")
+    return problems
+
+
+def _estimator_problems(channel: Channel, region_names: set[str]) -> list[str]:
+    roles = {p.name: p.role for p in channel.processes}
+    problems = []
+    for estimator in channel.estimators:
+        if isinstance(estimator, TemplateShift):
+            problems += [f"template shift {estimator.name}: unknown process {name}" for name in (estimator.process, estimator.template) if name not in roles]
+            continue
+        regions = (estimator.region,) if isinstance(estimator, DataMinus) else (estimator.b, estimator.c, estimator.d)
+        problems += [f"estimator {estimator.output}: region {r} does not exist" for r in regions if r not in region_names]
+        problems += [f"estimator {estimator.output}: subtracts unknown process {name}" for name in estimator.subtract if name not in roles]
+        problems += [f"estimator {estimator.output}: subtracts the auxiliary process {name}" for name in estimator.subtract if roles.get(name) == "auxiliary"]
+        if list(roles.values()).count("data") != 1:
+            problems.append(f"estimator {estimator.output}: needs exactly one data process")
+        if estimator.output in roles:
+            problems.append(f"estimator {estimator.output}: output collides with a process")
+    return problems
+
+
+def channel_problems(channel: Channel) -> list[str]:
+    region_names = {NOMINAL, *(r.name for r in channel.regions)}
+    problems = _process_problems(channel) + _region_problems(channel)
+    problems += _duplicates("category", [c.name for c in channel.categories])
+    problems += _variation_problems(channel, region_names) + _estimator_problems(channel, region_names)
+    return [f"channel {channel.name}: {problem}" for problem in problems]
+
+
+def validate(analysis: Analysis) -> None:
+    """Raise AnalysisError listing every problem of the analysis."""
+    problems = [problem for channel in analysis.channels.values() for problem in channel_problems(channel)]
+    processes = {p.name: p.role for channel in analysis.channels.values() for p in channel.processes}
+    if analysis.signal not in processes:
+        problems.append(f"signal {analysis.signal} is not a process")
+    if analysis.ml is not None:
+        problems += [f"ML export: {name} is an auxiliary process" for name in analysis.ml.processes if processes.get(name) == "auxiliary"]
+    if problems:
+        raise AnalysisError("\n".join(sorted(set(problems))))
+
diff --git a/tests/mini_analysis.py b/tests/mini_analysis.py
index 35f6430..f0f85c4 100644
--- a/tests/mini_analysis.py
+++ b/tests/mini_analysis.py
@@ -1,51 +1,60 @@
-"""A tiny but complete Analysis used by the test suite (matches shapesmith.testing.make_mini_dataset)."""
+"""A tiny but complete Analysis used by the test suite (matches shapesmith.testing.make_mini_dataset).
+
+`build` is the analysis of the golden references; `build_embedding` adds the embedded sample, the CROWN shifts of
+the dataset with variations and the embedding ttbar-contamination template, as the SM analysis does with embedding
+and fake factors.
+"""
+import dataclasses
+
 from shapesmith.model import (
-    Analysis, Category, Channel, Estimator, LnN, MLExportConfig, Process, Region, Sample, Selection, Style, Variable, WeightVariation,
+    Analysis, Category, Channel, ColumnVariation, DataMinus, LnN, MLExportConfig, Process, Region, Sample, Selection, Style, TemplateShift, Variable, WeightVariation,
 )
 
 SCORE = Variable("score", "score", (0.0, 0.5, 1.0))
 M_VIS = Variable("m_vis", "m_vis", (0.0, 50.0, 100.0, 200.0))
+MC_WEIGHTS = {"trg": "trg_wgt", "pu": "puweight"}
 
 
-def build(config=None) -> Analysis:
-    channel = Channel(
+def _mc(name: str, group: str, role: str, plot_group: str, cuts: dict) -> Process:
+    return Process(name, group, role, plot_group, Selection(cuts=cuts, weights=MC_WEIGHTS))
+
+
+def channel_mt() -> Channel:
+    return Channel(
         name="mt",
-        skim=Selection(cuts={"veto": "veto < 0.5", "iso_loose": "id_loose > 0.5"}, weights={}),
-        baseline=Selection(
-            cuts={"veto": "veto < 0.5", "os": "(q_1 * q_2) < 0", "tau_iso": "id_tight > 0.5"},
-            weights={"trg": "trg_wgt"},
+        samples=(
+            Sample("DATA_A", "data", "data"),
+            Sample("ZTT_1", "DY", "mc", xsec=10.0, nevents=100, generator_weight=1.0),
+            Sample("SIG_1", "HH", "mc", xsec=1.0, nevents=50, generator_weight=1.0),
+        ),
+        skim={"veto": "veto < 0.5", "iso_loose": "id_loose > 0.5"},
+        cuts={"veto": "veto < 0.5", "os": "(q_1 * q_2) < 0", "tau_iso": "id_tight > 0.5"},
+        processes=(
+            Process("data", "data", "data", "data"),
+            _mc("ZTT", "DY", "background", "Z", {"gen": "gen_match == 5"}),
+            _mc("ZL", "DY", "background", "Z", {"gen": "gen_match != 5"}),
+            _mc("HH", "HH", "signal", "signal", {}),
         ),
         regions=(
             Region("same_sign", replace_cuts={"os": "(q_1 * q_2) > 0"}),
             Region("anti_iso", replace_cuts={"tau_iso": "(id_tight < 0.5) & (id_loose > 0.5)"}, add_weights={"fake_factor": "fake_factor"}),
         ),
+        categories=(Category("sig", "cls == 0", SCORE), Category("bkg", "cls == 1", SCORE)),
+        variables={"m_vis": M_VIS},
+        variations=(WeightVariation("CMS_puUp", {"pu": "puweight_up"}), WeightVariation("CMS_puDown", {"pu": "puweight_down"})),
+        estimators=(DataMinus("jetFakes", "anti_iso", ("ZTT", "ZL")),),
         keep_columns=("event",),
     )
-    samples = (
-        Sample("DATA_A", "data", "data", channels=("mt",)),
-        Sample("ZTT_1", "DY", "mc", xsec=10.0, nevents=100, generator_weight=1.0),
-        Sample("SIG_1", "HH", "mc", xsec=1.0, nevents=50, generator_weight=1.0),
-    )
-    processes = (
-        Process("data", "data", "data", "data", "data"),
-        Process("ztt", "DY", "ZTT", "true_tau", "Z", Selection(cuts={"gen": "gen_match == 5"}, weights={"pu": "puweight"})),
-        Process("zl", "DY", "ZL", "lepton_fake", "Z", Selection(cuts={"gen": "gen_match != 5"}, weights={"pu": "puweight"})),
-        Process("hh", "HH", "HH", "signal", "signal", Selection(cuts={}, weights={"pu": "puweight"})),
-    )
+
+
+def build(config=None) -> Analysis:
     return Analysis(
         name="mini",
         era="2018",
         lumi_pb=10.0,  # MC per-event weight ~1 so that data-driven estimates stay positive in the mini dataset
-        channels={"mt": channel},
-        samples=samples,
-        processes=processes,
         signal="HH",
-        categories=(Category("sig", "cls == 0", SCORE), Category("bkg", "cls == 1", SCORE)),
-        control_variables={"m_vis": M_VIS},
-        weight_variations=(WeightVariation("CMS_puUp", {"pu": "puweight_up"}), WeightVariation("CMS_puDown", {"pu": "puweight_down"})),
-        column_variations=(),
+        channels={"mt": channel_mt()},
         lnn=(LnN("lumi", ("*",), 1.025), LnN("zjXsec", ("ZTT", "ZL"), 1.02), LnN("ffNorm_$CHANNEL", ("jetFakes",), 1.1)),
-        estimator=Estimator("fake_factors", regions={"anti_iso": "anti_iso"}, subtract=("ZTT", "ZL"), output="jetFakes"),
         style=Style(
             colors={"Z": "#3f90da", "jetFakes": "#b9ac70", "HH": "#bd1f01"},
             labels={"Z": r"Z$\rightarrow\ell\ell$", "jetFakes": r"jet$\rightarrow\tau_h$"},
@@ -62,3 +71,26 @@ def build(config=None) -> Analysis:
             region_of={"jetFakes": "anti_iso"},
         ),
     )
+
+
+def embedding_variations() -> tuple[ColumnVariation, ...]:
+    tes = tuple(ColumnVariation(f"CMS_tes{d}", f"__tes{d}", applies_to=("mc", "embedding")) for d in ("Up", "Down"))
+    ff = tuple(ColumnVariation(f"CMS_ffStat{d}", f"__ffStat{d}", applies_to=("data", "mc", "embedding"), regions=("anti_iso",)) for d in ("Up", "Down"))
+    return tes + ff
+
+
+def build_embedding(config=None) -> Analysis:
+    """EMB (genuine taus) replaces ZTT, which stays as the auxiliary template of the contamination variation."""
+    analysis = build(config)
+    channel = analysis.channel("mt")
+    emb = Process("EMB", "EMB", "background", "EMB", Selection(cuts={"gen": "gen_match == 5"}, weights={"emb": "emb_genweight", "trg": "trg_wgt"}))
+    processes = tuple(dataclasses.replace(p, role="auxiliary") if p.name == "ZTT" else p for p in channel.processes) + (emb,)
+    channel = dataclasses.replace(
+        channel,
+        samples=channel.samples + (Sample("EMB_A", "EMB", "embedding"),),
+        processes=processes,
+        variations=channel.variations + embedding_variations(),
+        estimators=(DataMinus("jetFakes", "anti_iso", ("EMB", "ZL")), TemplateShift("CMS_htt_emb_ttbar_2018", "EMB", "ZTT", 0.1)),
+    )
+    ml = dataclasses.replace(analysis.ml, processes=("EMB", "ZL", "HH", "jetFakes"), label_of={"EMB": "is_Z", "ZL": "is_Z", "HH": "is_HH", "jetFakes": "is_jetFakes"})
+    return dataclasses.replace(analysis, channels={"mt": channel}, ml=ml)
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 8c0ec9b..1087337 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -40,7 +40,7 @@ def test_full_chain(tmp_path):
     _run("validate", "-c", str(config))
     _run("skim", "-c", str(config))
     assert (tmp_path / "skims" / "mt" / "ZTT_1" / "manifest.json").exists()
-    assert (tmp_path / "skims" / "versions.json").exists()
+    assert (tmp_path / "skims" / "versions" / "mini.json").exists()
     _run("hist", "-c", str(config), "--control")
     _run("estimate", "-c", str(config), "--control")
     _run("plot", "-c", str(config), "--control")
@@ -58,15 +58,11 @@ def test_full_chain(tmp_path):
     assert (tmp_path / "ml" / "mt" / "fold0_training.feather").exists()
     result = _run("inspect", str(tmp_path / "out" / "shapes.root"))
     assert "jetFakes" in result.output
+    assert _run("inspect", "--unchanged", str(tmp_path / "out" / "shapes.root")).output == ""
     with uproot.open(tmp_path / "out" / "shapes.root") as f:
         assert "mt_sig/jetFakes#nominal#Nominal#score" in f
 
 
-def test_example_config(tmp_path):
-    _run("example-config", str(tmp_path / "example.yaml"))
-    assert "analysis:" in (tmp_path / "example.yaml").read_text()
-
-
 def test_hist_regions_and_plot_region_cli(tmp_path):
     config = _write_config(tmp_path)
     _run("skim", "-c", str(config))
diff --git a/tests/test_config.py b/tests/test_config.py
index 232414f..ead8538 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -4,7 +4,8 @@
 
 import pytest
 
-from shapesmith.config import NtupleConfig, RunConfig, load_analysis, load_config, write_versions
+from shapesmith.config import NtupleConfig, RunConfig, load_analysis, load_config
+from shapesmith.provenance import write_versions
 
 
 def _yaml(tmp_path, text):
@@ -95,7 +96,9 @@ def test_overrides(tmp_path):
 
 def test_write_versions(tmp_path):
     config = RunConfig(analysis="tests.mini_analysis:build", era="2018", channels=["mt"], ntuples=NtupleConfig(base="/data"), skim_dir=tmp_path / "s", output_dir=tmp_path / "o")
-    record = json.loads(write_versions(config, tmp_path / "o").read_text())
+    path = write_versions(config, "mini", tmp_path / "o")
+    assert path == tmp_path / "o" / "versions" / "mini.json"
+    record = json.loads(path.read_text())
     assert record["shapesmith"]["version"] and record["analysis"]["module"] == "tests.mini_analysis:build"
-    assert record["sample_database"] is None and record["config"]["channels"] == ["mt"]
-    assert (tmp_path / "o" / "versions.json").exists()
+    assert record["sample_database"] is None and record["config"]["channels"] == ["mt"] and record["ntuples"] == "/data"
+    assert len(record["config_sha256"]) == 64
diff --git a/tests/test_datacards.py b/tests/test_datacards.py
index d1fbcb1..0dcce7b 100644
--- a/tests/test_datacards.py
+++ b/tests/test_datacards.py
@@ -1,9 +1,8 @@
 import uproot
 
-from shapesmith.datacards import bin_name, rebin_edges, run_datacards, shape_processes, write_datacard
-from shapesmith.histogram import Histogram
-from shapesmith.histograms import HistKey, HistogramSet
-from tests.mini_analysis import build
+from shapesmith.datacards import bin_name, rebin_edges, run_datacards, write_datacard
+from shapesmith.histogram import HistKey, Histogram, HistogramSet
+from tests.mini_analysis import build, build_embedding
 
 
 def _hist(values, edges=(0.0, 0.25, 0.5, 0.75, 1.0)):
@@ -13,22 +12,20 @@ def _hist(values, edges=(0.0, 0.25, 0.5, 0.75, 1.0)):
 def _hset():
     hset = HistogramSet()
     for category in ("sig", "bkg"):
-        hset.add(HistKey("mt", category, "data", "nominal", "Nominal", "score"), _hist([20, 10, 5, 2]))
-        hset.add(HistKey("mt", category, "ZTT", "nominal", "Nominal", "score"), _hist([10, 5, 2, 0.4]))
-        hset.add(HistKey("mt", category, "ZTT", "nominal", "CMS_puUp", "score"), _hist([11, 5.5, 2.2, 0.44]))
-        hset.add(HistKey("mt", category, "ZTT", "nominal", "CMS_puDown", "score"), _hist([9, 4.5, 1.8, 0.36]))
-        hset.add(HistKey("mt", category, "ZL", "nominal", "Nominal", "score"), _hist([5, 3, 1, 0.3]))
-        hset.add(HistKey("mt", category, "jetFakes", "nominal", "Nominal", "score"), _hist([4, 2, 1, 0.2]))
-        hset.add(HistKey("mt", category, "HH", "nominal", "Nominal", "score"), _hist([0.01, 0.02, 0.05, 0.1]))
-        hset.add(HistKey("mt", category, "HH", "nominal", "CMS_puUp", "score"), _hist([0.011, 0.022, 0.055, 0.11]))
-        hset.add(HistKey("mt", category, "HH", "nominal", "CMS_puDown", "score"), _hist([0.009, 0.018, 0.045, 0.09]))
+        hset[HistKey("mt", category, "data", "nominal", "Nominal", "score")] = _hist([20, 10, 5, 2])
+        hset[HistKey("mt", category, "ZTT", "nominal", "Nominal", "score")] = _hist([10, 5, 2, 0.4])
+        hset[HistKey("mt", category, "ZTT", "nominal", "CMS_puUp", "score")] = _hist([11, 5.5, 2.2, 0.44])
+        hset[HistKey("mt", category, "ZTT", "nominal", "CMS_puDown", "score")] = _hist([9, 4.5, 1.8, 0.36])
+        hset[HistKey("mt", category, "ZL", "nominal", "Nominal", "score")] = _hist([5, 3, 1, 0.3])
+        hset[HistKey("mt", category, "jetFakes", "nominal", "Nominal", "score")] = _hist([4, 2, 1, 0.2])
+        hset[HistKey("mt", category, "HH", "nominal", "Nominal", "score")] = _hist([0.01, 0.02, 0.05, 0.1])
+        hset[HistKey("mt", category, "HH", "nominal", "CMS_puUp", "score")] = _hist([0.011, 0.022, 0.055, 0.11])
+        hset[HistKey("mt", category, "HH", "nominal", "CMS_puDown", "score")] = _hist([0.009, 0.018, 0.045, 0.09])
     return hset
 
 
-def test_bin_name_and_shape_processes():
-    analysis = build()
-    assert bin_name(analysis, "mt", 0) == "htt_mt_1_2018"
-    assert shape_processes(analysis) == ("ZTT", "ZL", "HH")
+def test_bin_name():
+    assert bin_name(build(), "mt", 0) == "htt_mt_1_2018"
 
 
 def test_rebin_edges_merges_from_the_right():
@@ -70,3 +67,14 @@ def test_write_datacard_without_systematics(tmp_path):
 def test_run_datacards_final_states(tmp_path):
     paths = run_datacards(_hset(), build(), {"mt": ["mt"], "all": ["mt"]}, tmp_path, systematics=True, min_background=1.0)
     assert set(paths) == {"mt", "all"} and all(p.exists() for p in paths.values())
+
+
+def test_auxiliary_processes_and_templates_stay_out_of_the_card(tmp_path):
+    hset = _hset()
+    for category in ("sig", "bkg"):
+        hset[HistKey("mt", category, "EMB", "nominal", "Nominal", "score")] = _hist([3, 2, 1, 0.5])
+        hset[HistKey("mt", category, "EMB", "nominal", "emb1p002", "score")] = _hist([3, 2, 1, 0.6])
+    text = write_datacard(hset, build_embedding(), ["mt"], tmp_path / "mt").read_text()
+    processes = [line for line in text.splitlines() if line.startswith("process")][0].split()[1:]
+    assert "ZTT" not in processes and "EMB" in processes  # ZTT is the auxiliary template of the embedding
+    assert "emb1p002" not in text
diff --git a/tests/test_estimates.py b/tests/test_estimates.py
new file mode 100644
index 0000000..2bee7fd
--- /dev/null
+++ b/tests/test_estimates.py
@@ -0,0 +1,87 @@
+import dataclasses
+
+import pytest
+
+from shapesmith.estimates import clip_negative_bins, run_estimates
+from shapesmith.histogram import INCLUSIVE, HistKey, Histogram, HistogramSet
+from shapesmith.model import ABCD
+from tests.mini_analysis import build, build_embedding
+
+
+def _hist(values, variances=None):
+    return Histogram([0.0, 1.0, 2.0], values, variances if variances is not None else values)
+
+
+def _key(process, region, category=INCLUSIVE, variation="Nominal"):
+    return HistKey("mt", category, process, region, variation, "x")
+
+
+def _with_estimators(analysis, *estimators):
+    return dataclasses.replace(analysis, channels={"mt": dataclasses.replace(analysis.channel("mt"), estimators=estimators)})
+
+
+def test_data_minus_subtracts_available_processes():
+    hset = HistogramSet()
+    hset[_key("data", "anti_iso")] = _hist([10.0, 20.0])
+    hset[_key("ZTT", "anti_iso")] = _hist([1.0, 2.0])
+    # ZL missing on purpose -> skipped with a warning
+    added = run_estimates(hset, build(), ["mt"])
+    assert added == [_key("jetFakes", "nominal")]
+    out = hset[_key("jetFakes", "nominal")]
+    assert out.values.tolist() == [9.0, 18.0] and out.variances.tolist() == [11.0, 22.0]
+
+
+def test_data_minus_propagates_column_variations_with_nominal_fallback():
+    hset = HistogramSet()
+    hset[_key("data", "anti_iso")] = _hist([10.0, 20.0])
+    hset[_key("data", "anti_iso", variation="CMS_ffStatUp")] = _hist([12.0, 24.0])
+    hset[_key("EMB", "anti_iso")] = _hist([1.0, 2.0])
+    hset[_key("EMB", "anti_iso", variation="CMS_tesUp")] = _hist([2.0, 3.0])
+    hset[_key("ZL", "anti_iso")] = _hist([0.5, 0.5])
+    hset[_key("ZL", "anti_iso", variation="CMS_puUp")] = _hist([5.0, 5.0])  # a weight variation: not propagated
+    run_estimates(hset, build_embedding(), ["mt"])
+    assert {k.variation for k in hset.select(process="jetFakes")} == {"Nominal", "CMS_ffStatUp", "CMS_tesUp"}
+    assert hset[_key("jetFakes", "nominal")].values.tolist() == [8.5, 17.5]
+    assert hset[_key("jetFakes", "nominal", variation="CMS_ffStatUp")].values.tolist() == [10.5, 21.5]  # data varied, EMB and ZL nominal
+    assert hset[_key("jetFakes", "nominal", variation="CMS_tesUp")].values.tolist() == [7.5, 16.5]  # an EMB-only variation reaches jetFakes
+
+
+def test_template_shift_uses_the_auxiliary_template():
+    hset = HistogramSet()
+    hset[_key("EMB", "nominal")] = _hist([10.0, 10.0])
+    hset[_key("ZTT", "nominal")] = _hist([2.0, 4.0])
+    added = run_estimates(hset, build_embedding(), ["mt"])
+    assert set(added) == {_key("EMB", "nominal", variation=f"CMS_htt_emb_ttbar_2018{d}") for d in ("Up", "Down")}
+    assert hset[_key("EMB", "nominal", variation="CMS_htt_emb_ttbar_2018Up")].values.tolist() == pytest.approx([10.2, 10.4])
+    assert hset[_key("EMB", "nominal", variation="CMS_htt_emb_ttbar_2018Down")].values.tolist() == pytest.approx([9.8, 9.6])
+
+
+def test_abcd_estimate():
+    analysis = _with_estimators(build(), ABCD("QCD", "abcd_anti_iso", "abcd_same_sign", "abcd_same_sign_anti_iso", ("ZTT",)))
+    hset = HistogramSet()
+    for region, data, mc in (("abcd_anti_iso", (8.0, 8.0), (2.0, 2.0)), ("abcd_same_sign", (6.0, 6.0), (2.0, 2.0)), ("abcd_same_sign_anti_iso", (4.0, 4.0), (2.0, 2.0))):
+        hset[_key("data", region)] = _hist(list(data))
+        hset[_key("ZTT", region)] = _hist(list(mc))
+    run_estimates(hset, analysis, ["mt"])
+    assert hset[_key("QCD", "nominal")].values.tolist() == [12.0, 12.0]  # B=(6,6), C=8, D=4 -> factor 2
+
+
+def test_abcd_factor_zero_when_control_regions_empty():
+    analysis = _with_estimators(build(), ABCD("QCD", "abcd_anti_iso", "abcd_same_sign", "abcd_same_sign_anti_iso", ()))
+    hset = HistogramSet()
+    hset[_key("data", "abcd_anti_iso")] = _hist([5.0, 5.0])
+    hset[_key("data", "abcd_same_sign")] = _hist([0.0, 0.0])
+    hset[_key("data", "abcd_same_sign_anti_iso")] = _hist([1.0, 1.0])
+    run_estimates(hset, analysis, ["mt"])
+    assert hset[_key("QCD", "nominal")].values.tolist() == [0.0, 0.0]
+
+
+def test_clip_negative_bins_keeps_integral():
+    assert clip_negative_bins(_hist([-1.0, 5.0])).values.tolist() == [0.0, 4.0]
+    assert clip_negative_bins(_hist([-3.0, 1.0])).values.tolist() == [0.0, 0.0]
+
+
+def test_estimate_skips_categories_without_data_input():
+    hset = HistogramSet()
+    hset[_key("ZTT", "anti_iso")] = _hist([1.0, 2.0])
+    assert run_estimates(hset, build(), ["mt"]) == []
diff --git a/tests/test_estimators.py b/tests/test_estimators.py
deleted file mode 100644
index 6dda23e..0000000
--- a/tests/test_estimators.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import dataclasses
-
-from shapesmith.estimators import run_estimate
-from shapesmith.estimators.abcd import clip_negative_bins
-from shapesmith.histogram import Histogram
-from shapesmith.histograms import INCLUSIVE, HistKey, HistogramSet
-from shapesmith.model import Estimator
-from tests.mini_analysis import build
-
-
-def _hist(values, variances=None):
-    return Histogram([0.0, 1.0, 2.0], values, variances if variances is not None else values)
-
-
-def _key(process, region, category=INCLUSIVE, variation="Nominal"):
-    return HistKey("mt", category, process, region, variation, "x")
-
-
-def test_fake_factor_estimate_subtracts_available_processes():
-    analysis = build()
-    hset = HistogramSet()
-    hset.add(_key("data", "anti_iso"), _hist([10.0, 20.0]))
-    hset.add(_key("ZTT", "anti_iso"), _hist([1.0, 2.0]))
-    # ZL missing on purpose -> skipped with a warning
-    added = run_estimate(hset, analysis, ["mt"])
-    assert added == [_key("jetFakes", "nominal")]
-    out = hset.get(_key("jetFakes", "nominal"))
-    assert out.values.tolist() == [9.0, 18.0] and out.variances.tolist() == [11.0, 22.0]
-
-
-def test_abcd_estimate():
-    analysis = dataclasses.replace(
-        build(),
-        estimator=Estimator("abcd", regions={"B": "abcd_anti_iso", "C": "abcd_same_sign", "D": "abcd_same_sign_anti_iso"}, subtract=("ZTT",), output="QCD"),
-    )
-    hset = HistogramSet()
-    for region, data, mc in (("abcd_anti_iso", (8.0, 8.0), (2.0, 2.0)), ("abcd_same_sign", (6.0, 6.0), (2.0, 2.0)), ("abcd_same_sign_anti_iso", (4.0, 4.0), (2.0, 2.0))):
-        hset.add(_key("data", region), _hist(list(data)))
-        hset.add(_key("ZTT", region), _hist(list(mc)))
-    run_estimate(hset, analysis, ["mt"])
-    assert hset.get(_key("QCD", "nominal")).values.tolist() == [12.0, 12.0]  # B=(6,6), C=8, D=4 -> factor 2
-
-
-def test_abcd_factor_zero_when_control_regions_empty():
-    analysis = dataclasses.replace(
-        build(),
-        estimator=Estimator("abcd", regions={"B": "abcd_anti_iso", "C": "abcd_same_sign", "D": "abcd_same_sign_anti_iso"}, subtract=(), output="QCD"),
-    )
-    hset = HistogramSet()
-    hset.add(_key("data", "abcd_anti_iso"), _hist([5.0, 5.0]))
-    hset.add(_key("data", "abcd_same_sign"), _hist([0.0, 0.0]))
-    hset.add(_key("data", "abcd_same_sign_anti_iso"), _hist([1.0, 1.0]))
-    run_estimate(hset, analysis, ["mt"])
-    assert hset.get(_key("QCD", "nominal")).values.tolist() == [0.0, 0.0]
-
-
-def test_clip_negative_bins_keeps_integral():
-    assert clip_negative_bins(_hist([-1.0, 5.0])).values.tolist() == [0.0, 4.0]
-    assert clip_negative_bins(_hist([-3.0, 1.0])).values.tolist() == [0.0, 0.0]
-
-
-def test_estimate_skips_categories_without_data_input():
-    hset = HistogramSet()
-    hset.add(_key("ZTT", "anti_iso"), _hist([1.0, 2.0]))
-    assert run_estimate(hset, build(), ["mt"]) == []
diff --git a/tests/test_events.py b/tests/test_events.py
new file mode 100644
index 0000000..63d01b5
--- /dev/null
+++ b/tests/test_events.py
@@ -0,0 +1,55 @@
+import dataclasses
+
+import numpy as np
+import pytest
+
+from shapesmith.events import Query, load, select
+from shapesmith.model import ColumnVariation, Process, Region, Selection, WeightVariation
+from shapesmith.store import read_skims
+from tests.mini_analysis import build
+
+CHANNEL = build().channel("mt")
+ZTT = CHANNEL.process("ZTT")
+SKIM = ["veto < 0.5", "id_loose > 0.5"]
+
+
+def test_nominal_selection_has_channel_process_and_skim_cuts_and_the_process_weights():
+    cuts, weights = select(CHANNEL, ZTT, CHANNEL.region("nominal"))
+    assert cuts == ["veto < 0.5", "(q_1 * q_2) < 0", "id_tight > 0.5", "gen_match == 5", *SKIM]
+    assert weights == ["trg_wgt", "puweight"]
+
+
+def test_region_replaces_cuts_and_weights_in_place_and_appends_its_weights():
+    region = Region("r", replace_cuts={"os": "(q_1 * q_2) > 0"}, add_weights={"ff": "fake_factor"}, replace_weights={"trg": "1.0"})
+    cuts, weights = select(CHANNEL, ZTT, region)
+    assert cuts[1] == "(q_1 * q_2) > 0" and weights == ["1.0", "puweight", "fake_factor"]
+    data_cuts, data_weights = select(CHANNEL, CHANNEL.process("data"), region)
+    assert data_weights == ["fake_factor"]  # data carries no trg weight: nothing to replace
+
+
+def test_weight_variation_applies_only_to_processes_with_its_weights():
+    up = WeightVariation("CMS_puUp", {"pu": "puweight_up"})
+    assert select(CHANNEL, ZTT, CHANNEL.region("nominal"), up)[1] == ["trg_wgt", "puweight_up"]
+    assert select(CHANNEL, CHANNEL.process("data"), CHANNEL.region("nominal"), up) is None
+
+
+def test_column_variation_rewrites_every_cut_and_weight():
+    channel = dataclasses.replace(CHANNEL, skim={"iso_loose": "id_loose > 0.5"})
+    process = Process("X", "DY", "background", "Z", Selection(cuts={"gen": "gen_match == 5"}, weights={"w": "trg_wgt"}))
+    variation = ColumnVariation("tesUp", "__tesUp")
+    available = {"id_loose__tesUp", "gen_match__tesUp", "trg_wgt__tesUp", "q_1__tesUp"}
+    cuts, weights = select(channel, process, channel.region("nominal"), variation, available)
+    assert cuts == ["veto < 0.5", "(q_1__tesUp * q_2) < 0", "id_tight > 0.5", "gen_match__tesUp == 5", "id_loose__tesUp > 0.5"]
+    assert weights == ["trg_wgt__tesUp"]
+
+
+def test_load_returns_selected_events_with_weights(mini_run):
+    config, analysis = mini_run
+    events = load(config, analysis, Query("mt", "ZTT"), ["m_vis", "event"])
+    frame = read_skims(config.skim_dir, "mt", ["ZTT_1"], None)
+    selected = frame[(frame["veto"] < 0.5) & ((frame["q_1"] * frame["q_2"]) < 0) & (frame["id_tight"] > 0.5) & (frame["gen_match"] == 5)]
+    assert events.frame["event"].tolist() == selected["event"].tolist() and "m_vis" in events.frame
+    expected = selected["norm_weight"].to_numpy() * analysis.lumi_pb * selected["trg_wgt"].to_numpy().astype(np.float64) * selected["puweight"].to_numpy().astype(np.float64)
+    assert np.allclose(events.weights, expected)
+    with pytest.raises(ValueError, match="does not apply"):
+        load(config, analysis, Query("mt", "data", variation=WeightVariation("CMS_puUp", {"pu": "x"})))
diff --git a/tests/test_expressions.py b/tests/test_expressions.py
index fe25d55..a716815 100644
--- a/tests/test_expressions.py
+++ b/tests/test_expressions.py
@@ -3,7 +3,7 @@
 import pytest
 
 from shapesmith import expressions as ex
-from shapesmith.model import Region, Selection, WeightVariation
+from shapesmith.model import ColumnVariation
 
 
 @pytest.fixture
@@ -19,10 +19,10 @@ def test_columns_in_ignores_functions_keywords_and_numbers():
 def test_evaluate_mask_weight(frame):
     assert ex.evaluate(frame, "pt_1 > 20").tolist() == [False, True, True]
     assert ex.evaluate(frame, "1.0").tolist() == [1.0, 1.0, 1.0]
-    assert ex.mask(frame, {"a": "pt_1 > 20", "b": "(q_1 * q_2) < 0"}).tolist() == [False, True, False]
-    assert ex.mask(frame, {}).tolist() == [True, True, True]
-    assert ex.weight(frame, {"w": "w", "two": "2.0"}).tolist() == [1.0, 4.0, 2.0]
-    assert ex.weight(frame, {}).dtype == np.float64 and ex.weight(frame, {}).tolist() == [1.0, 1.0, 1.0]
+    assert ex.mask(frame, ["pt_1 > 20", "(q_1 * q_2) < 0"]).tolist() == [False, True, False]
+    assert ex.mask(frame, []).tolist() == [True, True, True]
+    assert ex.product(frame, ["w", "2.0"]).tolist() == [1.0, 4.0, 2.0]
+    assert ex.product(frame, []).dtype == np.float64 and ex.product(frame, []).tolist() == [1.0, 1.0, 1.0]
 
 
 def test_bool_product_falls_back_to_the_python_engine(frame):
@@ -34,24 +34,17 @@ def test_missing_column_is_reported(frame):
         ex.evaluate(frame, "nope > 1")
 
 
-def test_apply_region_and_variation():
-    base = Selection(cuts={"os": "(q_1 * q_2) < 0", "iso": "id > 0.5"}, weights={"pu": "puweight"})
-    ss = ex.apply_region(base, Region("same_sign", replace_cuts={"os": "(q_1 * q_2) > 0"}, add_weights={"ff": "fake_factor"}))
-    assert ss.cuts["os"] == "(q_1 * q_2) > 0" and ss.cuts["iso"] == "id > 0.5"
-    assert list(ss.weights) == ["pu", "ff"] and ss.weights["ff"] == "fake_factor"
-    up = ex.apply_weight_variation(base, WeightVariation("puUp", {"pu": "puweight_up"}))
-    assert up.weights["pu"] == "puweight_up" and up.cuts == base.cuts
-    with pytest.raises(KeyError):
-        ex.apply_region(base, Region("bad", replace_cuts={"nope": "1"}))
-    with pytest.raises(KeyError):
-        ex.apply_weight_variation(base, WeightVariation("bad", {"nope": "1"}))
+def test_shift_renames_only_columns_with_a_shifted_branch():
+    available = {"pt_1", "pt_1__jesUp", "met"}
+    jes = ColumnVariation("jesUp", "__jesUp")
+    assert ex.shift("(pt_1 > 25) & (met > 30) & (pt_10 > 1)", jes, available) == "(pt_1__jesUp > 25) & (met > 30) & (pt_10 > 1)"
+    assert ex.shift("abs(pt_1) > 1", jes, available) == "abs(pt_1__jesUp) > 1"
 
 
-def test_apply_column_variation_only_renames_available_shifted_columns():
-    available = {"pt_1", "pt_1__jesUp", "met"}
-    assert ex.apply_column_variation("(pt_1 > 25) & (met > 30) & (pt_10 > 1)", "__jesUp", available) == "(pt_1__jesUp > 25) & (met > 30) & (pt_10 > 1)"
+def test_shift_replaces_derived_columns_by_their_expression():
+    grid = ColumnVariation("emb1p002", derived={"pt_2": "pt_2 * 1.002", "m_vis": "m_vis * sqrt(1.002)"})
+    assert ex.shift("(pt_2 > 20) & (m_vis < 100) & (pt_1 > 25)", grid, set()) == "((pt_2 * 1.002) > 20) & ((m_vis * sqrt(1.002)) < 100) & (pt_1 > 25)"
 
 
-def test_selection_columns():
-    sel = Selection(cuts={"a": "pt_1 > 25"}, weights={"w": "puweight * trg_wgt"})
-    assert ex.selection_columns(sel) == {"pt_1", "puweight", "trg_wgt"}
+def test_columns_of():
+    assert ex.columns_of(["pt_1 > 25", "puweight * trg_wgt"]) == {"pt_1", "puweight", "trg_wgt"}
diff --git a/tests/test_fill.py b/tests/test_fill.py
new file mode 100644
index 0000000..9bcdb39
--- /dev/null
+++ b/tests/test_fill.py
@@ -0,0 +1,151 @@
+import dataclasses
+
+import numpy as np
+import pytest
+
+from shapesmith.fill import bookings, resolve_regions, run_hist, targets
+from shapesmith.histogram import INCLUSIVE, HistKey, HistogramSet
+from shapesmith.model import Region
+from shapesmith.store import read_skims
+
+
+def test_histkey_roundtrip():
+    key = HistKey("mt", "sig", "ZTT", "nominal", "CMS_puUp", "score")
+    assert key.directory == "mt_sig" and key.object_name == "ZTT#nominal#CMS_puUp#score"
+    assert HistKey.parse(key.path) == key
+    assert HistKey.parse("mt_inclusive/data#anti_iso#Nominal#m_vis") == HistKey("mt", INCLUSIVE, "data", "anti_iso", "Nominal", "m_vis")
+
+
+def test_bookings_follow_process_roles_and_sample_kinds(mini_run):
+    _, analysis = mini_run
+    by_process = {b.process.name: b for b in bookings(analysis.channel("mt"), systematics=True)}
+    assert by_process["data"].regions == ("nominal", "anti_iso") and by_process["data"].variations == ()
+    assert by_process["HH"].regions == ("nominal",) and [v.name for v in by_process["HH"].variations] == ["CMS_puUp", "CMS_puDown"]
+    assert by_process["ZTT"].regions == ("nominal", "anti_iso") and len(by_process["ZTT"].variations) == 2
+    assert all(b.variations == () for b in bookings(analysis.channel("mt"), systematics=False))
+
+
+def test_explicit_regions_include_signal_and_all_expands_per_channel(mini_run):
+    _, analysis = mini_run
+    channel = analysis.channel("mt")
+    other = dataclasses.replace(channel, name="et", regions=channel.regions[:1])
+
+    assert resolve_regions(channel, ["all"]) == ("nominal", "same_sign", "anti_iso")
+    assert resolve_regions(other, ["all"]) == ("nominal", "same_sign")
+    assert {b.process.name: b.regions for b in bookings(channel, True, ["same_sign"])}["HH"] == ("same_sign",)
+    with pytest.raises(KeyError, match="channel mt: unknown region 'missing'"):
+        bookings(channel, False, ["missing"])
+
+
+def test_targets(mini_run):
+    channel = mini_run[1].channel("mt")
+    assert [(t.category, t.cut, t.variable.name) for t in targets(channel, control=False, variables=None)] == [("sig", "cls == 0", "score"), ("bkg", "cls == 1", "score")]
+    assert [(t.category, t.cut, t.variable.name) for t in targets(channel, control=True, variables=None)] == [(INCLUSIVE, None, "m_vis")]
+    with pytest.raises(KeyError):
+        targets(channel, control=True, variables=["nope"])
+
+
+def test_run_hist_control_matches_numpy(mini_run):
+    config, analysis = mini_run
+    hset = run_hist(config, analysis, ["mt"], control=True, variables=None, systematics=True, processes=None, output=config.output_dir / "control_shapes.root")
+    frame = read_skims(config.skim_dir, "mt", ["ZTT_1"], None)
+    sel = (frame["veto"] < 0.5) & ((frame["q_1"] * frame["q_2"]) < 0) & (frame["id_tight"] > 0.5) & (frame["gen_match"] == 5)
+    weight = frame["norm_weight"] * analysis.lumi_pb * frame["trg_wgt"] * frame["puweight"]
+    expected, _ = np.histogram(frame.loc[sel, "m_vis"], bins=[0, 50, 100, 200], weights=weight[sel])
+    h = hset[HistKey("mt", INCLUSIVE, "ZTT", "nominal", "Nominal", "m_vis")]
+    assert np.allclose(h.values, expected) and h.edges.tolist() == [0.0, 50.0, 100.0, 200.0]
+    data = read_skims(config.skim_dir, "mt", ["DATA_A"], None)
+    dsel = (data["veto"] < 0.5) & ((data["q_1"] * data["q_2"]) < 0) & (data["id_tight"] > 0.5)
+    assert hset[HistKey("mt", INCLUSIVE, "data", "nominal", "Nominal", "m_vis")].sum() == pytest.approx(dsel.sum())
+    anti = (data["veto"] < 0.5) & ((data["q_1"] * data["q_2"]) < 0) & (data["id_tight"] < 0.5) & (data["id_loose"] > 0.5)
+    assert hset[HistKey("mt", INCLUSIVE, "data", "anti_iso", "Nominal", "m_vis")].sum() == pytest.approx(data.loc[anti, "fake_factor"].sum())
+    assert not hset.select(process="data", variation="CMS_puUp")
+    up = hset[HistKey("mt", INCLUSIVE, "ZTT", "nominal", "CMS_puUp", "m_vis")]
+    assert np.allclose(up.values, expected * 1.05)
+    assert (config.output_dir / "control_shapes.root").exists() and (config.output_dir / "control_shapes.json").exists()
+    reloaded = HistogramSet.load(config.output_dir / "control_shapes.root")
+    assert len(reloaded) == len(hset)
+    again = reloaded[HistKey("mt", INCLUSIVE, "ZTT", "nominal", "Nominal", "m_vis")]
+    assert np.allclose(again.values, expected) and np.allclose(again.variances, h.variances) and again.edges.tolist() == [0.0, 50.0, 100.0, 200.0]
+
+
+def test_run_hist_explicit_region_uses_event_selection_for_every_process(mini_run):
+    config, analysis = mini_run
+    hset = run_hist(
+        config,
+        analysis,
+        ["mt"],
+        control=True,
+        variables=None,
+        systematics=False,
+        processes=None,
+        output=config.output_dir / "same_sign.root",
+        regions=["same_sign"],
+    )
+
+    data = read_skims(config.skim_dir, "mt", ["DATA_A"], None)
+    same_sign = (data["veto"] < 0.5) & ((data["q_1"] * data["q_2"]) > 0) & (data["id_tight"] > 0.5)
+    assert hset[HistKey("mt", INCLUSIVE, "data", "same_sign", "Nominal", "m_vis")].sum() == pytest.approx(same_sign.sum())
+    assert HistKey("mt", INCLUSIVE, "HH", "same_sign", "Nominal", "m_vis") in hset
+    assert {key.region for key in hset} == {"same_sign"}
+
+
+def test_region_weight_replacement_applies_where_the_process_carries_the_weight(mini_run):
+    config, analysis = mini_run
+    channel = analysis.channel("mt")
+    regions = tuple(dataclasses.replace(r, replace_weights={"pu": "puweight_up"}) if r.name == "same_sign" else r for r in channel.regions)
+    analysis = dataclasses.replace(analysis, channels={"mt": dataclasses.replace(channel, regions=regions)})
+    hset = run_hist(config, analysis, ["mt"], True, None, False, ["data", "ZTT"], config.output_dir / "weight_replacement.root", regions=["same_sign"])
+
+    data = read_skims(config.skim_dir, "mt", ["DATA_A"], None)
+    selected_data = (data["veto"] < 0.5) & ((data["q_1"] * data["q_2"]) > 0) & (data["id_tight"] > 0.5)
+    assert hset[HistKey("mt", INCLUSIVE, "data", "same_sign", "Nominal", "m_vis")].sum() == pytest.approx(selected_data.sum())
+    mc = read_skims(config.skim_dir, "mt", ["ZTT_1"], None)
+    selected = mc[(mc["veto"] < 0.5) & ((mc["q_1"] * mc["q_2"]) > 0) & (mc["id_tight"] > 0.5) & (mc["gen_match"] == 5)]
+    expected = selected["norm_weight"] * analysis.lumi_pb * selected["trg_wgt"] * selected["puweight_up"]
+    assert hset[HistKey("mt", INCLUSIVE, "ZTT", "same_sign", "Nominal", "m_vis")].sum() == pytest.approx(expected.sum())
+
+
+def test_run_hist_keeps_other_processes(mini_run):
+    config, analysis = mini_run
+    output = config.output_dir / "partial.root"
+    first = run_hist(config, analysis, ["mt"], control=True, variables=None, systematics=False, processes=None, output=output)
+    again = run_hist(config, analysis, ["mt"], control=True, variables=None, systematics=False, processes=["HH"], output=output)
+    assert len(again) == len(first) and set(again) == set(first)
+
+
+def test_run_hist_always_refills_the_requested_scopes(mini_run):
+    config, analysis = mini_run
+    output = config.output_dir / "refill.root"
+    first = run_hist(config, analysis, ["mt"], True, None, False, ["HH"], output)
+    channel = analysis.channel("mt")
+    doubled = tuple(dataclasses.replace(p, selection=dataclasses.replace(p.selection, weights={**p.selection.weights, "two": "2.0"})) if p.name == "HH" else p for p in channel.processes)
+    analysis = dataclasses.replace(analysis, channels={"mt": dataclasses.replace(channel, processes=doubled)})
+    again = run_hist(config, analysis, ["mt"], True, None, False, ["HH"], output)
+    key = HistKey("mt", INCLUSIVE, "HH", "nominal", "Nominal", "m_vis")
+    assert np.allclose(again[key].values, 2 * first[key].values)
+
+
+def test_run_hist_keeps_other_variables_and_regions(mini_run):
+    config, analysis = mini_run
+    channel = analysis.channel("mt")
+    channel = dataclasses.replace(channel, variables={**channel.variables, "score": channel.categories[0].variable})
+    analysis = dataclasses.replace(analysis, channels={"mt": channel})
+    output = config.output_dir / "partial_regions.root"
+    first = run_hist(config, analysis, ["mt"], True, None, False, None, output, regions=["all"])
+    kept_key = HistKey("mt", INCLUSIVE, "HH", "nominal", "Nominal", "score")
+    kept_values = first[kept_key].values.copy()
+
+    again = run_hist(config, analysis, ["mt"], True, ["m_vis"], False, ["HH"], output, regions=["same_sign"])
+
+    assert set(again) == set(first)
+    assert np.array_equal(again[kept_key].values, kept_values)
+
+
+def test_run_hist_categories(mini_run):
+    config, analysis = mini_run
+    hset = run_hist(config, analysis, ["mt"], control=False, variables=None, systematics=False, processes=["HH", "data"], output=config.output_dir / "shapes.root")
+    assert {k.category for k in hset} == {"sig", "bkg"}
+    assert {k.process for k in hset} == {"HH", "data"}
+    assert hset.select(process="HH", region="anti_iso") == []
+    assert hset[HistKey("mt", "sig", "HH", "nominal", "Nominal", "score")].edges.tolist() == [0.0, 0.5, 1.0]
diff --git a/tests/test_golden.py b/tests/test_golden.py
index bc691ce..b48b4b5 100644
--- a/tests/test_golden.py
+++ b/tests/test_golden.py
@@ -14,11 +14,12 @@
 
 from shapesmith.config import FriendConfig, NtupleConfig, RunConfig
 from shapesmith.datacards import run_datacards
-from shapesmith.estimators import run_estimate
-from shapesmith.histograms import run_hist
+from shapesmith.estimates import run_estimates
+from shapesmith.fill import run_hist
 from shapesmith.ml_export import run_ml_export
-from shapesmith.model import Estimator, Region
+from shapesmith.model import ABCD, Region
 from shapesmith.skim import run_skim
+from shapesmith.store import read_manifest, write_manifest
 from shapesmith.testing import make_mini_dataset
 from tests.mini_analysis import build
 
@@ -36,8 +37,8 @@ def _abcd_variant(analysis):
         Region("abcd_anti_iso", replace_cuts={"tau_iso": anti}),
         Region("abcd_same_sign_anti_iso", replace_cuts={"os": "(q_1 * q_2) > 0", "tau_iso": anti}),
     )
-    estimator = Estimator("abcd", {"B": "abcd_anti_iso", "C": "same_sign", "D": "abcd_same_sign_anti_iso"}, ("ZTT", "ZL"), "QCD")
-    return dataclasses.replace(analysis, channels={"mt": dataclasses.replace(channel, regions=regions)}, estimator=estimator)
+    estimators = (ABCD("QCD", "abcd_anti_iso", "same_sign", "abcd_same_sign_anti_iso", ("ZTT", "ZL")),)
+    return dataclasses.replace(analysis, channels={"mt": dataclasses.replace(channel, regions=regions, estimators=estimators)})
 
 
 def _produce(tmp: Path) -> tuple[dict, str]:
@@ -50,15 +51,15 @@ def _produce(tmp: Path) -> tuple[dict, str]:
     analysis = build(config)
     run_skim(config, analysis)
     result = {}
-    control = run_hist(config, analysis, ["mt"], True, None, True, None, tmp / "out" / "control_shapes.root", force=True)
+    control = run_hist(config, analysis, ["mt"], True, None, True, None, tmp / "out" / "control_shapes.root")
     result["control_hist"] = _digest(control)
-    run_estimate(control, analysis, ["mt"])
+    run_estimates(control, analysis, ["mt"])
     result["control_estimate"] = _digest(control)
-    regions = run_hist(config, analysis, ["mt"], True, None, True, None, tmp / "out" / "regions.root", force=True, regions=["all"])
+    regions = run_hist(config, analysis, ["mt"], True, None, True, None, tmp / "out" / "regions.root", regions=["all"])
     result["control_regions_all"] = _digest(regions)
-    shapes = run_hist(config, analysis, ["mt"], False, None, True, None, tmp / "out" / "shapes.root", force=True)
+    shapes = run_hist(config, analysis, ["mt"], False, None, True, None, tmp / "out" / "shapes.root")
     result["shapes_hist"] = _digest(shapes)
-    run_estimate(shapes, analysis, ["mt"])
+    run_estimates(shapes, analysis, ["mt"])
     result["shapes_estimate"] = _digest(shapes)
     card = run_datacards(shapes, analysis, {"mt": ["mt"]}, tmp / "out" / "datacards", True, 1.0)["mt"].read_text()
     with uproot.open(tmp / "out" / "datacards" / "mt" / "common" / "htt_input_2018.root") as f:
@@ -68,16 +69,29 @@ def _produce(tmp: Path) -> tuple[dict, str]:
             if cls.startswith("TH1")
         }
     abcd = _abcd_variant(analysis)
-    abcd_hset = run_hist(config, abcd, ["mt"], True, None, False, None, tmp / "out" / "abcd.root", force=True)
-    run_estimate(abcd_hset, abcd, ["mt"])
+    abcd_hset = run_hist(config, abcd, ["mt"], True, None, False, None, tmp / "out" / "abcd.root")
+    run_estimates(abcd_hset, abcd, ["mt"])
     result["abcd_control_estimate"] = _digest(abcd_hset)
     result["ml_export"] = {}
     for path in run_ml_export(config, analysis, ["mt"]):
         frame = pd.read_feather(path)
         result["ml_export"][path.name] = {"|".join(column): np.asarray(frame[column]).astype(np.float64).tolist() for column in frame.columns}
+    result["reused_old_contracts"] = _reuse_old_contracts(config, analysis)
     return result, card
 
 
+def _reuse_old_contracts(config, analysis) -> bool:
+    """Skims whose manifests carry the contracts written by aa5daa5 (no friends recorded) are reused without
+    --force when no friend is configured, as for the v4 skims; a configured friend makes them incompatible."""
+    for nick, contract in json.loads((GOLDEN / "mini_golden.json").read_text())["skim_contracts"].items():
+        path = config.skim_dir / "mt" / nick / "manifest.json"
+        write_manifest(path, {**read_manifest(path), "contract": contract})
+    with pytest.raises(ValueError, match="friends missing"):
+        run_skim(config, analysis)
+    without_friends = config.model_copy(update={"ntuples": config.ntuples.model_copy(update={"friends": []})})
+    return all(r.skipped for r in run_skim(without_friends, analysis))
+
+
 def _bitwise_equal(a, b) -> bool:
     return np.asarray(a, dtype=np.float64).tobytes() == np.asarray(b, dtype=np.float64).tobytes()
 
@@ -96,6 +110,10 @@ def test_histograms_match_the_golden_outputs(produced, part):
     assert not different
 
 
+def test_skims_with_the_old_contracts_are_reused(produced):
+    assert produced[0]["reused_old_contracts"]
+
+
 def test_datacard_text_matches_the_golden_card(produced):
     assert produced[1] == (GOLDEN / "datacard_mt.txt").read_text()
 
diff --git a/tests/test_histograms.py b/tests/test_histograms.py
deleted file mode 100644
index 240e4ff..0000000
--- a/tests/test_histograms.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import dataclasses
-
-import numpy as np
-import pytest
-
-from shapesmith.histograms import INCLUSIVE, HistKey, HistogramSet, bookings, resolve_regions, run_hist, targets
-from shapesmith.io.skims import read_skims
-from shapesmith.model import Region, Selection
-
-
-def test_histkey_roundtrip():
-    key = HistKey("mt", "sig", "ZTT", "nominal", "CMS_puUp", "score")
-    assert key.directory == "mt_sig" and key.object_name == "ZTT#nominal#CMS_puUp#score"
-    assert HistKey.parse(key.path) == key
-    assert HistKey.parse("mt_inclusive/data#anti_iso#Nominal#m_vis") == HistKey("mt", INCLUSIVE, "data", "anti_iso", "Nominal", "m_vis")
-
-
-def test_bookings_follow_process_kinds(mini_run):
-    _, analysis = mini_run
-    by_process = {b.process.name: b for b in bookings(analysis, "mt", systematics=True)}
-    assert by_process["data"].regions == ("nominal", "anti_iso") and by_process["data"].variations == ()
-    assert by_process["HH"].regions == ("nominal",) and [v.name for v in by_process["HH"].variations] == ["CMS_puUp", "CMS_puDown"]
-    assert by_process["ZTT"].regions == ("nominal", "anti_iso") and len(by_process["ZTT"].variations) == 2
-    assert all(b.variations == () for b in bookings(analysis, "mt", systematics=False))
-
-
-def test_explicit_regions_include_signal_and_all_expands_per_channel(mini_run):
-    _, analysis = mini_run
-    channel = analysis.channel("mt")
-    other = dataclasses.replace(channel, name="et", regions=channel.regions[:1])
-    analysis = dataclasses.replace(analysis, channels={"mt": channel, "et": other})
-
-    assert resolve_regions(analysis, "mt", ["all"]) == ("nominal", "same_sign", "anti_iso")
-    assert resolve_regions(analysis, "et", ["all"]) == ("nominal", "same_sign")
-    assert {b.process.name: b.regions for b in bookings(analysis, "mt", True, ["same_sign"])}["HH"] == ("same_sign",)
-    with pytest.raises(KeyError, match="channel mt: unknown region 'missing'"):
-        bookings(analysis, "mt", False, ["missing"])
-
-
-def test_targets(mini_run):
-    _, analysis = mini_run
-    assert [(t.category, t.cut, t.variable.name) for t in targets(analysis, control=False, variables=None)] == [("sig", "cls == 0", "score"), ("bkg", "cls == 1", "score")]
-    assert [(t.category, t.cut, t.variable.name) for t in targets(analysis, control=True, variables=None)] == [(INCLUSIVE, None, "m_vis")]
-    with pytest.raises(KeyError):
-        targets(analysis, control=True, variables=["nope"])
-
-
-def test_run_hist_control_matches_numpy(mini_run):
-    config, analysis = mini_run
-    hset = run_hist(config, analysis, ["mt"], control=True, variables=None, systematics=True, processes=None, output=config.output_dir / "control_shapes.root", force=True)
-    frame = read_skims(config.skim_dir, "mt", ["ZTT_1"], None)
-    sel = (frame["veto"] < 0.5) & ((frame["q_1"] * frame["q_2"]) < 0) & (frame["id_tight"] > 0.5) & (frame["gen_match"] == 5)
-    weight = frame["norm_weight"] * analysis.lumi_pb * frame["trg_wgt"] * frame["puweight"]
-    expected, _ = np.histogram(frame.loc[sel, "m_vis"], bins=[0, 50, 100, 200], weights=weight[sel])
-    h = hset.get(HistKey("mt", INCLUSIVE, "ZTT", "nominal", "Nominal", "m_vis"))
-    assert np.allclose(h.values, expected) and h.edges.tolist() == [0.0, 50.0, 100.0, 200.0]
-    data = read_skims(config.skim_dir, "mt", ["DATA_A"], None)
-    dsel = (data["veto"] < 0.5) & ((data["q_1"] * data["q_2"]) < 0) & (data["id_tight"] > 0.5)
-    assert hset.get(HistKey("mt", INCLUSIVE, "data", "nominal", "Nominal", "m_vis")).sum() == pytest.approx(dsel.sum())
-    anti = (data["veto"] < 0.5) & ((data["q_1"] * data["q_2"]) < 0) & (data["id_tight"] < 0.5) & (data["id_loose"] > 0.5)
-    assert hset.get(HistKey("mt", INCLUSIVE, "data", "anti_iso", "Nominal", "m_vis")).sum() == pytest.approx(data.loc[anti, "fake_factor"].sum())
-    assert not hset.keys(process="data", variation="CMS_puUp")
-    up = hset.get(HistKey("mt", INCLUSIVE, "ZTT", "nominal", "CMS_puUp", "m_vis"))
-    assert np.allclose(up.values, expected * 1.05)
-    assert (config.output_dir / "control_shapes.root").exists() and (config.output_dir / "control_shapes.json").exists()
-    reloaded = HistogramSet.load(config.output_dir / "control_shapes.root")
-    assert len(reloaded) == len(hset)
-    again = reloaded.get(HistKey("mt", INCLUSIVE, "ZTT", "nominal", "Nominal", "m_vis"))
-    assert np.allclose(again.values, expected) and np.allclose(again.variances, h.variances) and again.edges.tolist() == [0.0, 50.0, 100.0, 200.0]
-
-
-def test_run_hist_explicit_region_uses_event_selection_for_every_process(mini_run):
-    config, analysis = mini_run
-    hset = run_hist(
-        config,
-        analysis,
-        ["mt"],
-        control=True,
-        variables=None,
-        systematics=False,
-        processes=None,
-        output=config.output_dir / "same_sign.root",
-        force=True,
-        regions=["same_sign"],
-    )
-
-    data = read_skims(config.skim_dir, "mt", ["DATA_A"], None)
-    same_sign = (data["veto"] < 0.5) & ((data["q_1"] * data["q_2"]) > 0) & (data["id_tight"] > 0.5)
-    assert hset.get(HistKey("mt", INCLUSIVE, "data", "same_sign", "Nominal", "m_vis")).sum() == pytest.approx(same_sign.sum())
-    assert hset.has(HistKey("mt", INCLUSIVE, "HH", "same_sign", "Nominal", "m_vis"))
-    assert {key.region for key in hset.keys()} == {"same_sign"}
-
-
-def test_region_replacement_of_baseline_weight_is_mc_only(mini_run):
-    config, analysis = mini_run
-    channel = analysis.channel("mt")
-    baseline = Selection(channel.baseline.cuts, {**channel.baseline.weights, "mc_only": "puweight"})
-    regions = tuple(
-        Region(region.name, region.replace_cuts, {**region.add_weights, "mc_only": "puweight_up"})
-        if region.name == "same_sign"
-        else region
-        for region in channel.regions
-    )
-    analysis = dataclasses.replace(analysis, channels={"mt": dataclasses.replace(channel, baseline=baseline, regions=regions)})
-
-    hset = run_hist(
-        config,
-        analysis,
-        ["mt"],
-        True,
-        None,
-        False,
-        ["data", "ztt"],
-        config.output_dir / "weight_replacement.root",
-        force=True,
-        regions=["same_sign"],
-    )
-
-    data = read_skims(config.skim_dir, "mt", ["DATA_A"], None)
-    selected_data = (data["veto"] < 0.5) & ((data["q_1"] * data["q_2"]) > 0) & (data["id_tight"] > 0.5)
-    assert hset.get(HistKey("mt", INCLUSIVE, "data", "same_sign", "Nominal", "m_vis")).sum() == pytest.approx(selected_data.sum())
-
-    mc = read_skims(config.skim_dir, "mt", ["ZTT_1"], None)
-    selected_mc = (mc["veto"] < 0.5) & ((mc["q_1"] * mc["q_2"]) > 0) & (mc["id_tight"] > 0.5) & (mc["gen_match"] == 5)
-    expected = mc.loc[selected_mc, "norm_weight"] * analysis.lumi_pb * mc.loc[selected_mc, "trg_wgt"] * mc.loc[selected_mc, "puweight"] * mc.loc[selected_mc, "puweight_up"]
-    assert hset.get(HistKey("mt", INCLUSIVE, "ZTT", "same_sign", "Nominal", "m_vis")).sum() == pytest.approx(expected.sum())
-
-
-def test_run_hist_force_keeps_other_processes(mini_run):
-    config, analysis = mini_run
-    output = config.output_dir / "partial.root"
-    first = run_hist(config, analysis, ["mt"], control=True, variables=None, systematics=False, processes=None, output=output, force=True)
-    again = run_hist(config, analysis, ["mt"], control=True, variables=None, systematics=False, processes=["hh"], output=output, force=True)
-    assert len(again) == len(first) and set(again.keys()) == set(first.keys())
-
-
-def test_run_hist_partial_force_keeps_other_variables_and_regions(mini_run):
-    config, analysis = mini_run
-    analysis = dataclasses.replace(analysis, control_variables={**analysis.control_variables, "score": analysis.categories[0].variable})
-    output = config.output_dir / "partial_regions.root"
-    first = run_hist(config, analysis, ["mt"], True, None, False, None, output, force=True, regions=["all"])
-    kept_key = HistKey("mt", INCLUSIVE, "HH", "nominal", "Nominal", "score")
-    kept_values = first.get(kept_key).values.copy()
-
-    again = run_hist(config, analysis, ["mt"], True, ["m_vis"], False, ["hh"], output, force=True, regions=["same_sign"])
-
-    assert set(again.keys()) == set(first.keys())
-    assert np.array_equal(again.get(kept_key).values, kept_values)
-
-
-def test_run_hist_categories(mini_run):
-    config, analysis = mini_run
-    hset = run_hist(config, analysis, ["mt"], control=False, variables=None, systematics=False, processes=["hh", "data"], output=config.output_dir / "shapes.root", force=True)
-    assert {k.category for k in hset.keys()} == {"sig", "bkg"}
-    assert {k.process for k in hset.keys()} == {"HH", "data"}
-    assert hset.keys(process="HH", region="anti_iso") == []
-    assert hset.get(HistKey("mt", "sig", "HH", "nominal", "Nominal", "score")).edges.tolist() == [0.0, 0.5, 1.0]
diff --git a/tests/test_fit.py b/tests/test_limits.py
similarity index 66%
rename from tests/test_fit.py
rename to tests/test_limits.py
index b51b917..1d23698 100644
--- a/tests/test_fit.py
+++ b/tests/test_limits.py
@@ -1,7 +1,12 @@
+from pathlib import Path
+
 import numpy as np
+import pytest
 import uproot
 
-from shapesmith.fit import collect, combine_script, write_summary
+from shapesmith import cmssw
+from shapesmith.config import CombineConfig
+from shapesmith.limits import collect, limit_commands, write_summary
 
 
 def _limit_tree(path, quantiles, limits, r=None):
@@ -12,10 +17,21 @@ def _limit_tree(path, quantiles, limits, r=None):
         f["limit"] = data
 
 
-def test_combine_script_contents():
-    script = combine_script("/cmssw/CMSSW_14_1_9", "el9_amd64_gcc12", __import__("pathlib").Path("/cards/mt"))
-    assert "export SCRAM_ARCH=el9_amd64_gcc12" in script
-    assert "cd /cmssw/CMSSW_14_1_9/src" in script and "eval $(scramv1 runtime -sh)" in script
+def test_cmssw_script_sets_up_the_environment_before_the_commands():
+    script = cmssw.script(CombineConfig(cmssw_dir="/cmssw/CMSSW_14_1_9"), Path("/cards/mt"), ["echo one", "echo two"])
+    lines = script.splitlines()
+    assert lines[:2] == ["set -e", "export SCRAM_ARCH=el9_amd64_gcc12"]
+    assert "cd /cmssw/CMSSW_14_1_9/src" in lines and "eval $(scramv1 runtime -sh)" in lines
+    assert lines[-3:] == ["cd /cards/mt", "echo one", "echo two"]
+
+
+def test_cmssw_run_needs_a_combine_configuration(tmp_path):
+    with pytest.raises(ValueError, match="needs `combine`"):
+        cmssw.run(["true"], None, tmp_path)
+
+
+def test_limit_commands():
+    script = "\n".join(limit_commands())
     assert "text2workspace.py combined.txt -o workspace.root -m 125" in script
     assert "-M AsymptoticLimits" in script and "--expectSignal 0" in script and "-n .Limit" in script
     assert "-M Significance" in script and "-n .Significance" in script
diff --git a/tests/test_model.py b/tests/test_model.py
deleted file mode 100644
index c07a5cf..0000000
--- a/tests/test_model.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import dataclasses
-
-import pytest
-
-from shapesmith import model
-from tests.mini_analysis import build
-
-
-def test_mini_analysis_validates():
-    analysis = build()
-    analysis.validate()
-    assert analysis.process("ZTT").kind == "true_tau"
-    assert [p.name for p in analysis.processes_of_kind("data")] == ["data"]
-    assert analysis.backgrounds() == ("ZTT", "ZL", "jetFakes")
-    assert [s.nick for s in analysis.samples_for("DY", "mt")] == ["ZTT_1"]
-    assert [s.nick for s in analysis.samples_for("data", "mt")] == ["DATA_A"]
-    assert analysis.samples_for("data", "et") == ()
-
-
-def test_sample_norm_weight():
-    mc = model.Sample("X", "G", "mc", xsec=10.0, nevents=100, generator_weight=0.5)
-    assert mc.norm_weight == pytest.approx(10.0 / (100 * 0.5))
-    assert model.Sample("D", "data", "data").norm_weight == 1.0
-
-
-def test_validate_reports_all_problems():
-    analysis = build()
-    broken = dataclasses.replace(
-        analysis,
-        processes=analysis.processes + (model.Process("x", "NOGROUP", "ZTT", "other", "Z"),),
-        estimator=dataclasses.replace(analysis.estimator, regions={"anti_iso": "does_not_exist"}),
-    )
-    with pytest.raises(model.AnalysisError) as excinfo:
-        broken.validate()
-    message = str(excinfo.value)
-    assert "duplicate process name" in message and "ZTT" in message
-    assert "NOGROUP" in message
-    assert "does_not_exist" in message
-
-
-def test_validate_checks_region_and_variation_targets():
-    analysis = build()
-    channel = analysis.channels["mt"]
-    bad_region = dataclasses.replace(channel, regions=channel.regions + (model.Region("bad", {"no_such_cut": "1"}),))
-    with pytest.raises(model.AnalysisError, match="no_such_cut"):
-        dataclasses.replace(analysis, channels={"mt": bad_region}).validate()
-    with pytest.raises(model.AnalysisError, match="no_such_weight"):
-        dataclasses.replace(analysis, weight_variations=(model.WeightVariation("v", {"no_such_weight": "1"}),)).validate()
-
-
-def test_dataclasses_are_frozen():
-    with pytest.raises(dataclasses.FrozenInstanceError):
-        build().signal = "other"
-
-
-def test_process_selection_per_channel():
-    shared = model.Process("x", "G", "X", "other", "rare", model.Selection(cuts={"a": "a > 1"}))
-    assert shared.selection_for("mt") is shared.selection_for("tt")
-    per_channel = model.Process("y", "G", "Y", "other", "rare", {"mt": model.Selection(cuts={"a": "a > 1"}), "tt": model.Selection(cuts={"a": "a > 2"})})
-    assert per_channel.selection_for("tt").cuts["a"] == "a > 2"
-    with pytest.raises(KeyError):
-        per_channel.selection_for("et")
diff --git a/tests/test_io.py b/tests/test_ntuples.py
similarity index 76%
rename from tests/test_io.py
rename to tests/test_ntuples.py
index cfb6b9d..5aed7e8 100644
--- a/tests/test_io.py
+++ b/tests/test_ntuples.py
@@ -2,8 +2,7 @@
 import pytest
 
 from shapesmith.config import FriendConfig, NtupleConfig
-from shapesmith.io.discovery import discover, join_url, list_root_files
-from shapesmith.io.ntuples import MissingColumnsError, available_columns, num_entries, read_columns, read_metadata
+from shapesmith.ntuples import MissingColumnsError, NtupleFile, discover, friend_bases, join_url, list_root_files, read_ntuple
 from shapesmith.testing import make_mini_dataset, make_ntuple
 
 
@@ -43,32 +42,35 @@ def test_missing_friend_is_an_error(dataset):
         discover(_config(root), "2018", "ZTT_1", "mt", "mc")
 
 
-def test_read_columns_joins_friends(dataset):
+def test_friend_bases_follow_applies_to(dataset):
+    _, root = dataset
+    assert friend_bases(_config(root, applies_to=("mc",)), "mc") == [str(root / "CROWNFriends" / "nn")]
+    assert friend_bases(_config(root, applies_to=("mc",)), "data") == []
+
+
+def test_read_ntuple_joins_friends(dataset):
     info, root = dataset
     ntuple = discover(_config(root), "2018", "ZTT_1", "mt", "mc")[0]
-    columns = available_columns(ntuple)
-    assert columns["m_vis"].endswith("CROWNRun/2018/ZTT_1/mt/ZTT_1_0.root") and columns["score"].endswith("CROWNFriends/nn/2018/ZTT_1/mt/ZTT_1_0.root")
-    frame = read_columns(ntuple, {"m_vis", "score", "event"})
+    frame, metadata, branches = read_ntuple(ntuple, {"m_vis", "score", "event"}, {"not_there"})
     assert list(sorted(frame.columns)) == ["event", "m_vis", "score"] and len(frame) == 50
     assert frame["m_vis"].dtype == np.float32
-    assert num_entries(ntuple.path) == 50
-    assert read_metadata(ntuple.path)["sample_type"] == "mc"
+    assert metadata["sample_type"] == "mc"
+    assert {"m_vis", "score", "fake_factor", "genWeight"} <= branches
 
 
 def test_missing_columns_are_listed(dataset):
     info, root = dataset
     ntuple = discover(_config(root), "2018", "ZTT_1", "mt", "mc")[0]
     with pytest.raises(MissingColumnsError) as excinfo:
-        read_columns(ntuple, {"m_vis", "nope", "also_nope"})
+        read_ntuple(ntuple, {"m_vis", "nope", "also_nope"})
     assert excinfo.value.missing == ["also_nope", "nope"]
 
 
 def test_friend_length_mismatch_is_an_error(tmp_path):
     main = make_ntuple(tmp_path / "main.root", {"a": np.zeros(3, dtype=np.float32)})
     friend = make_ntuple(tmp_path / "friend.root", {"b": np.zeros(2, dtype=np.float32)})
-    from shapesmith.io.discovery import NtupleFile
     with pytest.raises(ValueError, match="entries"):
-        read_columns(NtupleFile(str(main), (str(friend),), "X", "mt", "main.root"), {"a", "b"})
+        read_ntuple(NtupleFile(str(main), (str(friend),), "X", "mt", "main.root"), {"a", "b"})
 
 
 def test_missing_columns_error_is_picklable():
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index a429591..eade100 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -1,10 +1,9 @@
 import pytest
 
-from shapesmith.histogram import Histogram
-from shapesmith.histograms import INCLUSIVE, HistKey, HistogramSet
+from shapesmith.histogram import INCLUSIVE, HistKey, Histogram, HistogramSet
 from shapesmith.plotting.stack import _group_histogram, default_signal_scale, run_plot
 from shapesmith.plotting.style import axis_label, grouped_backgrounds
-from tests.mini_analysis import build
+from tests.mini_analysis import build, build_embedding
 
 
 def _hist(values, edges=(0.0, 50.0, 100.0, 200.0)):
@@ -13,23 +12,24 @@ def _hist(values, edges=(0.0, 50.0, 100.0, 200.0)):
 
 def test_grouped_backgrounds_and_axis_label():
     analysis = build()
-    assert grouped_backgrounds(analysis) == [("jetFakes", ["jetFakes"]), ("Z", ["ZTT", "ZL"])]
+    assert grouped_backgrounds(analysis, "mt") == [("jetFakes", ["jetFakes"]), ("Z", ["ZTT", "ZL"])]
+    assert grouped_backgrounds(build_embedding(), "mt") == [("jetFakes", ["jetFakes"]), ("Z", ["ZL"]), ("EMB", ["EMB"])]  # no auxiliary ZTT
     assert axis_label(analysis, "mt", "m_vis") == r"$m_{vis}$ / GeV"
     assert axis_label(analysis, "mt", "unknown_var") == "unknown_var"
 
 
 def test_default_signal_scale():
     hset = HistogramSet()
-    hset.add(HistKey("mt", INCLUSIVE, "ZTT", "nominal", "Nominal", "m_vis"), _hist([100, 50, 20]))
-    hset.add(HistKey("mt", INCLUSIVE, "HH", "nominal", "Nominal", "m_vis"), _hist([0.1, 0.2, 0.05]))
+    hset[HistKey("mt", INCLUSIVE, "ZTT", "nominal", "Nominal", "m_vis")] = _hist([100, 50, 20])
+    hset[HistKey("mt", INCLUSIVE, "HH", "nominal", "Nominal", "m_vis")] = _hist([0.1, 0.2, 0.05])
     assert default_signal_scale(hset, build(), "mt", INCLUSIVE, "m_vis") in (100.0, 200.0)
 
 
 def test_group_histogram_reads_only_the_selected_region():
     hset = HistogramSet()
-    hset.add(HistKey("mt", INCLUSIVE, "ZTT", "nominal", "Nominal", "m_vis"), _hist([100, 100, 100]))
-    hset.add(HistKey("mt", INCLUSIVE, "ZTT", "same_sign", "Nominal", "m_vis"), _hist([3, 2, 1]))
-    hset.add(HistKey("mt", INCLUSIVE, "jetFakes", "nominal", "Nominal", "m_vis"), _hist([50, 50, 50]))
+    hset[HistKey("mt", INCLUSIVE, "ZTT", "nominal", "Nominal", "m_vis")] = _hist([100, 100, 100])
+    hset[HistKey("mt", INCLUSIVE, "ZTT", "same_sign", "Nominal", "m_vis")] = _hist([3, 2, 1])
+    hset[HistKey("mt", INCLUSIVE, "jetFakes", "nominal", "Nominal", "m_vis")] = _hist([50, 50, 50])
 
     selected = _group_histogram(hset, "mt", INCLUSIVE, ["ZTT", "jetFakes"], "m_vis", region="same_sign")
 
@@ -40,7 +40,7 @@ def test_run_plot_writes_files(tmp_path):
     analysis = build()
     hset = HistogramSet()
     for process, values in (("data", [40, 25, 10]), ("ZTT", [20, 12, 5]), ("ZL", [10, 8, 2]), ("jetFakes", [8, 4, 2]), ("HH", [0.05, 0.1, 0.02])):
-        hset.add(HistKey("mt", INCLUSIVE, process, "nominal", "Nominal", "m_vis"), _hist(values))
+        hset[HistKey("mt", INCLUSIVE, process, "nominal", "Nominal", "m_vis")] = _hist(values)
     files = run_plot(hset, analysis, ["mt"], control=True, category=None, variables=["m_vis"], output_dir=tmp_path, blind=False, log=False, signal_scale=None, normalize_by_bin_width=False)
     assert sorted(p.name for p in files) == ["inclusive_m_vis.pdf", "inclusive_m_vis.png"]
     assert all(p.stat().st_size > 1000 for p in files)
@@ -52,8 +52,8 @@ def test_run_plot_routes_non_nominal_region_and_needs_its_histograms(tmp_path):
     analysis = build()
     hset = HistogramSet()
     for process, values in (("data", [8, 5, 2]), ("ZTT", [4, 3, 1]), ("HH", [0.5, 0.2, 0.1])):
-        hset.add(HistKey("mt", INCLUSIVE, process, "same_sign", "Nominal", "m_vis"), _hist(values))
-    hset.add(HistKey("mt", INCLUSIVE, "jetFakes", "nominal", "Nominal", "m_vis"), _hist([100, 100, 100]))
+        hset[HistKey("mt", INCLUSIVE, process, "same_sign", "Nominal", "m_vis")] = _hist(values)
+    hset[HistKey("mt", INCLUSIVE, "jetFakes", "nominal", "Nominal", "m_vis")] = _hist([100, 100, 100])
 
     files = run_plot(
         hset,
@@ -76,7 +76,7 @@ def test_run_plot_routes_non_nominal_region_and_needs_its_histograms(tmp_path):
 def test_non_nominal_plot_rejects_missing_data_unless_blinded(tmp_path):
     analysis = build()
     hset = HistogramSet()
-    hset.add(HistKey("mt", INCLUSIVE, "ZTT", "same_sign", "Nominal", "m_vis"), _hist([4, 3, 1]))
+    hset[HistKey("mt", INCLUSIVE, "ZTT", "same_sign", "Nominal", "m_vis")] = _hist([4, 3, 1])
 
     with pytest.raises(ValueError, match=r"data.*same_sign.*hist --regions same_sign"):
         run_plot(hset, analysis, ["mt"], True, None, ["m_vis"], tmp_path, region="same_sign")
diff --git a/tests/test_shapes.py b/tests/test_shapes.py
new file mode 100644
index 0000000..9bc3f60
--- /dev/null
+++ b/tests/test_shapes.py
@@ -0,0 +1,35 @@
+import uproot
+
+from shapesmith.histogram import HistKey, Histogram, HistogramSet
+from shapesmith.shapes import run_sync, write_shapes
+from tests.mini_analysis import build
+
+
+def _hist(value):
+    return Histogram([0.0, 0.5, 1.0], [value, value], [value, value])
+
+
+def test_write_shapes(tmp_path):
+    path = write_shapes(tmp_path / "sub" / "shapes.root", [("bin", "ZTT", _hist(2.0)), ("bin", "ZTT_CMS_puUp", _hist(2.5))])
+    with uproot.open(path) as f:
+        assert f["bin/ZTT_CMS_puUp"].values().tolist() == [2.5, 2.5] and f["bin/ZTT"].axis().edges().tolist() == [0.0, 0.5, 1.0]
+
+
+def test_run_sync_layout(tmp_path):
+    analysis = build()
+    hset = HistogramSet()
+    for category in ("sig", "bkg"):
+        hset[HistKey("mt", category, "data", "nominal", "Nominal", "score")] = _hist(3.0)
+        hset[HistKey("mt", category, "data", "anti_iso", "Nominal", "score")] = _hist(1.0)
+        hset[HistKey("mt", category, "ZTT", "nominal", "Nominal", "score")] = _hist(2.0)
+        hset[HistKey("mt", category, "ZTT", "nominal", "CMS_puUp", "score")] = _hist(2.1)
+        hset[HistKey("mt", category, "ZTT", "anti_iso", "Nominal", "score")] = _hist(0.2)
+        hset[HistKey("mt", category, "HH", "nominal", "Nominal", "score")] = _hist(0.1)
+        hset[HistKey("mt", category, "jetFakes", "nominal", "Nominal", "score")] = _hist(0.8)
+    hset[HistKey("mt", "inclusive", "ZTT", "nominal", "Nominal", "m_vis")] = (_hist(5.0))  # control shape, never synced
+    files = run_sync(hset, analysis, ["mt"], tmp_path)
+    assert files == [tmp_path / "synced" / "htt_mt.inputs-Run2018.root"]
+    with uproot.open(files[0]) as f:
+        names = sorted(k for k in f.keys(recursive=True, cycle=False) if "/" in k)
+        assert names == sorted(f"mt_{c}/{p}" for c in ("sig", "bkg") for p in ("data_obs", "ZTT", "ZTT_CMS_puUp", "HH", "jetFakes"))
+        assert f["mt_sig/data_obs"].values().tolist() == [3.0, 3.0] and f["mt_sig/data_obs"].axis().edges().tolist() == [0.0, 0.5, 1.0]
diff --git a/tests/test_skim.py b/tests/test_skim.py
index 4d4380d..8d13cb0 100644
--- a/tests/test_skim.py
+++ b/tests/test_skim.py
@@ -1,3 +1,4 @@
+import dataclasses
 import json
 from pathlib import Path
 
@@ -6,9 +7,8 @@
 import pytest
 
 from shapesmith.config import FriendConfig, NtupleConfig, RunConfig
-from shapesmith.io.skims import SkimMissingError, read_manifest, read_skims, skim_path, write_manifest
-from shapesmith.model import Region, Selection
-from shapesmith.skim import SKIM_COLUMNS, columns_for_sample, required_columns, run_skim
+from shapesmith.skim import needed_columns, run_skim
+from shapesmith.store import SKIM_COLUMNS, SkimMissingError, read_manifest, read_skims, skim_path, write_manifest
 from shapesmith.testing import make_mini_dataset
 from tests.mini_analysis import build
 
@@ -31,18 +31,21 @@ def config(tmp_path):
     return _config(tmp_path)
 
 
-def _tighter_skim(analysis):
-    import dataclasses
+def _replace_channel(analysis, **changes):
+    return dataclasses.replace(analysis, channels={"mt": dataclasses.replace(analysis.channel("mt"), **changes)})
 
-    channel = analysis.channel("mt")
-    changed = dataclasses.replace(channel, skim=Selection(cuts={**channel.skim.cuts, "tighter": "m_vis > 40"}))
-    return dataclasses.replace(analysis, channels={"mt": changed})
+
+def _tighter_skim(analysis):
+    return _replace_channel(analysis, skim={**analysis.channel("mt").skim, "tighter": "m_vis > 40"})
 
 
 def _with_cut(analysis, nick, cut):
-    import dataclasses
+    samples = tuple(dataclasses.replace(s, cut=cut) if s.nick == nick else s for s in analysis.channel("mt").samples)
+    return _replace_channel(analysis, samples=samples)
 
-    return dataclasses.replace(analysis, samples=tuple(dataclasses.replace(s, cut=cut) if s.nick == nick else s for s in analysis.samples))
+
+def _sample(analysis, nick):
+    return next(s for s in analysis.channel("mt").samples if s.nick == nick)
 
 
 def _add_npartons(tmp_path):
@@ -59,47 +62,34 @@ def _add_npartons(tmp_path):
         make_ntuple(path, columns, metadata)
 
 
-def test_required_columns_cover_every_expression():
-    columns = required_columns(build(), "mt")
-    assert {"veto", "id_loose", "q_1", "q_2", "id_tight", "trg_wgt", "fake_factor", "gen_match", "puweight", "puweight_up", "puweight_down", "score", "cls", "m_vis", "event"} <= columns
-    assert "genWeight" in columns
+def test_needed_columns_cover_every_expression_of_the_sample():
+    analysis = build()
+    channel = analysis.channel("mt")
+    mc = needed_columns(channel, _sample(analysis, "ZTT_1"))
+    assert {"veto", "id_loose", "q_1", "q_2", "id_tight", "trg_wgt", "fake_factor", "gen_match", "puweight", "puweight_up", "puweight_down", "score", "cls", "m_vis", "event", "genWeight"} <= mc
+    data = needed_columns(channel, _sample(analysis, "DATA_A"))
+    assert {"fake_factor", "m_vis", "score", "event"} <= data and not {"puweight", "gen_match", "genWeight", "trg_wgt"} & data
 
 
 def test_sample_cut_columns_are_required_by_that_sample_only():
     analysis = _with_cut(build(), "ZTT_1", "npartons == 0")
-    ztt, sig = analysis.samples[1], analysis.samples[2]
-    assert "npartons" in columns_for_sample(analysis, "mt", ztt)
-    assert "npartons" not in columns_for_sample(analysis, "mt", sig)
-    assert "npartons" not in required_columns(analysis, "mt")  # read from the files of ZTT_1 only: other samples may lack the column
-
+    channel = analysis.channel("mt")
+    assert "npartons" in needed_columns(channel, _sample(analysis, "ZTT_1"))
+    assert "npartons" not in needed_columns(channel, _sample(analysis, "SIG_1"))
 
-def test_region_replacement_of_baseline_weight_stays_optional_for_data(config):
-    import dataclasses
 
+def test_a_replaced_weight_is_not_read_for_processes_without_it(config):
     analysis = build()
-    channel = analysis.channel("mt")
-    baseline = Selection(channel.baseline.cuts, {**channel.baseline.weights, "mc_only": "puweight"})
-    regions = tuple(
-        Region(region.name, region.replace_cuts, {**region.add_weights, "mc_only": "puweight_up"})
-        if region.name == "same_sign"
-        else region
-        for region in channel.regions
-    )
-    analysis = dataclasses.replace(analysis, channels={"mt": dataclasses.replace(channel, baseline=baseline, regions=regions)})
-    data = analysis.samples[0]
-
-    assert "puweight_up" not in columns_for_sample(analysis, "mt", data)
+    regions = tuple(dataclasses.replace(r, replace_weights={"pu": "puweight_up * 1.0"}) for r in analysis.channel("mt").regions)
+    analysis = _replace_channel(analysis, regions=regions, variations=())
+    assert "puweight_up" in needed_columns(analysis.channel("mt"), _sample(analysis, "ZTT_1"))
+    assert "puweight_up" not in needed_columns(analysis.channel("mt"), _sample(analysis, "DATA_A"))
     run_skim(config, analysis)
-    assert "puweight_up" not in read_skims(config.skim_dir, "mt", [data.nick], None)
+    assert "puweight_up" not in read_skims(config.skim_dir, "mt", ["DATA_A"], None)
 
 
 def test_empty_skims_keep_a_readable_schema(config):
-    import dataclasses
-
-    analysis = build(config)
-    channel = analysis.channel("mt")
-    strict = dataclasses.replace(channel, skim=Selection(cuts={"nothing": "m_vis > 1e9"}))
-    analysis = dataclasses.replace(analysis, channels={"mt": strict})
+    analysis = _replace_channel(build(), skim={"nothing": "m_vis > 1e9"})
     run_skim(config, analysis, ["mt"])
     frame = read_skims(config.skim_dir, "mt", ["ZTT_1", "DATA_A"], ["m_vis", "sample_nick", "is_mc"])
     assert len(frame) == 0 and str(frame["sample_nick"].dtype) in ("string", "object") and frame["is_mc"].dtype == bool
@@ -112,8 +102,7 @@ def test_run_skim_writes_parquet_and_manifest(config):
     frame = read_skims(config.skim_dir, "mt", ["ZTT_1"], None)
     assert set(SKIM_COLUMNS) <= set(frame.columns)
     assert frame["sample_nick"].unique().tolist() == ["ZTT_1"] and frame["is_mc"].all() and not frame["is_data"].any()
-    expected_norm = 10.0 / (100 * 1.0)
-    assert np.allclose(np.abs(frame["norm_weight"]), expected_norm)
+    assert np.allclose(np.abs(frame["norm_weight"]), 10.0 / (100 * 1.0))
     assert set(np.sign(frame["norm_weight"]).unique()) <= {-1.0, 1.0}
     assert (frame["veto"] < 0.5).all() and (frame["id_loose"] > 0.5).all()  # skim selection applied
     assert ((frame["q_1"] * frame["q_2"]) > 0).any()  # both charges kept
@@ -122,9 +111,9 @@ def test_run_skim_writes_parquet_and_manifest(config):
     manifest = read_manifest(config.skim_dir / "mt" / "ZTT_1" / "manifest.json")
     assert manifest["nick"] == "ZTT_1" and len(manifest["files"]) == 2 and manifest["n_out"] == len(frame)
     assert manifest["metadata"]["sample_type"] == "mc"
-    assert manifest["normalisation"]["kind"] == "mc" and set(manifest["normalisation"]) == {"kind", "xsec", "nevents", "generator_weight"}
+    assert manifest["normalisation"] == {"kind": "mc", "xsec": 10.0, "nevents": 100, "generator_weight": 1.0}
     assert manifest["contract"]["selection"] == {"iso_loose": "id_loose > 0.5", "veto": "veto < 0.5"}
-    assert "m_vis" in manifest["contract"]["required_columns"]
+    assert manifest["contract"]["required_columns"] == sorted(needed_columns(analysis.channel("mt"), _sample(analysis, "ZTT_1")))
     assert manifest["contract"]["normalisation"] == manifest["normalisation"]
 
 
@@ -150,14 +139,10 @@ def test_sample_cut_is_part_of_the_skim_contract(tmp_path):
     run_skim(config, analysis)
     path = config.skim_dir / "mt" / "ZTT_1" / "manifest.json"
     plain = read_manifest(path)["contract"]
-    assert plain == {  # unchanged for samples without a cut, so their existing skims stay reusable
-        "selection": {"iso_loose": "id_loose > 0.5", "veto": "veto < 0.5"},
-        "required_columns": sorted(required_columns(analysis, "mt")),
-        "normalisation": {"kind": "mc", "xsec": 10.0, "nevents": 100, "generator_weight": 1.0},
-    }
+    assert set(plain) == {"selection", "required_columns", "normalisation", "friends"}  # no sample_cut key without a cut
     for cut in ("gen_match == 5", "gen_match == 6", None):  # set, changed, removed (gen_match is required anyway)
         changed = _with_cut(analysis, "ZTT_1", cut)
-        with pytest.raises(ValueError, match=r"mt/ZTT_1 is incompatible \(sample cut changed\).*shapesmith skim --force"):
+        with pytest.raises(ValueError, match=r"mt/ZTT_1: sample_cut changed"):
             run_skim(config, changed)
         run_skim(config, changed, force=True)
         assert read_manifest(path)["contract"] == (plain if cut is None else {**plain, "sample_cut": cut})
@@ -171,14 +156,12 @@ def test_a_sample_cut_on_a_new_column_leaves_the_other_skims_reusable(tmp_path):
     run_skim(config, analysis)
     plain = read_manifest(config.skim_dir / "mt" / "SIG_1" / "manifest.json")["contract"]
     changed = _with_cut(analysis, "ZTT_1", "npartons == 0")
-    with pytest.raises(ValueError, match=r"mt/ZTT_1 is incompatible \(required columns changed, sample cut changed\)"):
+    with pytest.raises(ValueError, match=r"mt/ZTT_1: sample_cut changed, columns missing: npartons"):
         run_skim(config, changed)
     run_skim(config, changed, samples=["ZTT_1"], force=True)
     assert all(r.skipped for r in run_skim(config, changed))  # DATA_A and SIG_1 keep the skims made before the cut existed
     assert read_manifest(config.skim_dir / "mt" / "SIG_1" / "manifest.json")["contract"] == plain
-    cut = read_manifest(config.skim_dir / "mt" / "ZTT_1" / "manifest.json")
-    assert cut["contract"]["required_columns"] == sorted({*plain["required_columns"], "npartons"})
-    assert "npartons" in cut["columns"]
+    assert "npartons" in read_manifest(config.skim_dir / "mt" / "ZTT_1" / "manifest.json")["columns"]
 
 
 def test_run_skim_skips_existing_files_unless_forced(config):
@@ -209,6 +192,16 @@ def test_run_skim_resumes_after_a_read_error_without_recomputing_finished_files(
     assert set(manifest["completed"]) == {"ZTT_1_0.root", "ZTT_1_1.root"} and manifest["n_in"] == 400
 
 
+def test_resumed_files_keep_the_stored_columns(config, tmp_path):
+    analysis = build()
+    run_skim(config, analysis)
+    (config.skim_dir / "mt" / "ZTT_1" / "ZTT_1_1.parquet").unlink()
+    fewer = _replace_channel(analysis, variations=())  # puweight_up/down are no longer needed
+    assert [r.basename for r in run_skim(config, fewer) if not r.skipped] == ["ZTT_1_1.root"]
+    schemas = [set(pd.read_parquet(p).columns) for p in sorted((config.skim_dir / "mt" / "ZTT_1").glob("*.parquet"))]
+    assert schemas[0] == schemas[1] and "puweight_up" in schemas[1]
+
+
 def test_run_skim_records_empty_inputs_under_the_new_contract_after_force(tmp_path):
     config = _config(tmp_path, n_events=0)
     analysis = build()
@@ -217,7 +210,7 @@ def test_run_skim_records_empty_inputs_under_the_new_contract_after_force(tmp_pa
     path = config.skim_dir / "mt" / "ZTT_1" / "manifest.json"
     changed = _tighter_skim(analysis)
     run_skim(config, changed, force=True)
-    assert read_manifest(path)["contract"]["selection"] == changed.channel("mt").skim.cuts
+    assert read_manifest(path)["contract"]["selection"] == changed.channel("mt").skim
     assert all(r.skipped for r in run_skim(config, changed))
 
 
@@ -232,13 +225,13 @@ def test_run_skim_manifest_counts_cover_reused_files(config):
 
 
 def test_run_skim_keeps_the_previous_parquet_when_a_write_fails(tmp_path, monkeypatch):
-    import shapesmith.io.skims as skims
+    import shapesmith.store as store
 
     config = _config(tmp_path, workers=1)  # inline jobs, so the patched writer is used
     run_skim(config, build())
     target = config.skim_dir / "mt" / "ZTT_1" / "ZTT_1_1.parquet"
     rows = len(pd.read_parquet(target))
-    real = skims.pq.write_table
+    real = store.pq.write_table
 
     def failing(table, where, **kwargs):
         if Path(where).name.startswith("ZTT_1_1"):
@@ -246,7 +239,7 @@ def failing(table, where, **kwargs):
             raise OSError("disk full")
         return real(table, where, **kwargs)
 
-    monkeypatch.setattr(skims.pq, "write_table", failing)
+    monkeypatch.setattr(store.pq, "write_table", failing)
     with pytest.raises(RuntimeError, match="disk full"):
         run_skim(config, build(), force=True)
     assert len(pd.read_parquet(target)) == rows
@@ -271,36 +264,26 @@ def partial(self, text, *args, **kwargs):
     assert read_manifest(path) == {"version": 1}
 
 
-@pytest.mark.parametrize("change", ["selection", "columns", "normalisation"])
-def test_run_skim_rejects_incompatible_reuse_with_force_guidance(config, change):
-    import dataclasses
-
+@pytest.mark.parametrize(("change", "problem"), [("selection", "selection changed"), ("columns", "columns missing: new_required_column"), ("normalisation", "normalisation changed")])
+def test_run_skim_lists_every_incompatible_skim_with_force_guidance(config, change, problem):
     analysis = build()
     run_skim(config, analysis)
     if change == "selection":
         analysis = _tighter_skim(analysis)
     elif change == "columns":
-        channel = analysis.channel("mt")
-        changed = dataclasses.replace(channel, keep_columns=(*channel.keep_columns, "new_required_column"))
-        analysis = dataclasses.replace(analysis, channels={"mt": changed})
+        analysis = _replace_channel(analysis, keep_columns=("event", "new_required_column"))
     else:
-        samples = tuple(dataclasses.replace(sample, xsec=sample.xsec * 2) if sample.nick == "ZTT_1" else sample for sample in analysis.samples)
-        analysis = dataclasses.replace(analysis, samples=samples)
-
-    with pytest.raises(ValueError, match=r"incompatible.*shapesmith skim --force"):
+        analysis = _replace_channel(analysis, samples=tuple(dataclasses.replace(s, xsec=s.xsec * 2) for s in analysis.channel("mt").samples))
+    with pytest.raises(ValueError, match=r"shapesmith skim --force") as excinfo:
         run_skim(config, analysis)
+    assert all(f"mt/{nick}: {problem}" in str(excinfo.value) for nick in ("DATA_A", "SIG_1", "ZTT_1"))
 
 
-def test_run_skim_rejects_legacy_manifest_without_selection_contract(config):
-    analysis = build()
-    run_skim(config, analysis)
-    path = config.skim_dir / "mt" / "ZTT_1" / "manifest.json"
-    manifest = read_manifest(path)
-    manifest.pop("contract")
-    path.write_text(json.dumps(manifest))
-
-    with pytest.raises(ValueError, match=r"selection was not recorded.*shapesmith skim --force"):
-        run_skim(config, analysis)
+def test_a_skim_without_manifest_is_incompatible(config):
+    run_skim(config, build())
+    (config.skim_dir / "mt" / "ZTT_1" / "manifest.json").unlink()
+    with pytest.raises(ValueError, match="mt/ZTT_1: its manifest is missing"):
+        run_skim(config, build())
 
 
 def test_read_skims_missing_nick(config):
@@ -309,4 +292,4 @@ def test_read_skims_missing_nick(config):
 
 
 def test_skim_path():
-    assert skim_path(config_dir := __import__("pathlib").Path("/s"), "mt", "N", "N_0.root") == config_dir / "mt" / "N" / "N_0.parquet"
+    assert skim_path(Path("/s"), "mt", "N", "N_0.root") == Path("/s") / "mt" / "N" / "N_0.parquet"
diff --git a/tests/test_sync.py b/tests/test_sync.py
deleted file mode 100644
index 97cb806..0000000
--- a/tests/test_sync.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import uproot
-
-from shapesmith.histogram import Histogram
-from shapesmith.histograms import HistKey, HistogramSet
-from shapesmith.sync import run_sync, synced_processes
-from tests.mini_analysis import build
-
-
-def _hist(value):
-    return Histogram([0.0, 0.5, 1.0], [value, value], [value, value])
-
-
-def test_synced_processes():
-    assert synced_processes(build()) == ("ZTT", "ZL", "jetFakes", "HH")
-
-
-def test_run_sync_layout(tmp_path):
-    analysis = build()
-    hset = HistogramSet()
-    for category in ("sig", "bkg"):
-        hset.add(HistKey("mt", category, "data", "nominal", "Nominal", "score"), _hist(3.0))
-        hset.add(HistKey("mt", category, "data", "anti_iso", "Nominal", "score"), _hist(1.0))
-        hset.add(HistKey("mt", category, "ZTT", "nominal", "Nominal", "score"), _hist(2.0))
-        hset.add(HistKey("mt", category, "ZTT", "nominal", "CMS_puUp", "score"), _hist(2.1))
-        hset.add(HistKey("mt", category, "ZTT", "anti_iso", "Nominal", "score"), _hist(0.2))
-        hset.add(HistKey("mt", category, "HH", "nominal", "Nominal", "score"), _hist(0.1))
-        hset.add(HistKey("mt", category, "jetFakes", "nominal", "Nominal", "score"), _hist(0.8))
-    hset.add(HistKey("mt", "inclusive", "ZTT", "nominal", "Nominal", "m_vis"), _hist(5.0))  # control shape, never synced
-    files = run_sync(hset, analysis, ["mt"], tmp_path)
-    assert files == [tmp_path / "synced" / "htt_mt.inputs-Run2018.root"]
-    with uproot.open(files[0]) as f:
-        names = sorted(k for k in f.keys(recursive=True, cycle=False) if "/" in k)
-        assert names == sorted(f"mt_{c}/{p}" for c in ("sig", "bkg") for p in ("data_obs", "ZTT", "ZTT_CMS_puUp", "HH", "jetFakes"))
-        assert f["mt_sig/data_obs"].values().tolist() == [3.0, 3.0] and f["mt_sig/data_obs"].axis().edges().tolist() == [0.0, 0.5, 1.0]
diff --git a/tests/test_validate.py b/tests/test_validate.py
new file mode 100644
index 0000000..ec387b2
--- /dev/null
+++ b/tests/test_validate.py
@@ -0,0 +1,97 @@
+import dataclasses
+
+import pytest
+
+from shapesmith import model
+from shapesmith.validate import validate
+from tests.mini_analysis import build, build_embedding
+
+
+def _with_channel(analysis, **changes):
+    channel = dataclasses.replace(analysis.channel("mt"), **changes)
+    return dataclasses.replace(analysis, channels={"mt": channel})
+
+
+def test_mini_analysis_validates():
+    for analysis in (build(), build_embedding()):
+        validate(analysis)
+    channel = build().channel("mt")
+    assert channel.backgrounds() == ("ZTT", "ZL", "jetFakes")
+    assert channel.data() == "data" and channel.kind_of(channel.process("ZTT")) == "mc"
+    assert [s.nick for s in channel.samples_of("DY")] == ["ZTT_1"]
+
+
+def test_auxiliary_processes_are_no_backgrounds():
+    assert build_embedding().channel("mt").backgrounds() == ("ZL", "EMB", "jetFakes")
+
+
+def test_sample_norm_weight():
+    mc = model.Sample("X", "G", "mc", xsec=10.0, nevents=100, generator_weight=0.5)
+    assert mc.norm_weight == pytest.approx(10.0 / (100 * 0.5))
+    assert model.Sample("D", "data", "data").norm_weight == 1.0
+
+
+def test_validate_reports_all_problems():
+    analysis = build()
+    channel = analysis.channel("mt")
+    broken = _with_channel(
+        analysis,
+        processes=channel.processes + (model.Process("ZTT", "NOGROUP", "background", "Z"),),
+        estimators=(model.DataMinus("jetFakes", "does_not_exist", ("ZTT", "nope")),),
+    )
+    with pytest.raises(model.AnalysisError) as excinfo:
+        validate(broken)
+    message = str(excinfo.value)
+    assert "channel mt: duplicate process ZTT" in message
+    assert "no samples for group NOGROUP" in message
+    assert "region does_not_exist does not exist" in message and "subtracts unknown process nope" in message
+
+
+def test_validate_checks_region_targets():
+    channel = build().channel("mt")
+    for region, problem in (
+        (model.Region("bad", {"no_such_cut": "1"}), "replaces unknown cut no_such_cut"),
+        (model.Region("bad", replace_weights={"no_such_weight": "1"}), "replaces unknown weight no_such_weight"),
+        (model.Region("bad", add_weights={"pu": "1"}), "adds weight pu, which a process carries"),
+    ):
+        with pytest.raises(model.AnalysisError, match=problem):
+            validate(_with_channel(build(), regions=channel.regions + (region,)))
+
+
+def test_validate_checks_variations():
+    channel = build().channel("mt")
+    for variations, problem in (
+        ((model.WeightVariation("vUp", {"no_such_weight": "1"}), model.WeightVariation("vDown", {"pu": "1"})), "replaces unknown weight no_such_weight"),
+        ((model.ColumnVariation("tesUp", "__tesUp"),), "its partner tesDown is missing"),
+        ((model.ColumnVariation("tesDown", "__tesDown", regions=("nowhere",)), model.ColumnVariation("tesUp", "__tesUp")), "unknown region nowhere"),
+        ((model.ColumnVariation("xUp", "__xUp", {"m_vis": "m_vis"}), model.ColumnVariation("xDown", "")), "needs exactly one of suffix and derived"),
+        ((model.ColumnVariation("CMS_puUp", "__puUp"),), "duplicate variation CMS_puUp"),
+        ((model.ColumnVariation("aUp", "__aUp", applies_to=("simulation",)), model.ColumnVariation("aDown", "__aDown")), "unknown sample kind simulation"),
+    ):
+        with pytest.raises(model.AnalysisError, match=problem):
+            validate(_with_channel(build(), variations=channel.variations + variations))
+
+
+def test_a_variation_without_direction_is_a_template():
+    channel = build().channel("mt")
+    grid = model.ColumnVariation("emb1p002", derived={"m_vis": "m_vis * 1.002"}, applies_to=("mc",), regions=("nominal",))
+    validate(_with_channel(build(), variations=channel.variations + (grid,)))
+
+
+def test_validate_checks_estimators_and_auxiliary_processes():
+    analysis = build_embedding()
+    channel = analysis.channel("mt")
+    with pytest.raises(model.AnalysisError, match="subtracts the auxiliary process ZTT"):
+        validate(_with_channel(analysis, estimators=(model.DataMinus("jetFakes", "anti_iso", ("ZTT",)),)))
+    with pytest.raises(model.AnalysisError, match="template shift t: unknown process TTT"):
+        validate(_with_channel(analysis, estimators=(model.TemplateShift("t", "EMB", "TTT", 0.1),)))
+    with pytest.raises(model.AnalysisError, match="ML export: ZTT is an auxiliary process"):
+        validate(dataclasses.replace(analysis, ml=dataclasses.replace(analysis.ml, processes=("ZTT",))))
+    mixed = channel.samples + (model.Sample("DATA_B", "EMB", "data"),)
+    with pytest.raises(model.AnalysisError, match="process EMB: samples of different kinds"):
+        validate(_with_channel(analysis, samples=mixed))
+
+
+def test_dataclasses_are_frozen():
+    with pytest.raises(dataclasses.FrozenInstanceError):
+        build().signal = "other"
diff --git a/tests/test_variations.py b/tests/test_variations.py
new file mode 100644
index 0000000..e31805a
--- /dev/null
+++ b/tests/test_variations.py
@@ -0,0 +1,171 @@
+"""Column variations end to end on the mini dataset with CROWN shifts: skim checks and reuse, fill rule, estimates."""
+import dataclasses
+import shutil
+
+import numpy as np
+import pytest
+
+from shapesmith.config import FriendConfig, NtupleConfig, RunConfig
+from shapesmith.estimates import run_estimates
+from shapesmith.fill import bookings, run_hist
+from shapesmith.histogram import INCLUSIVE, HistKey
+from shapesmith.model import ColumnVariation
+from shapesmith.skim import run_skim
+from shapesmith.store import read_manifest, read_skims
+from shapesmith.testing import make_mini_dataset
+from tests.mini_analysis import build_embedding
+
+TES = ("CMS_tesUp", "CMS_tesDown")
+FF = ("CMS_ffStatUp", "CMS_ffStatDown")
+
+
+@pytest.fixture
+def config(tmp_path):
+    make_mini_dataset(tmp_path / "data", n_files=2, n_events=300, variations=True)
+    return RunConfig(
+        analysis="tests.mini_analysis:build_embedding",
+        era="2018",
+        channels=["mt"],
+        ntuples=NtupleConfig(base=str(tmp_path / "data" / "CROWNRun"), friends=[FriendConfig(base=str(tmp_path / "data" / "CROWNFriends" / "nn"))]),
+        skim_dir=tmp_path / "skims",
+        output_dir=tmp_path / "out",
+        workers=1,
+    )
+
+
+def _analysis(skim_cut=None, keep=TES + FF):
+    analysis = build_embedding()
+    channel = analysis.channel("mt")
+    variations = tuple(v for v in channel.variations if not isinstance(v, ColumnVariation) or v.name in keep)
+    skim = {**channel.skim, "mass": skim_cut} if skim_cut else channel.skim
+    return dataclasses.replace(analysis, channels={"mt": dataclasses.replace(channel, variations=variations, skim=skim)})
+
+
+def _manifest(config, nick):
+    return read_manifest(config.skim_dir / "mt" / nick / "manifest.json")
+
+
+def test_skim_reads_the_shifted_columns_of_each_kind(config):
+    run_skim(config, _analysis())
+    emb = read_skims(config.skim_dir, "mt", ["EMB_A"], None)
+    data = read_skims(config.skim_dir, "mt", ["DATA_A"], None)
+    assert {"m_vis__tesUp", "m_vis__tesDown", "fake_factor__ffStatUp"} <= set(emb.columns)
+    assert "fake_factor__ffStatUp" in data and "m_vis__tesUp" not in data  # tes does not apply to data
+    assert _manifest(config, "EMB_A")["contract"]["column_variations"] == {"CMS_tesUp": "__tesUp", "CMS_tesDown": "__tesDown", "CMS_ffStatUp": "__ffStatUp", "CMS_ffStatDown": "__ffStatDown"}
+    assert _manifest(config, "DATA_A")["contract"]["column_variations"] == {"CMS_ffStatUp": "__ffStatUp", "CMS_ffStatDown": "__ffStatDown"}
+
+
+def test_an_event_that_passes_only_shifted_is_kept_and_filled_only_shifted(config):
+    analysis = _analysis(skim_cut="m_vis > 100")
+    run_skim(config, analysis)
+    emb = read_skims(config.skim_dir, "mt", ["EMB_A"], None)
+    only_shifted = (emb["m_vis"] <= 100) & (emb["m_vis__tesUp"] > 100)
+    assert only_shifted.any() and ((emb["m_vis"] > 100) | only_shifted).all()
+    hset = run_hist(config, analysis, ["mt"], True, None, True, ["EMB"], config.output_dir / "control.root")
+    nominal = hset[HistKey("mt", INCLUSIVE, "EMB", "nominal", "Nominal", "m_vis")]
+    up = hset[HistKey("mt", INCLUSIVE, "EMB", "nominal", "CMS_tesUp", "m_vis")]
+    base = (emb["veto"] < 0.5) & ((emb["q_1"] * emb["q_2"]) < 0) & (emb["id_tight"] > 0.5) & (emb["gen_match"] == 5)
+    weights = emb["emb_genweight"].astype(float) * emb["trg_wgt"].astype(float)
+    assert nominal.sum() == pytest.approx(weights[base & (emb["m_vis"] > 100)].sum())  # the skim cut is re-applied nominally
+    assert up.sum() == pytest.approx(weights[base & (emb["m_vis__tesUp"] > 100) & (emb["m_vis__tesUp"] <= 200)].sum())  # 200: last edge
+
+
+def test_a_declared_shift_without_branches_fails_the_skim(config):
+    analysis = build_embedding()
+    channel = analysis.channel("mt")
+    jes = tuple(ColumnVariation(f"CMS_jes{d}", f"__jes{d}", applies_to=("mc",)) for d in ("Up", "Down"))
+    analysis = dataclasses.replace(analysis, channels={"mt": dataclasses.replace(channel, variations=channel.variations + jes)})
+    with pytest.raises(RuntimeError, match=r"declared shift CMS_jesUp \(__jesUp\) has no shifted branch"):
+        run_skim(config, analysis)
+
+
+def test_an_undeclared_shift_branch_fails_the_skim_of_kinds_with_declared_shifts(config):
+    with pytest.raises(RuntimeError, match=r"ZTT_1_0.root: undeclared shift __ffStatDown; undeclared shift __ffStatUp") as excinfo:
+        run_skim(config, _analysis(keep=TES))
+    assert "DATA_A" not in str(excinfo.value)  # no shift declared for data: its fake_factor__ffStat branches are not checked
+
+
+def test_a_superset_skim_serves_fewer_variations(config):
+    run_skim(config, _analysis())
+    assert all(r.skipped for r in run_skim(config, _analysis(keep=FF)))
+    assert all(r.skipped for r in run_skim(config, _analysis(keep=())))
+
+
+def test_a_missing_variation_makes_the_skim_incompatible(config):
+    run_skim(config, _analysis(keep=()))  # without declared shifts, the shifted branches are not checked
+    with pytest.raises(ValueError, match=r"mt/EMB_A: column variations missing: CMS_ffStatDown, CMS_ffStatUp, CMS_tesDown, CMS_tesUp"):
+        run_skim(config, _analysis())
+
+
+def test_an_incomplete_superset_skim_is_skimmed_again(config):
+    run_skim(config, _analysis())
+    (config.skim_dir / "mt" / "EMB_A" / "EMB_A_1.parquet").unlink()
+    results = run_skim(config, _analysis(keep=()))
+    assert sorted(r.basename for r in results if not r.skipped) == ["EMB_A_0.root", "EMB_A_1.root"]
+    assert "column_variations" not in _manifest(config, "EMB_A")["contract"]
+
+
+def test_friends_are_part_of_the_contract(config, tmp_path):
+    run_skim(config, _analysis())
+    assert _manifest(config, "ZTT_1")["contract"]["friends"] == [str(tmp_path / "data" / "CROWNFriends" / "nn")]
+    shutil.copytree(tmp_path / "data" / "CROWNFriends" / "nn", tmp_path / "data" / "CROWNFriends" / "nn_v2")
+    mc_only = FriendConfig(base=str(tmp_path / "data" / "CROWNFriends" / "nn_v2"), applies_to=("mc",))
+    changed = config.model_copy(update={"ntuples": config.ntuples.model_copy(update={"friends": [*config.ntuples.friends, mc_only]})})
+    with pytest.raises(ValueError, match=r"incompatible") as excinfo:
+        run_skim(changed, _analysis())
+    assert "mt/ZTT_1: friends missing" in str(excinfo.value) and "DATA_A" not in str(excinfo.value) and "EMB_A" not in str(excinfo.value)
+
+
+def test_the_auxiliary_process_is_booked_nominal_only_without_variations():
+    by_process = {b.process.name: b for b in bookings(_analysis().channel("mt"), systematics=True)}
+    assert (by_process["ZTT"].regions, by_process["ZTT"].variations) == (("nominal",), ())
+    assert by_process["EMB"].regions == ("nominal", "anti_iso") and {v.name for v in by_process["EMB"].variations} == set(TES + FF)
+
+
+def test_column_variations_are_filled_only_where_they_change_an_expression(config):
+    analysis = _analysis()
+    run_skim(config, analysis)
+    hset = run_hist(config, analysis, ["mt"], True, None, True, None, config.output_dir / "control.root")
+    categories = run_hist(config, analysis, ["mt"], False, None, True, None, config.output_dir / "shapes.root")
+
+    def variations(hs, process, region):
+        return {k.variation for k in hs.select(process=process, region=region)} - {"Nominal"}
+
+    assert variations(hset, "EMB", "nominal") == set(TES)  # the ff weight exists only in anti_iso, where ffStat is filled
+    assert variations(hset, "EMB", "anti_iso") == set(TES + FF)
+    assert variations(hset, "data", "anti_iso") == set(FF) and variations(hset, "data", "nominal") == set()
+    assert variations(hset, "ZL", "nominal") == {"CMS_puUp", "CMS_puDown", *TES}
+    assert variations(categories, "EMB", "nominal") == set()  # score and cls carry no tes shift
+    emb = read_skims(config.skim_dir, "mt", ["EMB_A"], None)
+    selected = emb[(emb["veto"] < 0.5) & ((emb["q_1"] * emb["q_2"]) < 0) & (emb["id_tight"] > 0.5) & (emb["gen_match"] == 5)]
+    expected, _ = np.histogram(selected["m_vis__tesUp"], bins=[0.0, 50.0, 100.0, 200.0], weights=selected["emb_genweight"].astype(float) * selected["trg_wgt"].astype(float))
+    assert np.allclose(hset[HistKey("mt", INCLUSIVE, "EMB", "nominal", "CMS_tesUp", "m_vis")].values, expected)
+
+
+def test_a_process_cut_on_a_shifted_column_is_rewritten(config):
+    analysis = _analysis()
+    channel = analysis.channel("mt")
+    zl = channel.process("ZL")
+    zl = dataclasses.replace(zl, selection=dataclasses.replace(zl.selection, cuts={**zl.selection.cuts, "mass": "m_vis > 60"}))
+    analysis = dataclasses.replace(analysis, channels={"mt": dataclasses.replace(channel, processes=tuple(zl if p.name == "ZL" else p for p in channel.processes))})
+    run_skim(config, analysis)
+    hset = run_hist(config, analysis, ["mt"], False, None, True, ["ZL"], config.output_dir / "shapes.root")
+    mc = read_skims(config.skim_dir, "mt", ["ZTT_1"], None)
+    base = (mc["veto"] < 0.5) & ((mc["q_1"] * mc["q_2"]) < 0) & (mc["id_tight"] > 0.5) & (mc["gen_match"] != 5) & (mc["cls"] == 0)
+    weights = mc["norm_weight"] * 10.0 * mc["trg_wgt"].astype(float) * mc["puweight"].astype(float)
+    assert hset[HistKey("mt", "sig", "ZL", "nominal", "CMS_tesUp", "score")].sum() == pytest.approx(weights[base & (mc["m_vis__tesUp"] > 60)].sum())
+    assert hset[HistKey("mt", "sig", "ZL", "nominal", "Nominal", "score")].sum() == pytest.approx(weights[base & (mc["m_vis"] > 60)].sum())
+
+
+def test_jet_fakes_carry_the_column_variations_of_their_inputs(config):
+    analysis = _analysis()
+    run_skim(config, analysis)
+    hset = run_hist(config, analysis, ["mt"], True, None, True, None, config.output_dir / "control.root")
+    run_estimates(hset, analysis, ["mt"])
+    assert {k.variation for k in hset.select(process="jetFakes")} == {"Nominal", *TES, *FF}
+    key = HistKey("mt", INCLUSIVE, "jetFakes", "nominal", "CMS_tesUp", "m_vis")
+    data = hset[HistKey("mt", INCLUSIVE, "data", "anti_iso", "Nominal", "m_vis")].values
+    emb = hset[HistKey("mt", INCLUSIVE, "EMB", "anti_iso", "CMS_tesUp", "m_vis")].values
+    zl = hset[HistKey("mt", INCLUSIVE, "ZL", "anti_iso", "CMS_tesUp", "m_vis")].values
+    assert np.allclose(hset[key].values, data - emb - zl)  # data has no tes shift: its nominal enters
+    assert {k.variation for k in hset.select(process="EMB", region="nominal")} >= {"CMS_htt_emb_ttbar_2018Up", "CMS_htt_emb_ttbar_2018Down"}

From 00364c1e5c3cf89b4fa806ca745064fae5b1e801 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 01:33:02 +0200
Subject: [PATCH 04/33] feat: shapesmith measure runs Analysis.measurement

A measurement is an object with a name and run(context); the stage calls it
with a MeasureContext (config, analysis, channels, output directory
<output_dir>/<name>/<era>/, events() on the one event rule, payload
provenance) and records versions there. --suggest-binning is passed through
and writes nothing.
---
 src/shapesmith/cli.py                   |  9 ++++
 src/shapesmith/measurements/__init__.py | 50 +++++++++++++++++
 tests/test_measure.py                   | 71 +++++++++++++++++++++++++
 3 files changed, 130 insertions(+)
 create mode 100644 src/shapesmith/measurements/__init__.py
 create mode 100644 tests/test_measure.py

diff --git a/src/shapesmith/cli.py b/src/shapesmith/cli.py
index 3554b5e..2abe6e1 100644
--- a/src/shapesmith/cli.py
+++ b/src/shapesmith/cli.py
@@ -113,6 +113,15 @@ def estimate(config: Path = ConfigOption, channels: Optional[str] = ChannelsOpti
     typer.echo(f"{len(added)} histograms added to {path}")
 
 
+@app.command()
+def measure(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption, suggest_binning: bool = typer.Option(False, "--suggest-binning", help="print proposed bin edges instead of measuring"), workers: Optional[int] = WorkersOption):
+    """Run the analysis's measurement; results in <output_dir>/<measurement>/<era>/."""
+    from shapesmith.measurements import run_measure
+
+    cfg, analysis, selected = _setup(config, channels, workers=workers, overrides=overrides)
+    typer.echo(f"measurement output: {run_measure(cfg, analysis, selected, suggest_binning)}")
+
+
 @app.command()
 def sync(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption):
     """Write combine-style shape files per channel."""
diff --git a/src/shapesmith/measurements/__init__.py b/src/shapesmith/measurements/__init__.py
new file mode 100644
index 0000000..e462034
--- /dev/null
+++ b/src/shapesmith/measurements/__init__.py
@@ -0,0 +1,50 @@
+"""Measurements: plugins on the same event query and histogram tools as the analysis stages.
+
+An analysis sets `Analysis.measurement` to an object with a `name` and a `run(context)` method;
+`shapesmith measure` runs it with its output directory `<output_dir>/<name>/<era>/`. The measurement reads the skims
+itself (`context.events`), possibly in several passes, and writes its results there.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Iterable, Protocol
+
+from shapesmith.config import RunConfig
+from shapesmith.events import Events, Query, load
+from shapesmith.model import Analysis
+from shapesmith.provenance import record, write_versions
+
+
+@dataclass(frozen=True)
+class MeasureContext:
+    config: RunConfig
+    analysis: Analysis
+    channels: list[str]
+    output: Path
+    suggest_binning: bool = False  # print proposed bin edges instead of measuring
+
+    def events(self, query: Query, columns: Iterable[str] = ()) -> Events:
+        return load(self.config, self.analysis, query, columns)
+
+    def provenance(self, **extra) -> dict:
+        """What a payload records about its origin: the versions record with the analysis name, the era and `extra`."""
+        result = record(self.config)
+        result["analysis"]["name"] = self.analysis.name
+        return {**result, "era": self.analysis.era, **extra}
+
+
+class Measurement(Protocol):
+    name: str
+
+    def run(self, context: MeasureContext) -> None: ...
+
+
+def run_measure(config: RunConfig, analysis: Analysis, channels: list[str], suggest_binning: bool = False) -> Path:
+    if analysis.measurement is None:
+        raise ValueError(f"analysis {analysis.name} defines no measurement")
+    output = Path(config.output_dir) / analysis.measurement.name / analysis.era
+    if not suggest_binning:
+        write_versions(config, analysis.name, output)
+    analysis.measurement.run(MeasureContext(config, analysis, channels, output, suggest_binning))
+    return output
diff --git a/tests/test_measure.py b/tests/test_measure.py
new file mode 100644
index 0000000..34a3250
--- /dev/null
+++ b/tests/test_measure.py
@@ -0,0 +1,71 @@
+import dataclasses
+import json
+import textwrap
+from dataclasses import dataclass
+
+import pytest
+from typer.testing import CliRunner
+
+from shapesmith.cli import app
+from shapesmith.events import Query
+from shapesmith.measurements import MeasureContext, run_measure
+from shapesmith.testing import make_mini_dataset
+from tests import mini_analysis
+
+
+@dataclass(frozen=True)
+class CountEvents:
+    """A minimal measurement: the weighted ZTT yield per channel, with the payload provenance."""
+
+    name: str = "count_events"
+
+    def run(self, context: MeasureContext) -> None:
+        result = {channel: float(context.events(Query(channel, "ZTT")).weights.sum()) for channel in context.channels}
+        if context.suggest_binning:
+            print(f"suggested: {result}")
+            return
+        (context.output / "result.json").write_text(json.dumps({"yields": result, "provenance": context.provenance(channel="mt")}))
+
+
+def build(config=None):
+    return dataclasses.replace(mini_analysis.build(config), measurement=CountEvents())
+
+
+def test_run_measure_needs_a_measurement(mini_run):
+    config, analysis = mini_run
+    with pytest.raises(ValueError, match="defines no measurement"):
+        run_measure(config, analysis, ["mt"])
+
+
+def test_run_measure_writes_into_its_own_directory(mini_run):
+    config, _ = mini_run
+    output = run_measure(config, build(config), ["mt"])
+    assert output == config.output_dir / "count_events" / "2018"
+    result = json.loads((output / "result.json").read_text())
+    assert result["yields"]["mt"] > 0
+    provenance = result["provenance"]
+    assert provenance["analysis"]["name"] == "mini" and provenance["analysis"]["module"] == "tests.mini_analysis:build"
+    assert provenance["era"] == "2018" and provenance["channel"] == "mt" and provenance["ntuples"] == config.ntuples.base and provenance["config_sha256"]
+    assert (output / "versions" / "mini.json").exists()
+
+
+def test_measure_command(tmp_path):
+    make_mini_dataset(tmp_path / "data", n_files=1, n_events=100)
+    config = tmp_path / "run.yaml"
+    config.write_text(textwrap.dedent(f"""
+        analysis: tests.test_measure:build
+        era: "2018"
+        channels: [mt]
+        ntuples:
+          base: {tmp_path / 'data' / 'CROWNRun'}
+          friends: [{{base: {tmp_path / 'data' / 'CROWNFriends' / 'nn'}}}]
+        skim_dir: {tmp_path / 'skims'}
+        output_dir: {tmp_path / 'out'}
+        workers: 1
+    """))
+    runner = CliRunner()
+    assert runner.invoke(app, ["skim", "-c", str(config)]).exit_code == 0
+    result = runner.invoke(app, ["measure", "-c", str(config), "--suggest-binning"])
+    assert result.exit_code == 0 and "suggested" in result.output and not (tmp_path / "out" / "count_events").exists()
+    assert runner.invoke(app, ["measure", "-c", str(config)]).exit_code == 0
+    assert (tmp_path / "out" / "count_events" / "2018" / "result.json").exists()

From 191fa5af85fd29023479a8b0b4390e9cdd414b0b Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 01:33:51 +0200
Subject: [PATCH 05/33] feat: correctionlib payload writer with provenance

shapesmith.payloads builds schema-v2 nodes (binning, category, correction,
correction set), stores the provenance as JSON in CorrectionSet.description,
checks every payload (schema, evaluator, no $schema key so that correctionlib
2.6 reads it) and writes it gzip compressed. correctionlib>=2.6 and
numpy>=2.0 become core dependencies.
---
 environment.yml            |  3 +-
 pyproject.toml             |  5 +--
 src/shapesmith/payloads.py | 72 ++++++++++++++++++++++++++++++++++++++
 tests/test_payloads.py     | 45 ++++++++++++++++++++++++
 4 files changed, 122 insertions(+), 3 deletions(-)
 create mode 100644 src/shapesmith/payloads.py
 create mode 100644 tests/test_payloads.py

diff --git a/environment.yml b/environment.yml
index a29d712..dcbd6bb 100644
--- a/environment.yml
+++ b/environment.yml
@@ -6,7 +6,8 @@ dependencies:
   - numexpr>=2.8
   - pandas>=2.1
   - pyarrow>=15
-  - numpy>=1.26
+  - numpy>=2.0
+  - correctionlib>=2.6
   - mplhep>=0.3.30
   - matplotlib>=3.8
   - typer>=0.12
diff --git a/pyproject.toml b/pyproject.toml
index b5c1535..897e48a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "shapesmith"
-description = "From CROWN ntuples to histograms, background estimates, datacards, fits and ML training folds - without ROOT."
+description = "From CROWN ntuples to histograms, background estimates, datacards, fits, ML training folds and measurements - without ROOT."
 readme = "README.md"
 requires-python = ">=3.11"
 license = {text = "MIT"}
@@ -14,7 +14,8 @@ dependencies = [
     "numexpr>=2.8",
     "pandas>=2.1",
     "pyarrow>=15",
-    "numpy>=1.26",
+    "numpy>=2.0",
+    "correctionlib>=2.6",
     "mplhep>=0.3.30",
     "matplotlib>=3.8",
     "typer>=0.12",
diff --git a/src/shapesmith/payloads.py b/src/shapesmith/payloads.py
new file mode 100644
index 0000000..c8e7819
--- /dev/null
+++ b/src/shapesmith/payloads.py
@@ -0,0 +1,72 @@
+"""correctionlib payloads (schema v2): node builders, provenance, the schema check and gzip I/O.
+
+The provenance of a payload (what produced it) is a JSON object in `CorrectionSet.description`. Payloads are written
+without unset fields, so that files written with a newer correctionlib (which adds a `$schema` key) stay readable by
+correctionlib 2.6 (LCG_108) and by CROWN.
+"""
+from __future__ import annotations
+
+import gzip
+import json
+from pathlib import Path
+from typing import Mapping, Sequence
+
+import correctionlib
+import correctionlib.schemav2 as cs
+import numpy as np
+
+Node = cs.Binning | cs.Category | float
+
+
+def variable(name: str, type: str = "real", description: str | None = None) -> cs.Variable:
+    fields = {"description": description} if description is not None else {}
+    return cs.Variable(name=name, type=type, **fields)
+
+
+def binning(input: str, edges: Sequence[float], content: Sequence[Node], flow: str | Node = "clamp") -> cs.Binning:
+    """A binned node; numbers (also numpy arrays) become plain floats."""
+    content = [float(c) if isinstance(c, (int, float, np.number)) else c for c in content]
+    return cs.Binning(nodetype="binning", input=input, edges=[float(e) for e in edges], content=content, flow=flow)
+
+
+def category(input: str, content: Mapping[str | int, Node], default: Node | None = None) -> cs.Category:
+    items = [cs.CategoryItem(key=key, value=float(value) if isinstance(value, (int, float, np.number)) else value) for key, value in content.items()]
+    fields = {"default": float(default) if isinstance(default, (int, float, np.number)) else default} if default is not None else {}
+    return cs.Category(nodetype="category", input=input, content=items, **fields)
+
+
+def correction(name: str, inputs: Sequence[cs.Variable], data: Node, version: int = 1, description: str | None = None, output: cs.Variable | None = None) -> cs.Correction:
+    fields = {"description": description} if description is not None else {}
+    return cs.Correction(name=name, version=version, inputs=list(inputs), output=output or variable("weight"), data=data, **fields)
+
+
+def correction_set(corrections: Sequence[cs.Correction], provenance: Mapping) -> cs.CorrectionSet:
+    return cs.CorrectionSet(schema_version=2, description=json.dumps(provenance, sort_keys=True), corrections=list(corrections))
+
+
+def provenance(cset: cs.CorrectionSet) -> dict:
+    return json.loads(cset.description)
+
+
+def dumps(cset: cs.CorrectionSet) -> str:
+    """The payload JSON, checked: valid schema v2, loadable by the correctionlib evaluator, no `$schema` key."""
+    text = cset.model_dump_json(exclude_unset=True)
+    if "$schema" in json.loads(text):
+        raise ValueError("payload carries a $schema key, which correctionlib 2.6 rejects")
+    cs.CorrectionSet.model_validate_json(text)
+    correctionlib.CorrectionSet.from_string(text)
+    return text
+
+
+def write(cset: cs.CorrectionSet, path: Path) -> Path:
+    """Write the checked payload; a `.gz` path is gzip compressed."""
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    data = dumps(cset).encode()
+    path.write_bytes(gzip.compress(data, mtime=0) if path.suffix == ".gz" else data)
+    return path
+
+
+def read(path: Path) -> cs.CorrectionSet:
+    data = Path(path).read_bytes()
+    return cs.CorrectionSet.model_validate_json(gzip.decompress(data) if Path(path).suffix == ".gz" else data)
diff --git a/tests/test_payloads.py b/tests/test_payloads.py
new file mode 100644
index 0000000..dd4b41c
--- /dev/null
+++ b/tests/test_payloads.py
@@ -0,0 +1,45 @@
+import gzip
+import json
+
+import correctionlib
+import numpy as np
+import pytest
+
+from shapesmith import payloads
+
+
+def _payload():
+    ff = payloads.binning("pt_2", np.array([30.0, 40.0, 150.0]), np.array([0.2, 0.15]))
+    data = payloads.category("syst", {"StatShiftUp": payloads.binning("pt_2", [30.0, 150.0], [0.25])}, default=ff)
+    by_dm = payloads.category("dm", {0: 1.1, 1: 0.9}, default=1.0)
+    corrections = [
+        payloads.correction("QCD_fake_factors", [payloads.variable("pt_2"), payloads.variable("syst", "string")], data, version=2),
+        payloads.correction("tau_sf", [payloads.variable("dm", "int", "decay mode")], by_dm, description="per decay mode"),
+    ]
+    return payloads.correction_set(corrections, {"analysis": "mini", "era": "2018"})
+
+
+def test_payload_evaluates_and_carries_its_provenance(tmp_path):
+    path = payloads.write(_payload(), tmp_path / "out" / "fake_factors_mt.json.gz")
+    text = gzip.decompress(path.read_bytes()).decode()
+    assert "$schema" not in json.loads(text)
+    evaluator = correctionlib.CorrectionSet.from_string(text)
+    assert evaluator["QCD_fake_factors"].evaluate(35.0, "nominal") == pytest.approx(0.2)  # the default is the nominal
+    assert evaluator["QCD_fake_factors"].evaluate(500.0, "nominal") == pytest.approx(0.15)  # clamped
+    assert evaluator["QCD_fake_factors"].evaluate(35.0, "StatShiftUp") == pytest.approx(0.25)
+    assert evaluator["tau_sf"].evaluate(1) == pytest.approx(0.9) and evaluator["tau_sf"].evaluate(10) == 1.0
+    back = payloads.read(path)
+    assert payloads.provenance(back) == {"analysis": "mini", "era": "2018"}
+    assert back.corrections[0].version == 2 and back.corrections[1].inputs[0].description == "decay mode"
+
+
+def test_plain_json_is_written_uncompressed(tmp_path):
+    path = payloads.write(_payload(), tmp_path / "payload.json")
+    assert json.loads(path.read_text())["schema_version"] == 2
+
+
+def test_an_invalid_payload_is_rejected():
+    broken = payloads.correction("x", [payloads.variable("pt")], payloads.binning("pt", [0.0, 1.0], [1.0]))
+    broken.data.content.append(2.0)  # two values for one bin
+    with pytest.raises(Exception):
+        payloads.dumps(payloads.correction_set([broken], {}))

From a8f2c940f6ffc0a7af8c88e36c666efbea2e3a8a Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 01:37:47 +0200
Subject: [PATCH 06/33] feat(measurements): numpy port of TauFakeFactors kernel
 smoothing

smooth_kern transcribes ROOT's TGraphSmooth::SmoothKern (normal kernel,
BDRksmooth) including its running window start, which still sums the last
point left of the window. smooth ports FF_Updated _smooth_function: 100
Normal(y, stat_sigma * sigma) samples per bin from RandomState(19), the
100 n-point grid without its last point, mean and standard deviation over the
samples, binwise and hybrid options (_get_index_and_slices), clipping at zero
and padding to the outer bin edges.

tests/reference/make_smoothing_reference.py (run once with pyROOT from
LCG_108) calls ROOT and FF_Updated itself; the port matches its output to
5e-16 (kernel) and exactly (sampled curves).
---
 src/shapesmith/measurements/smoothing.py    |   118 +
 tests/reference/make_smoothing_reference.py |   102 +
 tests/reference/smoothing_root.json         | 36651 ++++++++++++++++++
 tests/test_smoothing.py                     |    56 +
 4 files changed, 36927 insertions(+)
 create mode 100644 src/shapesmith/measurements/smoothing.py
 create mode 100644 tests/reference/make_smoothing_reference.py
 create mode 100644 tests/reference/smoothing_root.json
 create mode 100644 tests/test_smoothing.py

diff --git a/src/shapesmith/measurements/smoothing.py b/src/shapesmith/measurements/smoothing.py
new file mode 100644
index 0000000..f2a1901
--- /dev/null
+++ b/src/shapesmith/measurements/smoothing.py
@@ -0,0 +1,118 @@
+"""Kernel smoothing of binned ratios as TauFakeFactors does it, without ROOT.
+
+The reference is FF_Updated `helper/ff_functions.py` (`_smooth_function`, `_get_index_and_slices`; sha256 31f89add...,
+the state that produced the SM 2018 payload): 100 samples per bin from Normal(y, stat_sigma * sigma) with seed 19,
+each smoothed with ROOT's TGraphSmooth::SmoothKern (normal kernel) on the grid linspace(x[start], x[end], 100 n + 1)
+without its last point; the nominal is the mean and the statistical band the standard deviation over the samples.
+Options: `binwise`, `smoothed`, and hybrids such as `binwise#[0]+smoothed` (the listed bins binwise).
+tests/reference/make_smoothing_reference.py checks the port against ROOT and FF_Updated.
+"""
+from __future__ import annotations
+
+import ast
+from dataclasses import dataclass
+
+import numpy as np
+
+KERNEL_SCALE = 0.3706506  # ROOT's normal kernel: quartiles at +-0.25 * bandwidth
+SAMPLES = 100
+SEED = 19
+
+
+@dataclass(frozen=True)
+class Curve:
+    """A piecewise constant function: `nominal[i]` on [edges[i], edges[i + 1]), with its statistical band."""
+
+    edges: np.ndarray
+    nominal: np.ndarray
+    up: np.ndarray
+    down: np.ndarray
+
+
+def smooth_kern(x: np.ndarray, y: np.ndarray, x_out: np.ndarray, bandwidth: float) -> np.ndarray:
+    """ROOT's TGraphSmooth::SmoothKern with the normal kernel (BDRksmooth), at the sorted points `x_out`.
+
+    A transcription of ROOT's loop, not a textbook kernel smoother: the window of an output point starts at a running
+    index that only moves forward and may still include the last point below x0 - cutoff (ROOT sums it), and ends at
+    the last point <= x0 + cutoff; where no point contributes, the result is 0. `y` may have a second axis (samples),
+    which is smoothed in one go.
+    """
+    x_out = np.asarray(x_out, dtype=np.float64)
+    if np.any(np.diff(x_out) < 0):
+        raise ValueError("x_out must be sorted")
+    order = np.argsort(x, kind="stable")
+    x, y = np.asarray(x, dtype=np.float64)[order], np.asarray(y, dtype=np.float64)[order]
+    width = KERNEL_SCALE * bandwidth
+    cutoff = 4 * width
+    result = np.zeros((len(x_out), *y.shape[1:]))
+    start = int(np.searchsorted(x, x_out[0] - cutoff, side="left"))
+    for j, x0 in enumerate(x_out):
+        end = int(np.searchsorted(x, x0 + cutoff, side="right"))
+        w = np.exp(-0.5 * (np.abs(x[start:end] - x0) / width) ** 2)
+        if w.sum() > 0:
+            result[j] = w @ y[start:end] / w.sum()
+        start = max(start, int(np.searchsorted(x, x0 - cutoff, side="left")) - 1)
+    return result
+
+
+def hybrid_slices(option: str) -> tuple[int, int, slice, slice, slice]:
+    """FF_Updated `_get_index_and_slices`: the first and last smoothed bin, and the slices of the binwise part on the
+    left, on the right and of the smoothed grid, for an option like `binwise#[0,]#[-1,]+smoothed`."""
+    start, end, left, right = 0, -1, False, False
+    if "binwise" in option and "smoothed" in option:
+        binwise = next(part for part in option.split("+") if "binwise" in part)
+        groups = [ast.literal_eval(group) for group in binwise.split("#")[1:]]
+        if len(groups) == 2:
+            start, end, left, right = groups[0][-1] + 1, groups[1][-1] - 1, True, True
+        elif all(index >= 0 for index in groups[0]):
+            start, left = groups[0][-1] + 1, True
+        elif all(index < 0 for index in groups[0]):
+            end, right = groups[0][-1] - 1, True
+        else:
+            raise ValueError(f"{option}: the binwise bins must be counted from one side")
+    return (
+        start,
+        end,
+        slice(None, start) if left else slice(0, 0),
+        slice(end + 1, None) if right else slice(-1, -1),
+        slice(None, -1) if right else slice(None, None),
+    )
+
+
+def smooth(x: np.ndarray, y: np.ndarray, errors: np.ndarray, bin_edges: np.ndarray, option: str, bandwidth: float, stat_sigma: float = 1.0) -> Curve:
+    """The binwise, smoothed or hybrid curve of a histogram with values `y`, symmetric `errors`, bin `x` positions
+    (the centres of mass) and `bin_edges`; negative values are set to 0, the curve covers the full bin range."""
+    x, y, errors, bin_edges = (np.asarray(a, dtype=np.float64) for a in (x, y, errors, bin_edges))
+    if option == "binwise":
+        return _clipped(bin_edges, y, y + stat_sigma * errors, y - stat_sigma * errors, bin_edges)
+    if "smoothed" not in option:
+        raise ValueError(f"unknown fit option {option!r}")
+    start, end, left, right, overlap = hybrid_slices(option)
+    rng = np.random.RandomState(SEED)  # the stream of TauFakeFactors' np.random.seed(19), without global state
+    samples = np.array([rng.normal(mu, stat_sigma * sigma, SAMPLES) for mu, sigma in zip(y, errors)])
+    samples[samples < 0.0] = 0.0
+    n = 100 * len(y)
+    grid = np.linspace(x[start], x[end], n + 1)
+    smoothed = smooth_kern(x, samples, grid[:n], bandwidth)
+    mean, std = smoothed.mean(axis=1), smoothed.std(axis=1)
+    if option == "smoothed":
+        return _clipped(grid, mean, mean + std, mean - std, bin_edges)
+    parts = [(bin_edges[left], y[left], stat_sigma * errors[left])]
+    if start != 0:  # the smoothed part begins at the lower edge of bin `start`, with its first value
+        parts.append((bin_edges[start : start + 1], mean[:1], std[:1]))
+    parts.append((grid[overlap], mean[overlap], std[overlap]))
+    if end != -1:  # and ends at the lower edge of bin `end + 1`, with its last value
+        parts.append((bin_edges[end : end + 1], mean[-1:], std[-1:]))
+    parts.append((bin_edges[right], y[right], stat_sigma * errors[right]))
+    edges, nominal, band = (np.concatenate(column) for column in zip(*parts))
+    return _clipped(edges, nominal, nominal + band, nominal - band, bin_edges)
+
+
+def _clipped(edges: np.ndarray, nominal: np.ndarray, up: np.ndarray, down: np.ndarray, bin_edges: np.ndarray) -> Curve:
+    """Negative values set to 0; the first and last value extended to the outer bin edges where the curve stops short."""
+    nominal, up, down = (np.where(values < 0, 0.0, values) for values in (nominal, up, down))
+    if edges[0] != bin_edges[0]:
+        edges, nominal, up, down = np.insert(edges, 0, bin_edges[0]), *(np.insert(v, 0, v[0]) for v in (nominal, up, down))
+    if edges[-1] != bin_edges[-1]:
+        edges, nominal, up, down = np.append(edges, bin_edges[-1]), *(np.append(v, v[-1]) for v in (nominal, up, down))
+    return Curve(edges, nominal, up, down)
diff --git a/tests/reference/make_smoothing_reference.py b/tests/reference/make_smoothing_reference.py
new file mode 100644
index 0000000..4209466
--- /dev/null
+++ b/tests/reference/make_smoothing_reference.py
@@ -0,0 +1,102 @@
+"""Write smoothing_root.json: the reference of tests/test_smoothing.py, computed once with ROOT (run manually).
+
+Needs pyROOT (LCG_108: `source /cvmfs/sft.cern.ch/lcg/views/LCG_108/x86_64-el9-gcc15-opt/setup.sh`) and jvoss's
+TauFakeFactors copy /work/jvoss/FF_Updated, whose `_smooth_function` produced the SM 2018 fake-factor payload:
+
+    python tests/reference/make_smoothing_reference.py
+
+- `kernel`: ROOT TGraphSmooth::SmoothKern(graph, "normal", bandwidth, nout, xout) directly, including gaps, points at
+  the cutoff and unsorted input. The TGraphSmooth object owns the output graph, so it is kept alive while reading.
+- `smooth`: FF_Updated `_smooth_function` on histograms with centre-of-mass bin positions, for every fit option.
+"""
+import array
+import hashlib
+import json
+import sys
+import types
+from pathlib import Path
+
+import numpy as np
+import ROOT
+
+FF_UPDATED = Path("/work/jvoss/FF_Updated")
+CUTOFF_1 = 4 * 0.3706506  # the kernel cutoff at bandwidth 1
+
+
+def root_smooth_kern(x, y, x_out, bandwidth, nout):
+    graph = ROOT.TGraph(len(x), array.array("d", x), array.array("d", y))
+    smoother = ROOT.TGraphSmooth("normal")
+    out = smoother.SmoothKern(graph, "normal", float(bandwidth), int(nout), array.array("d", x_out))
+    return [out.GetPointX(i) for i in range(out.GetN())], [out.GetPointY(i) for i in range(out.GetN())]
+
+
+def kernel_cases():
+    rng = np.random.default_rng(7)
+    cases = []
+    for _ in range(12):  # the TauFakeFactors use: 100 n grid points on [x_0, x_n-1], the last one dropped
+        n = int(rng.integers(2, 9))
+        x = np.sort(rng.uniform(0, 150, n))
+        cases.append((x, rng.uniform(0, 1, n), np.linspace(x[0], x[-1], 100 * n + 1), float(rng.choice([0.5, 3, 12, 20, 50])), 100 * n))
+    cases += [
+        ([0.0, 10.0], [1.0, 2.0], [0.0, 2.0, 5.0, 8.0, 9.9], 1.0, 5),  # a gap starting at a data point
+        ([0.0, 10.0], [1.0, 2.0], [5.0], 1.0, 1),  # an isolated output point in the gap
+        ([0.0, 100.0], [1.0, 2.0], np.linspace(0.0, 100.0, 11), 0.5, 11),  # exp underflow in a wide gap
+        ([0.0, 1.0, 2.0, 40.0, 41.0, 42.0], [1.0, 2.0, 3.0, 2.0, 1.0, 2.0], np.linspace(20.0, 42.0, 23), 2.0, 22),  # a gap in the first window
+        ([0.0, 100.0], [3.0, 5.0], [CUTOFF_1], 1.0, 1),  # a point exactly at x0 - cutoff
+        ([0.0, 100.0], [3.0, 5.0], [-CUTOFF_1], 1.0, 1),  # a point exactly at x0 + cutoff
+        ([35.0, 10.0, 60.0, 20.0], [1.1, 1.3, 0.9, 1.2], np.linspace(10.0, 60.0, 201), 12.0, 200),  # unsorted input
+    ]
+    result = []
+    for x, y, x_out, bandwidth, nout in cases:
+        x_root, y_root = root_smooth_kern(x, y, x_out, bandwidth, nout)
+        result.append({"x": list(map(float, x)), "y": list(map(float, y)), "bandwidth": bandwidth, "x_out": x_root, "y_out": y_root})
+    return result
+
+
+def smooth_cases():
+    wurlitzer = types.ModuleType("wurlitzer")  # imported by ff_functions for log capturing only
+    wurlitzer.STDOUT, wurlitzer.pipes = None, None
+    sys.modules["wurlitzer"] = wurlitzer
+    sys.path.insert(0, str(FF_UPDATED))
+    from helper.ff_functions import _smooth_function
+    from helper.hooks_and_patches import _EXTRA_PARAM_FLAG, _EXTRA_PARAM_MEANS
+
+    histograms = [  # (bin edges, centres of mass, values, errors, bandwidth)
+        ([30.0, 33.53, 38.0, 44.89, 58.39, 150.0], [31.7, 35.6, 41.2, 50.9, 72.5], [0.21, 0.19, 0.17, 0.15, 0.13], [0.01, 0.01, 0.012, 0.015, 0.02], 20.0),
+        ([30.0, 40.0, 60.0, 90.0, 150.0], [34.8, 48.1, 71.3, 105.0], [1.1, 0.95, 1.02, 0.7], [0.05, 0.06, 0.1, 0.4], 12.0),
+        ([0.0, 1.0, 2.0, 5.0, 10.0, 11.0], [0.5, 1.4, 3.2, 6.8, 10.5], [1.2, 0.9, 1.05, 0.02, 1.4], [0.3, 0.2, 0.25, 0.3, 0.9], 1.0),  # gaps, negative samples
+    ]
+    options = ["smoothed", "binwise", "binwise#[0]+smoothed", "binwise#[-1]+smoothed", "binwise#[0,]#[-1,]+smoothed"]
+    result = []
+    for edges, x, y, errors, bandwidth in histograms:
+        for option in options:
+            hist = ROOT.TH1D(f"h{len(result)}", "", len(y), array.array("d", edges))
+            for i, (value, error) in enumerate(zip(y, errors)):
+                hist.SetBinContent(i + 1, value)
+                hist.SetBinError(i + 1, error)
+            setattr(hist, _EXTRA_PARAM_FLAG, True)
+            setattr(hist, _EXTRA_PARAM_MEANS, list(x))
+            _, curve = _smooth_function(hist, edges, option, bandwidth, stat_sigma=1.0)
+            default = curve["default"]
+            result.append({
+                "bin_edges": edges, "x": x, "y": y, "errors": errors, "option": option, "bandwidth": bandwidth,
+                "edges": list(map(float, default["edges"])), "nominal": list(map(float, default["nominal"])),
+                "up": list(map(float, default["variations"]["StatUp"])), "down": list(map(float, default["variations"]["StatDown"])),
+            })
+    return result
+
+
+def main() -> None:
+    reference = {
+        "root_version": ROOT.gROOT.GetVersion(),
+        "ff_functions_sha256": hashlib.sha256((FF_UPDATED / "helper" / "ff_functions.py").read_bytes()).hexdigest(),
+        "kernel": kernel_cases(),
+        "smooth": smooth_cases(),
+    }
+    path = Path(__file__).with_name("smoothing_root.json")
+    path.write_text(json.dumps(reference, indent=0))
+    print(f"wrote {path}: {len(reference['kernel'])} kernel and {len(reference['smooth'])} smoothing cases")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/reference/smoothing_root.json b/tests/reference/smoothing_root.json
new file mode 100644
index 0000000..712769d
--- /dev/null
+++ b/tests/reference/smoothing_root.json
@@ -0,0 +1,36651 @@
+{
+"root_version": "6.36.02",
+"ff_functions_sha256": "31f89adda9847ebd1ca6f40689a61d47f7856eb6b00a623f1aeb2678b56eb0d6",
+"kernel": [
+{
+"x": [
+0.7897956848362087,
+33.781078498588776,
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+116.35285353677902,
+119.56041431280694,
+123.18426275741494,
+131.0330168094393,
+134.58207014543632
+],
+"y": [
+0.4679349528437208,
+0.3030324268193135,
+0.2784256121007733,
+0.2548695876541246,
+0.4450763058826466,
+0.5045482589579533,
+0.5534973520744925,
+0.9955002834343927
+],
+"bandwidth": 20.0,
+"x_out": [
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\ No newline at end of file
diff --git a/tests/test_smoothing.py b/tests/test_smoothing.py
new file mode 100644
index 0000000..3f34baa
--- /dev/null
+++ b/tests/test_smoothing.py
@@ -0,0 +1,56 @@
+"""The numpy smoothing against ROOT TGraphSmooth and TauFakeFactors (tests/reference/make_smoothing_reference.py)."""
+import json
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from shapesmith.measurements.smoothing import hybrid_slices, smooth, smooth_kern
+
+REFERENCE = json.loads((Path(__file__).parent / "reference" / "smoothing_root.json").read_text())
+
+
+def _close(a, b) -> bool:
+    a, b = np.asarray(a), np.asarray(b)
+    return a.shape == b.shape and bool(np.all(np.abs(a - b) <= 1e-12 * np.maximum(np.abs(b), 1e-300)))
+
+
+@pytest.mark.parametrize("case", REFERENCE["kernel"], ids=lambda c: f"n{len(c['x'])}_bw{c['bandwidth']}")
+def test_kernel_matches_root(case):
+    assert _close(smooth_kern(np.array(case["x"]), np.array(case["y"]), np.array(case["x_out"]), case["bandwidth"]), case["y_out"])
+
+
+@pytest.mark.parametrize("case", REFERENCE["smooth"], ids=lambda c: f"{c['option']}_bw{c['bandwidth']}_n{len(c['y'])}")
+def test_smoothing_matches_tau_fake_factors(case):
+    curve = smooth(case["x"], case["y"], case["errors"], case["bin_edges"], case["option"], case["bandwidth"])
+    for name in ("edges", "nominal", "up", "down"):
+        assert _close(getattr(curve, name), case[name]), name
+
+
+def test_the_kernel_sums_the_last_point_left_of_the_window_as_root_does():
+    x, y = np.array([0.0, 10.0]), np.array([1.0, 2.0])
+    assert smooth_kern(x, y, np.array([0.0, 2.0, 5.0]), 1.0).tolist() == [1.0, 1.0, 1.0]  # a textbook smoother gives 1, 0, 0
+    assert smooth_kern(x, y, np.array([5.0]), 1.0).tolist() == [0.0]  # nothing within reach of the first output point
+
+
+def test_the_kernel_smooths_all_samples_at_once():
+    x, samples = np.array([1.0, 2.0, 4.0]), np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
+    both = smooth_kern(x, samples, np.array([1.5, 3.0]), 2.0)
+    assert np.allclose(both[:, 1], smooth_kern(x, samples[:, 1], np.array([1.5, 3.0]), 2.0))
+    with pytest.raises(ValueError, match="sorted"):
+        smooth_kern(x, samples, np.array([3.0, 1.5]), 2.0)
+
+
+def test_hybrid_slices():
+    assert hybrid_slices("smoothed") == (0, -1, slice(0, 0), slice(-1, -1), slice(None, None))
+    assert hybrid_slices("binwise#[0,1]+smoothed") == (2, -1, slice(None, 2), slice(-1, -1), slice(None, None))
+    assert hybrid_slices("binwise#[-1]+smoothed") == (0, -2, slice(0, 0), slice(-1, None), slice(None, -1))
+    assert hybrid_slices("binwise#[0,]#[-1,]+smoothed") == (1, -2, slice(None, 1), slice(-1, None), slice(None, -1))
+    with pytest.raises(ValueError, match="one side"):
+        hybrid_slices("binwise#[0,-1]+smoothed")
+
+
+def test_binwise_keeps_the_bins_and_clips_at_zero():
+    curve = smooth([0.5, 1.5], [0.2, -0.1], [0.3, 0.05], [0.0, 1.0, 2.0], "binwise", 1.0)
+    assert curve.edges.tolist() == [0.0, 1.0, 2.0] and curve.nominal.tolist() == [0.2, 0.0]
+    assert curve.up.tolist() == pytest.approx([0.5, 0.0]) and curve.down.tolist() == [0.0, 0.0]

From af8c9a68d0ea76274d422d6267dc2fd20f16ce6e Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 01:39:02 +0200
Subject: [PATCH 07/33] test: cover unchanged_variations; drop an unused logger

---
 src/shapesmith/limits.py |  3 ---
 src/shapesmith/model.py  |  6 +++---
 tests/test_fill.py       |  1 -
 tests/test_histogram.py  | 11 ++++++++++-
 4 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/src/shapesmith/limits.py b/src/shapesmith/limits.py
index 3557029..8f9d93b 100644
--- a/src/shapesmith/limits.py
+++ b/src/shapesmith/limits.py
@@ -2,7 +2,6 @@
 from __future__ import annotations
 
 import json
-import logging
 from pathlib import Path
 
 import matplotlib
@@ -14,8 +13,6 @@
 from shapesmith import cmssw  # noqa: E402
 from shapesmith.config import RunConfig  # noqa: E402
 
-logger = logging.getLogger(__name__)
-
 QUANTILES = {0.025: "exp_m2", 0.16: "exp_m1", 0.5: "exp_median", 0.84: "exp_p1", 0.975: "exp_p2", -1.0: "observed"}
 COMMON = "-m {mass} --setParameterRanges r=-40,40 -t -1"
 
diff --git a/src/shapesmith/model.py b/src/shapesmith/model.py
index 043dfb3..649ba5d 100644
--- a/src/shapesmith/model.py
+++ b/src/shapesmith/model.py
@@ -8,15 +8,15 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Literal, Mapping
+from typing import TYPE_CHECKING, Literal, Mapping, get_args
 
 if TYPE_CHECKING:
     from shapesmith.measurements import Measurement
 
 SampleKind = Literal["data", "mc", "embedding"]
 Role = Literal["data", "signal", "background", "auxiliary"]  # auxiliary: booked for an estimator, never a datacard process
-SAMPLE_KINDS = ("data", "mc", "embedding")
-ROLES = ("data", "signal", "background", "auxiliary")
+SAMPLE_KINDS = get_args(SampleKind)
+ROLES = get_args(Role)
 NOMINAL = "nominal"
 
 
diff --git a/tests/test_fill.py b/tests/test_fill.py
index 9bcdb39..623b458 100644
--- a/tests/test_fill.py
+++ b/tests/test_fill.py
@@ -5,7 +5,6 @@
 
 from shapesmith.fill import bookings, resolve_regions, run_hist, targets
 from shapesmith.histogram import INCLUSIVE, HistKey, HistogramSet
-from shapesmith.model import Region
 from shapesmith.store import read_skims
 
 
diff --git a/tests/test_histogram.py b/tests/test_histogram.py
index 40808f7..c967832 100644
--- a/tests/test_histogram.py
+++ b/tests/test_histogram.py
@@ -2,7 +2,7 @@
 import pytest
 import uproot
 
-from shapesmith.histogram import Histogram
+from shapesmith.histogram import HistKey, Histogram, HistogramSet, unchanged_variations
 
 
 def test_fill_add_scale_sum():
@@ -39,3 +39,12 @@ def test_root_roundtrip_keeps_edges_and_errors(tmp_path):
         assert th.axis().edges().tolist() == [0.0, 0.2, 0.5, 1.0]
         back = Histogram.from_root(th)
     assert back.values.tolist() == [1.0, 2.0, 3.0] and back.variances.tolist() == [0.5, 1.0, 1.5] and back.edges.tolist() == [0.0, 0.2, 0.5, 1.0]
+
+
+def test_unchanged_variations():
+    hset = HistogramSet()
+    nominal = Histogram([0.0, 1.0], [2.0], [1.0])
+    hset[HistKey("mt", "inclusive", "EMB", "nominal", "Nominal", "m_vis")] = nominal
+    hset[HistKey("mt", "inclusive", "EMB", "nominal", "CMS_tesUp", "m_vis")] = nominal.copy()
+    hset[HistKey("mt", "inclusive", "EMB", "nominal", "CMS_tesDown", "m_vis")] = Histogram([0.0, 1.0], [1.9], [1.0])
+    assert unchanged_variations(hset) == [HistKey("mt", "inclusive", "EMB", "nominal", "CMS_tesUp", "m_vis")]

From 0d93d94df6062540ec1f4e46da88c59c8d7c72c2 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 01:39:38 +0200
Subject: [PATCH 08/33] docs: describe the rebuilt core in the README

---
 README.md | 137 +++++++++++++++++++++++++++++++++++++-----------------
 1 file changed, 94 insertions(+), 43 deletions(-)

diff --git a/README.md b/README.md
index 44e9106..bdb03ec 100644
--- a/README.md
+++ b/README.md
@@ -1,18 +1,18 @@
 # ShapeSmith
 
-From CROWN ntuples to histograms, background estimates, combine datacards, fits, plots and
-ML training folds. Columnar (uproot, pandas, numexpr, numpy), no ROOT needed except for
-`shapesmith fit`, which calls combine in a CMSSW subshell.
+From CROWN ntuples to histograms, background estimates, combine datacards, fits, plots,
+ML training folds and measurements (correctionlib payloads). Columnar (uproot, pandas,
+numexpr, numpy), no ROOT needed except for `shapesmith fit`, which calls combine in a CMSSW
+subshell.
 
-An analysis is a Python package that builds a `shapesmith.model.Analysis` (samples,
-selections, processes, regions, categories, variables, systematics, style) from a small run
+An analysis is a Python package that builds a `shapesmith.model.Analysis` from a small run
 YAML; ShapeSmith runs the steps. Example analysis:
 [BBTauTauAnalysis-ShapeSmith](https://github.com/KIT-CMS/BBTauTauAnalysis-ShapeSmith).
 
 ## Install
 
 ```bash
-source /cvmfs/sft.cern.ch/lcg/views/LCG_108/x86_64-el9-gcc15-opt/setup.sh   # Python 3.12, uproot, pandas, mplhep, XRootD
+source /cvmfs/sft.cern.ch/lcg/views/LCG_108/x86_64-el9-gcc15-opt/setup.sh   # Python 3.12, uproot, pandas, mplhep, correctionlib, XRootD
 python3 -m venv --system-site-packages ~/.venvs/shapesmith && source ~/.venvs/shapesmith/bin/activate
 pip install -e ".[test]"          # editable: changes in this checkout are live
 pytest                            # tests on synthetic ntuples, no network
@@ -21,47 +21,98 @@ pytest                            # tests on synthetic ntuples, no network
 ## Steps
 
 ```bash
-shapesmith example-config run.yaml            # annotated run configuration
-shapesmith validate  -c run.yaml              # build + validate the analysis, list the columns each channel needs
-shapesmith skim      -c run.yaml [--channels mt] [--samples TT,SingleMuon] [--workers 8]   # ntuples (+ friends) -> Parquet
-shapesmith hist      -c run.yaml [--control] [--regions all] [--skip-systematics] [--processes ztt,data]  # histograms (ROOT + JSON index)
-shapesmith estimate  -c run.yaml [--control]  # data-driven processes (fake factors / ABCD), embedding variations
+shapesmith validate  -c run.yaml              # build + validate the analysis, list processes and needed columns per channel
+shapesmith skim      -c run.yaml [--channels mt] [--samples TT,SingleMuon] [--force] [--workers 8]   # ntuples (+ friends) -> Parquet
+shapesmith hist      -c run.yaml [--control] [--regions all] [--skip-systematics] [--processes ZTT,data]  # histograms (ROOT + JSON index)
+shapesmith estimate  -c run.yaml [--control]  # estimated processes and variations (Channel.estimators)
+shapesmith measure   -c run.yaml [--suggest-binning]   # Analysis.measurement -> <output_dir>/<measurement>/<era>/
 shapesmith plot      -c run.yaml [--control] [--region nominal] [--blind] [--log]
 shapesmith sync      -c run.yaml              # combine-style shape files per channel
 shapesmith datacards -c run.yaml [--min-background 1.0] [--no-systematics]
-shapesmith fit       -c run.yaml [--final-states mt,all] [--skip-combine]
+shapesmith fit       -c run.yaml [--final-states mt,all] [--skip-combine]   # limits, significance, best fit
 shapesmith ml-export -c run.yaml              # Feather training folds
-shapesmith inspect   output/shapes.root       # what a histogram file contains
+shapesmith inspect   output/shapes.root [--unchanged]   # what a histogram file contains; variations equal to their nominal
 ```
 
-Two stages: `skim` reads the ntuples once (loose selection, all needed columns, normalisation
-weight `norm_weight = xsec / (nevents * generator_weight) * sign(genWeight)`) into
-`<skim_dir>/<channel>/<nick>/*.parquet` plus a `manifest.json`; everything else works on the
-skims. `skim`, `hist` and `ml-export` write a `versions.json` (versions, git hashes of core,
-analysis and sample database, full run configuration) next to their outputs.
-
-For named controls, `hist --regions same_sign,anti_iso` fills those regions, including
-signal contamination; `--regions all` expands each channel's defined regions plus nominal.
-Without this option the existing nominal/estimator booking applies. Use `--force` to refill
-an existing histogram file; partial refills preserve other regions, variables, channels and
-processes. `plot --region same_sign` reads that region and writes beneath
-`plots/<channel>/same_sign/`, with the region in the plot label. A nominal background
-estimate is never substituted into another region. Unblinded plots require actual data
-histograms; only explicit `--blind` uses Asimov data.
-
-A sample's own `cut` (e.g. one generator-level part of an inclusive sample) is applied at skim
-time on top of the loose selection; its `norm_weight` stays that of the whole sample. Only that
-sample reads the cut's columns and only its manifest records the cut, so the skims of the other
-samples stay reusable.
-
-Skim manifests record cuts, required columns and normalization. Reuse after changing this
-contract fails with `skim --force` guidance; legacy manifests without recorded cuts also
-require regeneration. The manifest is written before the first file of a sample and after
-every finished file (`completed`, with per-file event counts), so an interrupted skim resumes
-with only the unfinished files; `--force` starts the record afresh. Parquet files and manifests
-are replaced atomically through temporary files. A region weight that replaces a named
-baseline weight keeps its MC-only applicability. Newly added weights, such as a fake factor,
-also apply to data.
+`skim` reads the ntuples once into `<skim_dir>/<channel>/<nick>/*.parquet` plus a
+`manifest.json`; everything else works on the skims. `skim`, `hist`, `measure` and `ml-export`
+record what produced their outputs in `<directory>/versions/<analysis name>.json` (versions,
+git hashes of core, analysis and sample database, the full run configuration and its hash).
+
+## The analysis model
+
+Everything that differs by channel lives in its `Channel`: samples, cuts, processes, regions,
+categories, control variables, variations and estimators.
+
+- `Sample(nick, group, kind, xsec, nevents, generator_weight, cut)`: kind `data`, `mc` or
+  `embedding`; `norm_weight = xsec / (nevents * generator_weight) * sign(genWeight)` for MC.
+  A sample's own `cut` (e.g. one generator-level part of an inclusive sample) is applied at skim
+  time; its normalisation stays that of the whole sample.
+- `Process(name, group, role, plot_group, selection)`: role `data`, `signal`, `background` or
+  `auxiliary` (booked in the nominal region only, for an estimator; never in datacards, plots or
+  the ML export). A process owns its whole weight set.
+- `Region(name, replace_cuts, add_weights, replace_weights)`: cuts replaced by name, weights
+  replaced where a process carries them, weights added to every process.
+- `WeightVariation(name, replace_weights, applies_to, regions)` and
+  `ColumnVariation(name, suffix | derived, applies_to, regions)`: a column variation reads column
+  `c` as `c + suffix` where that branch exists (a CROWN shift), or replaces it by `derived[c]`, an
+  expression of nominal columns. Names ending in `Up`/`Down` need their partner and become
+  datacard shapes; other names are templates.
+- Estimators, run in order by `estimate`: `DataMinus(output, region, subtract, scale)`,
+  `ABCD(output, b, c, d, subtract)` and `TemplateShift(name, process, template, fraction)`.
+
+**The event rule** (`shapesmith.events`, used by hist, the ML export and measurements): cuts are
+the channel cuts with the region's replacements, the process cuts and the skim cuts; weights are
+the process weights with the region's replacements, then the region's added weights; a weight
+variation replaces weights in place (and does not apply to a process lacking one of them), a
+column variation rewrites every expression. Event weight: `(norm_weight * lumi) * product`,
+`lumi` for MC only.
+
+**Booking**: the signal in the nominal region, other processes in the nominal and the estimator
+regions (or `--regions`). A variation is filled where its `applies_to` and `regions` allow; a
+column variation only where it changes an expression of the histogram (elsewhere it equals the
+nominal). `DataMinus` builds its output for every column variation of data or a subtracted
+process, taking the nominal of inputs that lack it; weight variations are not propagated, ABCD
+is nominal only. `--skip-systematics` skips weight and column variations. `hist` always refills
+the requested scopes and keeps the other histograms of its file.
+
+## Skims
+
+Per sample, the skim keeps the columns of every expression its processes can use and the
+shifted branches of its column variations. An event is kept if the skim cuts pass nominally or
+under any column variation of its kind (hist re-applies the varied skim cuts). Where the channel
+declares CROWN shifts for a sample kind, every file must carry a shifted branch for each of them,
+and every shifted branch `c__X` of a needed column must be declared; otherwise the skim fails.
+
+The manifest records the skim contract. A stored skim is reused when its skim cuts,
+normalisation and sample cut are unchanged, its recorded column variations and friends contain
+the current ones, and its Parquet schema holds the needed columns; so one skim made with all
+variations and friends serves every subset. Incompatible skims are listed together, with the
+`skim --force --samples ...` fix. A new friend version gets a new friend base (a friend rewritten
+in place is not detected). The manifest is written before the first file of a sample and after
+every finished file, so an interrupted skim resumes with only the unfinished files. Parquet
+files and manifests are replaced atomically.
+
+## Samples
+
+`shapesmith.samples.read_sample_list` reads a KingMaker sample list copied unchanged (one nick per
+line); `normalisation(database, nicks)` looks every nick up in the KingMaker sample database
+(`datasets.json`) and reports all missing nicks at once. The database renames nicks now and
+then, so an older production needs the database checkout it was produced with.
+
+## Measurements
+
+`Analysis.measurement` is an object with a `name` and `run(context)`; `shapesmith measure` calls
+it with a `MeasureContext` (configuration, analysis, channels, output directory,
+`events(query)`, `provenance()`). Building blocks:
+
+- `shapesmith.payloads`: correctionlib schema-v2 builders; the provenance is JSON in
+  `CorrectionSet.description`; payloads are checked (schema, evaluator, no `$schema` key, so
+  correctionlib 2.6 reads them) and written gzip compressed.
+- `shapesmith.measurements.smoothing`: TauFakeFactors' kernel smoothing without ROOT, a port of
+  ROOT's `TGraphSmooth::SmoothKern` checked against ROOT and FF_Updated
+  (`tests/reference/make_smoothing_reference.py`).
+- `shapesmith.cmssw`: run commands in a CMSSW environment (`combine.cmssw_dir`).
 
 ## Run configuration
 
@@ -93,5 +144,5 @@ combine: {cmssw_dir: /work/USER/CMSSW_14_1_9, scram_arch: el9_amd64_gcc12}
 - `workers > 1` uses a spawn-based process pool (fork deadlocks with uproot/pyarrow threads).
   Scripts that call ShapeSmith functions with more than one worker therefore need the usual
   `if __name__ == "__main__":` guard; `--workers 1` runs everything inline for debugging.
-- Systematics are weight variations from ntuple columns plus lnN normalisation uncertainties;
-  NN scores and fake factors come from CROWN friend trees.
+- `tests/golden/` holds the mini-dataset outputs of core `aa5daa5`; the rebuilt core reproduces
+  them bitwise (`tests/test_golden.py`).

From d1364d9d48359bf476052ac667771b16a1b0732f Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 01:50:20 +0200
Subject: [PATCH 09/33] refactor(fill): plan a booking separately from filling
 it

---
 src/shapesmith/fill.py | 65 ++++++++++++++++++++++++++----------------
 1 file changed, 41 insertions(+), 24 deletions(-)

diff --git a/src/shapesmith/fill.py b/src/shapesmith/fill.py
index 0494ebe..9e8508e 100644
--- a/src/shapesmith/fill.py
+++ b/src/shapesmith/fill.py
@@ -10,6 +10,7 @@
 import logging
 from dataclasses import dataclass
 from pathlib import Path
+from typing import NamedTuple
 
 import numpy as np
 
@@ -86,50 +87,66 @@ def targets(channel: Channel, control: bool, variables: list[str] | None) -> lis
     return [Target(c.name, c.cut, c.variable) for c in channel.categories]
 
 
+class Fill(NamedTuple):
+    """The histograms of one region and variation of a booking: its cuts and weights, and per target the category
+    cut and the variable expression."""
+
+    region: str
+    variation: Variation | None
+    cuts: list[str]
+    weights: list[str]
+    targets: list[tuple[Target, str | None, str]]
+
+
 def _target_exprs(target: Target, variation: Variation | None, available: set[str]) -> tuple[str | None, str]:
     if not isinstance(variation, ColumnVariation):
         return target.cut, target.variable.expr
     return (shift(target.cut, variation, available) if target.cut else None), shift(target.variable.expr, variation, available)
 
 
-def fill_booking(config: RunConfig, analysis: Analysis, channel_name: str, booking: Booking, targets_: list[Target]) -> dict[HistKey, Histogram]:
-    """All histograms of one booking, from one frame of the process's skims."""
-    channel = analysis.channel(channel_name)
-    process = booking.process
-    nicks = [s.nick for s in channel.samples_of(process.group)]
-    available = schema(config.skim_dir, channel_name, nicks)
-    plan = []  # (region, variation, cuts, weights, [(target, cut, expression)])
+def plan_booking(channel: Channel, booking: Booking, targets_: list[Target], available: set[str]) -> list[Fill]:
+    """What to fill: every region with its applicable variations; a column variation only for the targets whose
+    cuts, weights, category or variable it changes."""
+    fills = []
     for region_name in booking.regions:
         region = channel.region(region_name)
-        nominal = select(channel, process, region)
+        nominal = select(channel, booking.process, region)
         for variation in (None, *(v for v in booking.variations if v.regions is None or region_name in v.regions)):
-            selection = select(channel, process, region, variation, available)
+            selection = select(channel, booking.process, region, variation, available)
             if selection is None:
-                logger.debug(f"{process.name}: variation {variation.name} not applicable, skipped")
+                logger.debug(f"{booking.process.name}: variation {variation.name} not applicable, skipped")
                 continue
             exprs = [(target, *_target_exprs(target, variation, available)) for target in targets_]
-            if isinstance(variation, ColumnVariation):
-                unchanged = selection == nominal
-                exprs = [(target, cut, expr) for target, cut, expr in exprs if not unchanged or (cut, expr) != (target.cut, target.variable.expr)]
+            if isinstance(variation, ColumnVariation) and selection == nominal:
+                exprs = [(target, cut, expr) for target, cut, expr in exprs if (cut, expr) != (target.cut, target.variable.expr)]
             if exprs:
-                plan.append((region_name, variation, *selection, exprs))
-    read = []
-    for _, _, cuts, weights, exprs in plan:
-        read += cuts + weights + [e for _, cut, expr in exprs for e in (cut, expr) if e]
-    frame = read_skims(config.skim_dir, channel_name, nicks, set(SKIM_COLUMNS) | columns_of(read))
+                fills.append(Fill(region_name, variation, *selection, exprs))
+    return fills
+
+
+def fill_booking(config: RunConfig, analysis: Analysis, channel_name: str, booking: Booking, targets_: list[Target]) -> dict[HistKey, Histogram]:
+    """All histograms of one booking, from one frame of the process's skims."""
+    channel = analysis.channel(channel_name)
+    process = booking.process
+    nicks = [s.nick for s in channel.samples_of(process.group)]
+    fills = plan_booking(channel, booking, targets_, schema(config.skim_dir, channel_name, nicks))
+    exprs = []
+    for f in fills:
+        exprs += f.cuts + f.weights + [e for _, cut, var in f.targets for e in (cut, var) if e]
+    frame = read_skims(config.skim_dir, channel_name, nicks, set(SKIM_COLUMNS) | columns_of(exprs))
     lumi_pb = lumi(analysis, channel, process)
     result: dict[HistKey, Histogram] = {}
-    for region_name, variation, cuts, weights, exprs in plan:
-        selected = frame[mask(frame, cuts)]
-        w = event_weights(selected, weights, lumi_pb)
-        for target, cut, expr in exprs:
+    for f in fills:
+        selected = frame[mask(frame, f.cuts)]
+        w = event_weights(selected, f.weights, lumi_pb)
+        for target, cut, expr in f.targets:
             in_category = mask(selected, [cut]) if cut else np.ones(len(selected), dtype=bool)
             values = evaluate(selected, expr).astype(np.float64)
             finite = np.isfinite(values) & np.isfinite(w)
             if (in_category & ~finite).any():
-                logger.warning(f"{process.name}/{region_name}/{target.variable.name}: {(in_category & ~finite).sum()} events with NaN/inf skipped")
+                logger.warning(f"{process.name}/{f.region}/{target.variable.name}: {(in_category & ~finite).sum()} events with NaN/inf skipped")
             keep = in_category & finite
-            key = HistKey(channel_name, target.category, process.name, region_name, variation.name if variation else NOMINAL_VARIATION, target.variable.name)
+            key = HistKey(channel_name, target.category, process.name, f.region, f.variation.name if f.variation else NOMINAL_VARIATION, target.variable.name)
             result[key] = Histogram.fill(target.variable.edges, values[keep], w[keep])
     return result
 

From 761c1d9f0066cc74214bbcd57be7f7eec0bce59d Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 06:49:23 +0200
Subject: [PATCH 10/33] feat(estimates): template shifts on every template
 variation; DataMinus can clip negative bins

A TemplateShift now also varies each template variation of its process (a
variation without a direction, e.g. an energy-scale grid point). The result
is named <variation>@<template>, which datacards never read as a shape. The
tau-ID/ES measurement needs this for the embedding ttbar contamination of
every grid point. DataMinus gains clip_negative for the same-sign QCD of the
measurement, as in the predecessor's estimate.
---
 src/shapesmith/estimates.py | 19 ++++++++++++-------
 src/shapesmith/histogram.py | 12 ++++++++++++
 src/shapesmith/model.py     |  7 +++++--
 tests/test_estimates.py     | 22 ++++++++++++++++++++++
 tests/test_histogram.py     |  8 +++++++-
 5 files changed, 58 insertions(+), 10 deletions(-)

diff --git a/src/shapesmith/estimates.py b/src/shapesmith/estimates.py
index 548ded7..6dc4040 100644
--- a/src/shapesmith/estimates.py
+++ b/src/shapesmith/estimates.py
@@ -2,15 +2,16 @@
 
 DataMinus: data minus the subtracted processes in its region, for the nominal and every column variation filled on
 data or a subtracted process there; an input without that variation contributes its nominal (under the fill rule it
-is unchanged). Weight variations are not propagated.
+is unchanged). Weight variations are not propagated. Optionally negative bins are clipped.
 ABCD: (data - MC)(B) * (data - MC)(C).sum() / (data - MC)(D).sum(), nominal only, negative bins clipped.
-TemplateShift: `process` +/- `fraction` * `template`, e.g. the ttbar contamination of the embedded sample.
+TemplateShift: `process` +/- `fraction` * `template`, e.g. the ttbar contamination of the embedded sample; also on top of
+every template variation of `process`.
 """
 from __future__ import annotations
 
 import logging
 
-from shapesmith.histogram import NOMINAL_VARIATION, HistKey, Histogram, HistogramSet
+from shapesmith.histogram import NOMINAL_VARIATION, HistKey, Histogram, HistogramSet, is_template, on_template
 from shapesmith.model import ABCD, NOMINAL, Analysis, Channel, ColumnVariation, DataMinus, TemplateShift
 
 logger = logging.getLogger(__name__)
@@ -50,9 +51,9 @@ def estimate_data_minus(hset: HistogramSet, channel: Channel, estimator: DataMin
     for category, variable in categories_and_variables(hset, channel, estimator.region):
         present = {k.variation for process in inputs for k in hset.select(channel=channel.name, category=category, process=process, region=estimator.region, variable=variable)}
         for variation in (NOMINAL_VARIATION, *sorted(present & column_variations)):
-            h = data_minus(hset, channel, category, variable, estimator.region, estimator.subtract, variation)
+            h = data_minus(hset, channel, category, variable, estimator.region, estimator.subtract, variation).scale(estimator.scale)
             key = HistKey(channel.name, category, estimator.output, NOMINAL, variation, variable)
-            hset[key] = h.scale(estimator.scale)
+            hset[key] = clip_negative_bins(h) if estimator.clip_negative else h
             added.append(key)
     return added
 
@@ -92,13 +93,17 @@ def estimate_abcd(hset: HistogramSet, channel: Channel, estimator: ABCD) -> list
 
 def estimate_template_shift(hset: HistogramSet, channel: Channel, estimator: TemplateShift) -> list[HistKey]:
     added = []
-    for key in hset.select(channel=channel.name, process=estimator.process, region=NOMINAL, variation=NOMINAL_VARIATION):
+    for key in hset.select(channel=channel.name, process=estimator.process, region=NOMINAL):
+        if key.variation != NOMINAL_VARIATION and not is_template(key.variation):
+            continue
         template = HistKey(channel.name, key.category, estimator.template, NOMINAL, NOMINAL_VARIATION, key.variable)
         if template not in hset:
             logger.warning(f"{template.path} missing, no {estimator.name} variation")
             continue
         for direction, sign in (("Up", 1.0), ("Down", -1.0)):
-            varied = HistKey(channel.name, key.category, estimator.process, NOMINAL, f"{estimator.name}{direction}", key.variable)
+            name = f"{estimator.name}{direction}"
+            variation = name if key.variation == NOMINAL_VARIATION else on_template(name, key.variation)
+            varied = HistKey(channel.name, key.category, estimator.process, NOMINAL, variation, key.variable)
             hset[varied] = hset[key].copy().add(hset[template], sign * estimator.fraction)
             added.append(varied)
     return added
diff --git a/src/shapesmith/histogram.py b/src/shapesmith/histogram.py
index d051ddf..a4a9a12 100644
--- a/src/shapesmith/histogram.py
+++ b/src/shapesmith/histogram.py
@@ -17,6 +17,18 @@
 
 NOMINAL_VARIATION = "Nominal"
 INCLUSIVE = "inclusive"  # the category of control-variable histograms
+TEMPLATE_SEPARATOR = "@"
+
+
+def is_template(variation: str) -> bool:
+    """A variation without a direction (e.g. one point of an energy-scale grid), not one on top of a template."""
+    return variation != NOMINAL_VARIATION and not variation.endswith(("Up", "Down")) and TEMPLATE_SEPARATOR not in variation
+
+
+def on_template(variation: str, template: str) -> str:
+    """The name of `variation` applied on top of the template variation `template`. It ends in the template's name,
+    so datacards never read it as a shape."""
+    return f"{variation}{TEMPLATE_SEPARATOR}{template}"
 
 
 @dataclass
diff --git a/src/shapesmith/model.py b/src/shapesmith/model.py
index 649ba5d..a276593 100644
--- a/src/shapesmith/model.py
+++ b/src/shapesmith/model.py
@@ -134,12 +134,14 @@ def __post_init__(self):
 
 @dataclass(frozen=True)
 class DataMinus:
-    """output = scale * (data - sum of `subtract`) in `region`, per column variation of its inputs."""
+    """output = scale * (data - sum of `subtract`) in `region`, per column variation of its inputs; with
+    `clip_negative`, negative bins are set to zero keeping the integral."""
 
     output: str
     region: str
     subtract: tuple[str, ...]
     scale: float = 1.0
+    clip_negative: bool = False
 
 
 @dataclass(frozen=True)
@@ -155,7 +157,8 @@ class ABCD:
 
 @dataclass(frozen=True)
 class TemplateShift:
-    """Variation `name`Up/Down of `process`: its nominal plus/minus `fraction` times the `template` process."""
+    """Variation `name`Up/Down of `process`: its nominal plus/minus `fraction` times the (nominal) `template` process.
+    Every template variation of `process` (e.g. an energy-scale grid point) gets the same variation on top of it."""
 
     name: str
     process: str
diff --git a/tests/test_estimates.py b/tests/test_estimates.py
index 2bee7fd..b6e6f36 100644
--- a/tests/test_estimates.py
+++ b/tests/test_estimates.py
@@ -85,3 +85,25 @@ def test_estimate_skips_categories_without_data_input():
     hset = HistogramSet()
     hset[_key("ZTT", "anti_iso")] = _hist([1.0, 2.0])
     assert run_estimates(hset, build(), ["mt"]) == []
+
+
+def test_data_minus_can_clip_negative_bins():
+    analysis = build()
+    estimator = dataclasses.replace(analysis.channel("mt").estimators[0], clip_negative=True)
+    hset = HistogramSet()
+    hset[_key("data", "anti_iso")] = _hist([2.0, 10.0])
+    hset[_key("ZTT", "anti_iso")] = _hist([3.0, 1.0])
+    run_estimates(hset, _with_estimators(analysis, estimator), ["mt"])
+    assert hset[_key("jetFakes", "nominal")].values.tolist() == pytest.approx([0.0, 8.0])  # (-1, 9) keeps the integral 8
+
+
+def test_template_shift_also_shifts_every_template_variation():
+    hset = HistogramSet()
+    hset[_key("EMB", "nominal")] = _hist([10.0, 10.0])
+    hset[_key("EMB", "nominal", variation="es+2")] = _hist([11.0, 9.0])
+    hset[_key("EMB", "nominal", variation="CMS_tesUp")] = _hist([12.0, 8.0])  # a shape variation: not shifted
+    hset[_key("ZTT", "nominal")] = _hist([2.0, 4.0])
+    run_estimates(hset, build_embedding(), ["mt"])
+    assert hset[_key("EMB", "nominal", variation="CMS_htt_emb_ttbar_2018Up@es+2")].values.tolist() == pytest.approx([11.2, 9.4])
+    assert hset[_key("EMB", "nominal", variation="CMS_htt_emb_ttbar_2018Down@es+2")].values.tolist() == pytest.approx([10.8, 8.6])
+    assert not hset.select(process="EMB", variation="CMS_htt_emb_ttbar_2018Up@CMS_tesUp")
diff --git a/tests/test_histogram.py b/tests/test_histogram.py
index c967832..0b09a43 100644
--- a/tests/test_histogram.py
+++ b/tests/test_histogram.py
@@ -2,7 +2,7 @@
 import pytest
 import uproot
 
-from shapesmith.histogram import HistKey, Histogram, HistogramSet, unchanged_variations
+from shapesmith.histogram import HistKey, Histogram, HistogramSet, is_template, on_template, unchanged_variations
 
 
 def test_fill_add_scale_sum():
@@ -48,3 +48,9 @@ def test_unchanged_variations():
     hset[HistKey("mt", "inclusive", "EMB", "nominal", "CMS_tesUp", "m_vis")] = nominal.copy()
     hset[HistKey("mt", "inclusive", "EMB", "nominal", "CMS_tesDown", "m_vis")] = Histogram([0.0, 1.0], [1.9], [1.0])
     assert unchanged_variations(hset) == [HistKey("mt", "inclusive", "EMB", "nominal", "CMS_tesUp", "m_vis")]
+
+
+def test_template_names():
+    assert is_template("es+2") and is_template("es-198")
+    assert not is_template("Nominal") and not is_template("CMS_tesUp") and not is_template("CMS_tesDown")
+    assert on_template("CMS_ttbarUp", "es-2") == "CMS_ttbarUp@es-2" and not is_template("CMS_ttbarUp@es-2")

From 37226936ec5a21843ce85b915e44945e34e6e9f2 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 06:51:13 +0200
Subject: [PATCH 11/33] feat(measure): --merge combines earlier results;
 payload transform node

MeasureContext.merge asks a measurement to combine the results of its earlier
runs (the tau-ID/ES measurement runs once per working-point combination) into
one payload. payloads.transform builds the formula transform the tau energy
scale payload uses for |eta|.
---
 src/shapesmith/cli.py                   | 4 ++--
 src/shapesmith/measurements/__init__.py | 5 +++--
 src/shapesmith/payloads.py              | 8 +++++++-
 tests/test_measure.py                   | 5 +++++
 tests/test_payloads.py                  | 7 +++++++
 5 files changed, 24 insertions(+), 5 deletions(-)

diff --git a/src/shapesmith/cli.py b/src/shapesmith/cli.py
index 2abe6e1..9301b89 100644
--- a/src/shapesmith/cli.py
+++ b/src/shapesmith/cli.py
@@ -114,12 +114,12 @@ def estimate(config: Path = ConfigOption, channels: Optional[str] = ChannelsOpti
 
 
 @app.command()
-def measure(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption, suggest_binning: bool = typer.Option(False, "--suggest-binning", help="print proposed bin edges instead of measuring"), workers: Optional[int] = WorkersOption):
+def measure(config: Path = ConfigOption, channels: Optional[str] = ChannelsOption, overrides: Optional[list[str]] = SetOption, suggest_binning: bool = typer.Option(False, "--suggest-binning", help="print proposed bin edges instead of measuring"), merge: bool = typer.Option(False, "--merge", help="combine the results of the earlier runs (e.g. one per working point) into one payload"), workers: Optional[int] = WorkersOption):
     """Run the analysis's measurement; results in <output_dir>/<measurement>/<era>/."""
     from shapesmith.measurements import run_measure
 
     cfg, analysis, selected = _setup(config, channels, workers=workers, overrides=overrides)
-    typer.echo(f"measurement output: {run_measure(cfg, analysis, selected, suggest_binning)}")
+    typer.echo(f"measurement output: {run_measure(cfg, analysis, selected, suggest_binning, merge)}")
 
 
 @app.command()
diff --git a/src/shapesmith/measurements/__init__.py b/src/shapesmith/measurements/__init__.py
index e462034..07851b5 100644
--- a/src/shapesmith/measurements/__init__.py
+++ b/src/shapesmith/measurements/__init__.py
@@ -23,6 +23,7 @@ class MeasureContext:
     channels: list[str]
     output: Path
     suggest_binning: bool = False  # print proposed bin edges instead of measuring
+    merge: bool = False  # combine the results of earlier runs (e.g. one per switch combination) into one payload
 
     def events(self, query: Query, columns: Iterable[str] = ()) -> Events:
         return load(self.config, self.analysis, query, columns)
@@ -40,11 +41,11 @@ class Measurement(Protocol):
     def run(self, context: MeasureContext) -> None: ...
 
 
-def run_measure(config: RunConfig, analysis: Analysis, channels: list[str], suggest_binning: bool = False) -> Path:
+def run_measure(config: RunConfig, analysis: Analysis, channels: list[str], suggest_binning: bool = False, merge: bool = False) -> Path:
     if analysis.measurement is None:
         raise ValueError(f"analysis {analysis.name} defines no measurement")
     output = Path(config.output_dir) / analysis.measurement.name / analysis.era
     if not suggest_binning:
         write_versions(config, analysis.name, output)
-    analysis.measurement.run(MeasureContext(config, analysis, channels, output, suggest_binning))
+    analysis.measurement.run(MeasureContext(config, analysis, channels, output, suggest_binning, merge))
     return output
diff --git a/src/shapesmith/payloads.py b/src/shapesmith/payloads.py
index c8e7819..43c61d6 100644
--- a/src/shapesmith/payloads.py
+++ b/src/shapesmith/payloads.py
@@ -15,7 +15,7 @@
 import correctionlib.schemav2 as cs
 import numpy as np
 
-Node = cs.Binning | cs.Category | float
+Node = cs.Binning | cs.Category | cs.Transform | float
 
 
 def variable(name: str, type: str = "real", description: str | None = None) -> cs.Variable:
@@ -35,6 +35,12 @@ def category(input: str, content: Mapping[str | int, Node], default: Node | None
     return cs.Category(nodetype="category", input=input, content=items, **fields)
 
 
+def transform(input: str, expression: str, content: Node) -> cs.Transform:
+    """`content` evaluated with `input` replaced by the TFormula `expression` of it (x), e.g. "abs(x)"."""
+    rule = cs.Formula(nodetype="formula", expression=expression, parser="TFormula", variables=[input])
+    return cs.Transform(nodetype="transform", input=input, rule=rule, content=content)
+
+
 def correction(name: str, inputs: Sequence[cs.Variable], data: Node, version: int = 1, description: str | None = None, output: cs.Variable | None = None) -> cs.Correction:
     fields = {"description": description} if description is not None else {}
     return cs.Correction(name=name, version=version, inputs=list(inputs), output=output or variable("weight"), data=data, **fields)
diff --git a/tests/test_measure.py b/tests/test_measure.py
index 34a3250..31eb695 100644
--- a/tests/test_measure.py
+++ b/tests/test_measure.py
@@ -24,6 +24,9 @@ def run(self, context: MeasureContext) -> None:
         if context.suggest_binning:
             print(f"suggested: {result}")
             return
+        if context.merge:
+            (context.output / "merged.json").write_text(json.dumps(result))
+            return
         (context.output / "result.json").write_text(json.dumps({"yields": result, "provenance": context.provenance(channel="mt")}))
 
 
@@ -69,3 +72,5 @@ def test_measure_command(tmp_path):
     assert result.exit_code == 0 and "suggested" in result.output and not (tmp_path / "out" / "count_events").exists()
     assert runner.invoke(app, ["measure", "-c", str(config)]).exit_code == 0
     assert (tmp_path / "out" / "count_events" / "2018" / "result.json").exists()
+    assert runner.invoke(app, ["measure", "-c", str(config), "--merge"]).exit_code == 0
+    assert (tmp_path / "out" / "count_events" / "2018" / "merged.json").exists()
diff --git a/tests/test_payloads.py b/tests/test_payloads.py
index dd4b41c..45d61fb 100644
--- a/tests/test_payloads.py
+++ b/tests/test_payloads.py
@@ -43,3 +43,10 @@ def test_an_invalid_payload_is_rejected():
     broken.data.content.append(2.0)  # two values for one bin
     with pytest.raises(Exception):
         payloads.dumps(payloads.correction_set([broken], {}))
+
+
+def test_transform_node():
+    by_abs_eta = payloads.transform("eta", "abs(x)", payloads.binning("eta", [0.0, 1.5, 2.5], [1.0, 2.0]))
+    cset = payloads.correction_set([payloads.correction("es", [payloads.variable("eta")], by_abs_eta)], {})
+    evaluator = correctionlib.CorrectionSet.from_string(payloads.dumps(cset))
+    assert evaluator["es"].evaluate(-2.0) == 2.0 and evaluator["es"].evaluate(0.5) == 1.0

From f1c9ca7366c4e7a1aadf4f28da79c0366a2ec24f Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 06:59:30 +0200
Subject: [PATCH 12/33] fix(cmssw): run CMSSW commands in a clean environment

From an LCG view (the ShapeSmith environment) the subshell inherited
PYTHONPATH and LD_LIBRARY_PATH, and combineTool.py failed to import ROOT
(GLIBCXX mismatch between LCG_108 and el9_amd64_gcc12). The subshell now keeps
only PATH=/usr/bin:/bin, HOME, USER, LOGNAME, TMPDIR and X509_USER_PROXY.
---
 src/shapesmith/cmssw.py | 16 ++++++++++++++--
 tests/test_limits.py    |  7 +++++++
 2 files changed, 21 insertions(+), 2 deletions(-)

diff --git a/src/shapesmith/cmssw.py b/src/shapesmith/cmssw.py
index f1d40d7..34d3f29 100644
--- a/src/shapesmith/cmssw.py
+++ b/src/shapesmith/cmssw.py
@@ -1,7 +1,12 @@
-"""Run commands inside a CMSSW environment (combine, CombineHarvester) in a bash subshell."""
+"""Run commands inside a CMSSW environment (combine, CombineHarvester) in a bash subshell.
+
+The subshell starts from a clean environment: the Python environment ShapeSmith runs in (e.g. an LCG view) would
+otherwise shadow CMSSW's libraries and Python packages.
+"""
 from __future__ import annotations
 
 import logging
+import os
 import subprocess
 from pathlib import Path
 from typing import Sequence
@@ -26,8 +31,15 @@ def script(combine: CombineConfig, cwd: Path, commands: Sequence[str]) -> str:
     )
 
 
+KEPT_VARIABLES = ("HOME", "USER", "LOGNAME", "TMPDIR", "X509_USER_PROXY")
+
+
+def clean_environment() -> dict[str, str]:
+    return {"PATH": "/usr/bin:/bin", **{name: os.environ[name] for name in KEPT_VARIABLES if name in os.environ}}
+
+
 def run(commands: Sequence[str], combine: CombineConfig | None, cwd: Path) -> None:
     if combine is None:
         raise ValueError("the run configuration needs `combine` (cmssw_dir) to run CMSSW commands")
     logger.info(f"running {len(commands)} CMSSW commands in {cwd}")
-    subprocess.run(["bash", "-c", script(combine, cwd, commands)], check=True)
+    subprocess.run(["bash", "-c", script(combine, cwd, commands)], check=True, env=clean_environment())
diff --git a/tests/test_limits.py b/tests/test_limits.py
index 1d23698..762cf46 100644
--- a/tests/test_limits.py
+++ b/tests/test_limits.py
@@ -25,6 +25,13 @@ def test_cmssw_script_sets_up_the_environment_before_the_commands():
     assert lines[-3:] == ["cd /cards/mt", "echo one", "echo two"]
 
 
+def test_cmssw_commands_run_in_a_clean_environment(monkeypatch):
+    monkeypatch.setenv("PYTHONPATH", "/lcg/python")
+    monkeypatch.setenv("HOME", "/home/someone")
+    environment = cmssw.clean_environment()
+    assert "PYTHONPATH" not in environment and environment["HOME"] == "/home/someone" and environment["PATH"] == "/usr/bin:/bin"
+
+
 def test_cmssw_run_needs_a_combine_configuration(tmp_path):
     with pytest.raises(ValueError, match="needs `combine`"):
         cmssw.run(["true"], None, tmp_path)

From a322d957dda0cdcf736b0005d76ceefdde7699b0 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 07:02:37 +0200
Subject: [PATCH 13/33] feat(model): a column variation can be restricted to
 sample groups

CROWN produces some shifts only for some samples (the MET recoil shifts for
DY and W, the muon-fake tau ES for DY). ColumnVariation.groups restricts a
variation to the samples of those groups: only they read, check and fill it,
so the per-file shift check no longer fails on the other samples.
---
 src/shapesmith/fill.py     |  4 ++--
 src/shapesmith/model.py    | 11 ++++++++++-
 src/shapesmith/skim.py     | 24 ++++++++++++------------
 src/shapesmith/validate.py |  5 ++++-
 tests/test_validate.py     |  1 +
 tests/test_variations.py   | 10 ++++++++++
 6 files changed, 39 insertions(+), 16 deletions(-)

diff --git a/src/shapesmith/fill.py b/src/shapesmith/fill.py
index 9e8508e..0568538 100644
--- a/src/shapesmith/fill.py
+++ b/src/shapesmith/fill.py
@@ -18,7 +18,7 @@
 from shapesmith.events import event_weights, lumi, select
 from shapesmith.expressions import columns_of, evaluate, mask, shift
 from shapesmith.histogram import INCLUSIVE, NOMINAL_VARIATION, HistKey, Histogram, HistogramSet
-from shapesmith.model import ABCD, NOMINAL, Analysis, Channel, ColumnVariation, DataMinus, Process, Variable, Variation
+from shapesmith.model import ABCD, NOMINAL, Analysis, Channel, ColumnVariation, DataMinus, Process, Variable, Variation, applies
 from shapesmith.parallel import run_jobs
 from shapesmith.store import SKIM_COLUMNS, read_skims, schema
 
@@ -75,7 +75,7 @@ def bookings(channel: Channel, systematics: bool, regions: list[str] | None = No
         else:
             process_regions = tuple(dict.fromkeys((NOMINAL, *estimator_regions(channel))))
         kind = channel.kind_of(process)
-        variations = tuple(v for v in channel.variations if kind in v.applies_to) if systematics else ()
+        variations = tuple(v for v in channel.variations if applies(v, kind, process.group)) if systematics else ()
         result.append(Booking(process, process_regions, variations))
     return result
 
diff --git a/src/shapesmith/model.py b/src/shapesmith/model.py
index a276593..4054a88 100644
--- a/src/shapesmith/model.py
+++ b/src/shapesmith/model.py
@@ -117,13 +117,15 @@ def __post_init__(self):
 class ColumnVariation:
     """Columns read shifted: `c + suffix` wherever that branch exists (a CROWN shift), or `derived[c]`, an expression
     of nominal columns (a shift computed in ShapeSmith). A name ending in Up/Down is one side of a shape nuisance;
-    any other name is a template that datacards never turn into a shape line."""
+    any other name is a template that datacards never turn into a shape line. `groups` restricts it to the samples of
+    some groups, e.g. a CROWN shift produced for some samples only."""
 
     name: str
     suffix: str = ""
     derived: Mapping[str, str] = field(default_factory=dict)
     applies_to: tuple[SampleKind, ...] = ("mc",)
     regions: tuple[str, ...] | None = None  # None: every booked region where the rewrite changes an expression
+    groups: tuple[str, ...] | None = None  # None: the samples of every group of those kinds
 
     def __post_init__(self):
         object.__setattr__(self, "derived", _frozen(self.derived))
@@ -132,6 +134,13 @@ def __post_init__(self):
 Variation = WeightVariation | ColumnVariation
 
 
+def applies(variation: Variation, kind: str, group: str) -> bool:
+    """Whether a variation belongs to the samples of a kind and group."""
+    if kind not in variation.applies_to:
+        return False
+    return not isinstance(variation, ColumnVariation) or variation.groups is None or group in variation.groups
+
+
 @dataclass(frozen=True)
 class DataMinus:
     """output = scale * (data - sum of `subtract`) in `region`, per column variation of its inputs; with
diff --git a/src/shapesmith/skim.py b/src/shapesmith/skim.py
index 912ccc8..74c4d8d 100644
--- a/src/shapesmith/skim.py
+++ b/src/shapesmith/skim.py
@@ -6,8 +6,8 @@
 they exist.
 Rows: an event is kept if the skim cuts pass nominally or under a column variation of its kind; then the sample cut
 applies (the normalisation stays that of the whole sample).
-Checks, per file, where the channel declares CROWN shifts for the sample kind: every declared shift has a shifted
-branch, and every shifted branch `c__X` of a needed column has a declared suffix `__X`.
+Checks, per file, where the channel declares CROWN shifts for the sample (its kind and group): every declared shift
+has a shifted branch, and every shifted branch `c__X` of a needed column has a declared suffix `__X`.
 Reuse: a stored skim serves when its skim cuts, normalisation and sample cut are equal, its column variations and
 friends contain the current ones and its Parquet schema holds the needed columns; see reuse_problems.
 """
@@ -25,7 +25,7 @@
 from shapesmith.config import RunConfig
 from shapesmith.events import select
 from shapesmith.expressions import columns_in, columns_of, mask, shift
-from shapesmith.model import NOMINAL_REGION, Analysis, Channel, ColumnVariation, Sample, WeightVariation
+from shapesmith.model import NOMINAL_REGION, Analysis, Channel, ColumnVariation, Sample, WeightVariation, applies
 from shapesmith.ntuples import NtupleFile, discover, friend_bases, read_ntuple
 from shapesmith.parallel import run_jobs
 from shapesmith.store import SKIM_COLUMNS, manifest_path, read_manifest, schema, skim_path, write_manifest, write_skim
@@ -46,8 +46,8 @@ class SkimResult:
     skipped: bool = False  # reused from a previous run; the counts come from its manifest record
 
 
-def column_variations(channel: Channel, kind: str) -> list[ColumnVariation]:
-    return [v for v in channel.variations if isinstance(v, ColumnVariation) and kind in v.applies_to]
+def column_variations(channel: Channel, sample: Sample) -> list[ColumnVariation]:
+    return [v for v in channel.variations if isinstance(v, ColumnVariation) and applies(v, sample.kind, sample.group)]
 
 
 def needed_columns(channel: Channel, sample: Sample) -> set[str]:
@@ -55,7 +55,7 @@ def needed_columns(channel: Channel, sample: Sample) -> set[str]:
     weight_variations = [v for v in channel.variations if isinstance(v, WeightVariation) and sample.kind in v.applies_to]
     exprs = [*channel.skim.values(), *(c.cut for c in channel.categories), *(c.variable.expr for c in channel.categories)]
     exprs += [variable.expr for variable in channel.variables.values()]
-    exprs += [expr for variation in column_variations(channel, sample.kind) for expr in variation.derived.values()]
+    exprs += [expr for variation in column_variations(channel, sample) for expr in variation.derived.values()]
     for process in channel.processes:
         if process.group != sample.group:
             continue
@@ -72,9 +72,9 @@ def needed_columns(channel: Channel, sample: Sample) -> set[str]:
     return columns
 
 
-def check_shifts(channel: Channel, kind: str, columns: set[str], branches: set[str]) -> None:
-    """Both directions of the declared CROWN shifts, on one file; nothing is checked for a kind without any."""
-    declared = {v.suffix: v.name for v in column_variations(channel, kind) if v.suffix}
+def check_shifts(channel: Channel, sample: Sample, columns: set[str], branches: set[str]) -> None:
+    """Both directions of the declared CROWN shifts, on one file; nothing is checked for a sample without any."""
+    declared = {v.suffix: v.name for v in column_variations(channel, sample) if v.suffix}
     if not declared:
         return
     problems = [f"declared shift {name} ({suffix}) has no shifted branch" for suffix, name in declared.items() if not any(c + suffix in branches for c in columns)]
@@ -87,10 +87,10 @@ def check_shifts(channel: Channel, kind: str, columns: set[str], branches: set[s
 def skim_one(ntuple: NtupleFile, sample: Sample, channel: Channel, columns: set[str], out_path: Path, optional: set[str] = frozenset()) -> SkimResult:
     """Read one file, keep the events that pass the skim cuts under any variation and the sample cut, add the
     bookkeeping columns and write Parquet. Every column in `columns` must exist; `optional` ones may be absent."""
-    variations = column_variations(channel, sample.kind)
+    variations = column_variations(channel, sample)
     shifted = {column + v.suffix for v in variations if v.suffix for column in columns}
     frame, metadata, branches = read_ntuple(ntuple, columns | shifted, set(optional) | shifted)
-    check_shifts(channel, sample.kind, columns, branches)
+    check_shifts(channel, sample, columns, branches)
     n_in = len(frame)
     nominal = list(channel.skim.values())
     selected = mask(frame, nominal)
@@ -134,7 +134,7 @@ def contract(config: RunConfig, channel: Channel, sample: Sample, columns: set[s
     result = {"selection": dict(channel.skim), "required_columns": sorted(columns), "normalisation": normalisation(sample)}
     if sample.cut is not None:
         result["sample_cut"] = sample.cut
-    variations = {v.name: v.suffix or dict(v.derived) for v in column_variations(channel, sample.kind)}
+    variations = {v.name: v.suffix or dict(v.derived) for v in column_variations(channel, sample)}
     if variations:
         result["column_variations"] = variations
     friends = friend_bases(config.ntuples, sample.kind)
diff --git a/src/shapesmith/validate.py b/src/shapesmith/validate.py
index fe42697..68248f8 100644
--- a/src/shapesmith/validate.py
+++ b/src/shapesmith/validate.py
@@ -1,7 +1,7 @@
 """Consistency checks of an Analysis; every problem is collected and reported at once."""
 from __future__ import annotations
 
-from shapesmith.model import NOMINAL, ROLES, SAMPLE_KINDS, Analysis, AnalysisError, Channel, DataMinus, TemplateShift, WeightVariation
+from shapesmith.model import NOMINAL, ROLES, SAMPLE_KINDS, Analysis, AnalysisError, Channel, ColumnVariation, DataMinus, TemplateShift, WeightVariation
 
 
 def _duplicates(what: str, names: list[str]) -> list[str]:
@@ -43,6 +43,7 @@ def _region_problems(channel: Channel) -> list[str]:
 
 def _variation_problems(channel: Channel, region_names: set[str]) -> list[str]:
     weights = {name for p in channel.processes for name in p.selection.weights}
+    groups = {s.group for s in channel.samples}
     names = [v.name for v in channel.variations]
     problems = _duplicates("variation", names)
     for variation in channel.variations:
@@ -51,6 +52,8 @@ def _variation_problems(channel: Channel, region_names: set[str]) -> list[str]:
             problems.append(f"variation {variation.name}: its partner {partner} is missing")
         problems += [f"variation {variation.name}: unknown sample kind {k}" for k in variation.applies_to if k not in SAMPLE_KINDS]
         problems += [f"variation {variation.name}: unknown region {r}" for r in variation.regions or () if r not in region_names]
+        if isinstance(variation, ColumnVariation):
+            problems += [f"variation {variation.name}: unknown sample group {g}" for g in variation.groups or () if g not in groups]
         if isinstance(variation, WeightVariation):
             problems += [f"variation {variation.name}: replaces unknown weight {w}" for w in variation.replace_weights if w not in weights]
         elif bool(variation.suffix) == bool(variation.derived):
diff --git a/tests/test_validate.py b/tests/test_validate.py
index ec387b2..d2c6f4d 100644
--- a/tests/test_validate.py
+++ b/tests/test_validate.py
@@ -67,6 +67,7 @@ def test_validate_checks_variations():
         ((model.ColumnVariation("xUp", "__xUp", {"m_vis": "m_vis"}), model.ColumnVariation("xDown", "")), "needs exactly one of suffix and derived"),
         ((model.ColumnVariation("CMS_puUp", "__puUp"),), "duplicate variation CMS_puUp"),
         ((model.ColumnVariation("aUp", "__aUp", applies_to=("simulation",)), model.ColumnVariation("aDown", "__aDown")), "unknown sample kind simulation"),
+        ((model.ColumnVariation("bUp", "__bUp", groups=("TT",)), model.ColumnVariation("bDown", "__bDown")), "unknown sample group TT"),
     ):
         with pytest.raises(model.AnalysisError, match=problem):
             validate(_with_channel(build(), variations=channel.variations + variations))
diff --git a/tests/test_variations.py b/tests/test_variations.py
index e31805a..b7d8772 100644
--- a/tests/test_variations.py
+++ b/tests/test_variations.py
@@ -79,6 +79,16 @@ def test_a_declared_shift_without_branches_fails_the_skim(config):
         run_skim(config, analysis)
 
 
+def test_a_shift_restricted_to_groups_belongs_to_their_samples_only(config):
+    analysis = build_embedding()
+    jes = tuple(ColumnVariation(f"CMS_jes{d}", f"__jes{d}", groups=("HH",)) for d in ("Up", "Down"))
+    channel = dataclasses.replace(analysis.channel("mt"), variations=analysis.channel("mt").variations + jes)
+    assert [b.process.name for b in bookings(channel, True) if jes[0] in b.variations] == ["HH"]
+    with pytest.raises(RuntimeError, match=r"declared shift CMS_jesUp \(__jesUp\) has no shifted branch") as excinfo:
+        run_skim(config, dataclasses.replace(analysis, channels={"mt": channel}))
+    assert "SIG_1" in str(excinfo.value) and "ZTT_1" not in str(excinfo.value)
+
+
 def test_an_undeclared_shift_branch_fails_the_skim_of_kinds_with_declared_shifts(config):
     with pytest.raises(RuntimeError, match=r"ZTT_1_0.root: undeclared shift __ffStatDown; undeclared shift __ffStatUp") as excinfo:
         run_skim(config, _analysis(keep=TES))

From 287757e26acc73aa6c98fc88287d144cbb609475 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 07:19:39 +0200
Subject: [PATCH 14/33] feat(measurements): tau_id_es, the tau-ID/ES
 measurement of embedded taus

The chain of smhtt_ul tauID_SFs_dev on ShapeSmith histograms, one run per
working-point combination: histograms and estimates of the mt channel and the
mm control region, the shapes in the MorphingTauID2017 input format, and per
category in CMSSW the datacards with the r_DY_incl rate parameters, the
multiSignalModel workspace, the 2D likelihood scan and the MultiDimFit singles
fit with the predecessor's options. The singles fit starts at the scan's best
fit, inside the region the scan gives (the close-up scans are gone). Results
with an interval at the fit range or a scan region at the scan boundary are
errors. --merge writes the correctionlib payload with the node structure of
the predecessor's file plus provenance. The energy-scale grid is in integer
units of 0.1 % (grid.py), so variation names, percentages and MorphingTauID2017
mass strings correspond exactly.

Reference tests (skipped without /work/jvoss): the payload built from the
predecessor's 36 singles fits equals its payload, the signal names equal those
of its shapes, and MorphingTauID2017 on its shapes writes its cards.
---
 README.md                                     |  34 +++-
 .../measurements/tau_id_es/__init__.py        | 140 +++++++++++++
 .../measurements/tau_id_es/combine.py         | 181 +++++++++++++++++
 src/shapesmith/measurements/tau_id_es/grid.py |  54 +++++
 .../measurements/tau_id_es/payload.py         | 114 +++++++++++
 .../measurements/tau_id_es/plots.py           | 101 ++++++++++
 .../measurements/tau_id_es/synced.py          |  50 +++++
 tests/test_tau_id_es.py                       | 190 ++++++++++++++++++
 tests/test_tau_id_es_reference.py             |  59 ++++++
 9 files changed, 913 insertions(+), 10 deletions(-)
 create mode 100644 src/shapesmith/measurements/tau_id_es/__init__.py
 create mode 100644 src/shapesmith/measurements/tau_id_es/combine.py
 create mode 100644 src/shapesmith/measurements/tau_id_es/grid.py
 create mode 100644 src/shapesmith/measurements/tau_id_es/payload.py
 create mode 100644 src/shapesmith/measurements/tau_id_es/plots.py
 create mode 100644 src/shapesmith/measurements/tau_id_es/synced.py
 create mode 100644 tests/test_tau_id_es.py
 create mode 100644 tests/test_tau_id_es_reference.py

diff --git a/README.md b/README.md
index bdb03ec..a9cdee3 100644
--- a/README.md
+++ b/README.md
@@ -25,7 +25,7 @@ shapesmith validate  -c run.yaml              # build + validate the analysis, l
 shapesmith skim      -c run.yaml [--channels mt] [--samples TT,SingleMuon] [--force] [--workers 8]   # ntuples (+ friends) -> Parquet
 shapesmith hist      -c run.yaml [--control] [--regions all] [--skip-systematics] [--processes ZTT,data]  # histograms (ROOT + JSON index)
 shapesmith estimate  -c run.yaml [--control]  # estimated processes and variations (Channel.estimators)
-shapesmith measure   -c run.yaml [--suggest-binning]   # Analysis.measurement -> <output_dir>/<measurement>/<era>/
+shapesmith measure   -c run.yaml [--suggest-binning] [--merge]   # Analysis.measurement -> <output_dir>/<measurement>/<era>/
 shapesmith plot      -c run.yaml [--control] [--region nominal] [--blind] [--log]
 shapesmith sync      -c run.yaml              # combine-style shape files per channel
 shapesmith datacards -c run.yaml [--min-background 1.0] [--no-systematics]
@@ -54,12 +54,14 @@ categories, control variables, variations and estimators.
 - `Region(name, replace_cuts, add_weights, replace_weights)`: cuts replaced by name, weights
   replaced where a process carries them, weights added to every process.
 - `WeightVariation(name, replace_weights, applies_to, regions)` and
-  `ColumnVariation(name, suffix | derived, applies_to, regions)`: a column variation reads column
-  `c` as `c + suffix` where that branch exists (a CROWN shift), or replaces it by `derived[c]`, an
-  expression of nominal columns. Names ending in `Up`/`Down` need their partner and become
-  datacard shapes; other names are templates.
-- Estimators, run in order by `estimate`: `DataMinus(output, region, subtract, scale)`,
-  `ABCD(output, b, c, d, subtract)` and `TemplateShift(name, process, template, fraction)`.
+  `ColumnVariation(name, suffix | derived, applies_to, regions, groups)`: a column variation reads
+  column `c` as `c + suffix` where that branch exists (a CROWN shift), or replaces it by
+  `derived[c]`, an expression of nominal columns; `groups` restricts it to the samples of some
+  groups (a CROWN shift produced for some samples only). Names ending in `Up`/`Down` need their
+  partner and become datacard shapes; other names are templates.
+- Estimators, run in order by `estimate`: `DataMinus(output, region, subtract, scale,
+  clip_negative)`, `ABCD(output, b, c, d, subtract)` and `TemplateShift(name, process, template,
+  fraction)`, which also varies every template variation `t` of the process (as `<name>Up@t`).
 
 **The event rule** (`shapesmith.events`, used by hist, the ML export and measurements): cuts are
 the channel cuts with the region's replacements, the process cuts and the skim cuts; weights are
@@ -81,7 +83,7 @@ the requested scopes and keeps the other histograms of its file.
 Per sample, the skim keeps the columns of every expression its processes can use and the
 shifted branches of its column variations. An event is kept if the skim cuts pass nominally or
 under any column variation of its kind (hist re-applies the varied skim cuts). Where the channel
-declares CROWN shifts for a sample kind, every file must carry a shifted branch for each of them,
+declares CROWN shifts for a sample (its kind and group), every file must carry a shifted branch for each of them,
 and every shifted branch `c__X` of a needed column must be declared; otherwise the skim fails.
 
 The manifest records the skim contract. A stored skim is reused when its skim cuts,
@@ -104,7 +106,18 @@ then, so an older production needs the database checkout it was produced with.
 
 `Analysis.measurement` is an object with a `name` and `run(context)`; `shapesmith measure` calls
 it with a `MeasureContext` (configuration, analysis, channels, output directory,
-`events(query)`, `provenance()`). Building blocks:
+`events(query)`, `provenance()`, and the flags `suggest_binning` and `merge`, the latter to combine
+the results of earlier runs into one payload). Measurements:
+
+- `shapesmith.measurements.tau_id_es`: tau-ID scale factor and energy scale of embedded taus, the
+  chain of smhtt_ul `tauID_SFs_dev`. One run per working-point combination fills the mt channel
+  and the mm control region, writes the shapes in the input format of MorphingTauID2017 and runs
+  per category in CMSSW the datacards, T2W, a 2D likelihood scan and the MultiDimFit singles fit,
+  the result (`results.json`, plots). The energy scale is a grid of template column variations in
+  units of 0.1 % (`grid.py`); `--merge` writes the correctionlib payload of all combinations. A
+  1 sigma interval at the fit range or a scan region at the scan boundary is an error.
+
+Building blocks:
 
 - `shapesmith.payloads`: correctionlib schema-v2 builders; the provenance is JSON in
   `CorrectionSet.description`; payloads are checked (schema, evaluator, no `$schema` key, so
@@ -112,7 +125,8 @@ it with a `MeasureContext` (configuration, analysis, channels, output directory,
 - `shapesmith.measurements.smoothing`: TauFakeFactors' kernel smoothing without ROOT, a port of
   ROOT's `TGraphSmooth::SmoothKern` checked against ROOT and FF_Updated
   (`tests/reference/make_smoothing_reference.py`).
-- `shapesmith.cmssw`: run commands in a CMSSW environment (`combine.cmssw_dir`).
+- `shapesmith.cmssw`: run commands in a CMSSW environment (`combine.cmssw_dir`), started from a
+  clean environment so that the Python environment of ShapeSmith does not shadow CMSSW's.
 
 ## Run configuration
 
diff --git a/src/shapesmith/measurements/tau_id_es/__init__.py b/src/shapesmith/measurements/tau_id_es/__init__.py
new file mode 100644
index 0000000..1573c7f
--- /dev/null
+++ b/src/shapesmith/measurements/tau_id_es/__init__.py
@@ -0,0 +1,140 @@
+"""Tau-ID scale factor and energy scale of embedded taus, per decay-mode (and pT) category.
+
+The chain of the predecessor (smhtt_ul tauID_SFs_dev) on ShapeSmith histograms. One run per working-point
+combination fills the mt channel and its mm control region (the analysis builds them for `vsjet_wp` and `vsele_wp`),
+adds the estimates, writes the shapes in the MorphingTauID2017 format and runs per category in CMSSW (combine.py);
+the result is the singles fit. `--merge` combines the runs into the correctionlib payload.
+
+Output, below <output_dir>/tau_id_es/<era>/:
+- <vsjet>_<vsele>/: shapes.root (histograms and estimates), synced/ (MorphingTauID2017 inputs), fits/ (per
+  category the cards, workspace, scan and singles fit), results.json, plots/;
+- DeepTau2018v2p5_id_es_embedding<era>UL.json.gz, written by --merge.
+A result with a problem (an interval at the fit range, a scan region at the scan boundary, a failed crossing) is an
+error: the run raises after writing results.json, and --merge refuses it.
+"""
+from __future__ import annotations
+
+import json
+import logging
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import asdict, dataclass
+from pathlib import Path
+
+from shapesmith import cmssw, payloads
+from shapesmith.config import CombineConfig
+from shapesmith.estimates import run_estimates
+from shapesmith.fill import run_hist
+from shapesmith.measurements import MeasureContext
+from shapesmith.measurements.tau_id_es import combine, plots
+from shapesmith.measurements.tau_id_es.combine import Interval
+from shapesmith.measurements.tau_id_es.grid import CATEGORIES, CHANNEL, CONTROL_CATEGORY, CONTROL_CHANNEL, SIGNAL, grid_name
+from shapesmith.measurements.tau_id_es.payload import correction_set, payload_name
+from shapesmith.measurements.tau_id_es.synced import write_synced
+from shapesmith.model import Analysis
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True)
+class TauIdEsMeasurement:
+    vsjet_wp: str
+    vsele_wp: str
+    grid: tuple[int, ...]  # the ES shifts in 0.1 % (grid.grid); each is a template variation of EMB in mt
+    name: str = "tau_id_es"
+
+    def run(self, context: MeasureContext) -> None:
+        if context.suggest_binning:
+            raise ValueError("tau_id_es: --suggest-binning is not supported, the m_vis bins are fixed in the analysis")
+        if context.merge:
+            merge(context)
+        else:
+            measure(self, context)
+
+
+def check(analysis: Analysis, measurement: TauIdEsMeasurement) -> None:
+    """The analysis has the channels, categories, signal and grid variations the chain expects."""
+    problems = [f"channel {name} is missing" for name in (CHANNEL, CONTROL_CHANNEL) if name not in analysis.channels]
+    if not problems:
+        channel = analysis.channel(CHANNEL)
+        problems += [f"unknown mt category {c.name} (known: {', '.join(CATEGORIES)})" for c in channel.categories if c.name not in CATEGORIES]
+        if [c.name for c in analysis.channel(CONTROL_CHANNEL).categories] != [CONTROL_CATEGORY]:
+            problems.append(f"the {CONTROL_CHANNEL} channel needs exactly the category {CONTROL_CATEGORY}")
+        if SIGNAL not in channel.backgrounds():
+            problems.append(f"process {SIGNAL} is missing in {CHANNEL}")
+        declared = {v.name for v in channel.variations}
+        problems += [f"grid variation {grid_name(shift)} is missing in {CHANNEL}" for shift in measurement.grid if grid_name(shift) not in declared]
+    if problems:
+        raise ValueError("tau_id_es: " + "; ".join(problems))
+
+
+def fit_category(combine_config: CombineConfig | None, category: str, synced: Path, directory: Path, era: str, grid: tuple[int, ...]) -> dict:
+    """Datacards, workspace, 2D scan, singles fit and postfit shapes of one category below `directory`/fits; its
+    result and problems."""
+    work_dir = Path(directory) / "fits"
+    work_dir.mkdir(parents=True, exist_ok=True)
+    cmssw.run([f"{combine.morphing_command(category, synced, era, grid)} > morphing_{category}.log 2>&1"], combine_config, work_dir)
+    cards = combine.card_dir(work_dir, category)
+    combine.add_rate_parameters(cards, category)
+    cmssw.run([f"{combine.workspace_command(category)} > workspace.log 2>&1", f"{combine.scan_command(category, grid)} > scan.log 2>&1"], combine_config, cards)
+    scan = combine.read_scan(combine.output_path(cards, f"scan_2D_{category}"), category)
+    start, r_range, es_range, problems = combine.singles_inputs(scan, grid)
+    cmssw.run([f"{combine.singles_command(category, start, r_range, es_range)} > singles.log 2>&1", f"{combine.postfit_command(category)} > postfit.log 2>&1"], combine_config, cards)
+    sf, es = combine.read_singles(combine.output_path(cards, f"singles_{category}"), category)
+    problems += combine.interval_problems("r", sf, r_range) + combine.interval_problems("ES", es, es_range)
+    return {"sf": asdict(sf), "es": asdict(es), "start": start, "ranges": {"r": r_range, "ES": es_range}, "problems": problems}
+
+
+def plot_category(directory: Path, category: str, fit: dict) -> None:
+    """The 2D scan with the singles result and the postfit shapes of one fitted category."""
+    cards = combine.card_dir(Path(directory) / "fits", category)
+    scan = combine.read_scan(combine.output_path(cards, f"scan_2D_{category}"), category)
+    plots.plot_scan(scan, Interval(**fit["sf"]), Interval(**fit["es"]), category, Path(directory) / "plots" / "scans" / category)
+    plots.plot_postfit(cards / "postfit_shapes.root", Path(directory) / "plots" / "postfit" / category)
+
+
+def measure(measurement: TauIdEsMeasurement, context: MeasureContext) -> Path:
+    analysis, config = context.analysis, context.config
+    check(analysis, measurement)
+    directory = context.output / f"{measurement.vsjet_wp}_{measurement.vsele_wp}"
+    shapes = directory / "shapes.root"
+    hset = run_hist(config, analysis, [CHANNEL, CONTROL_CHANNEL], False, None, True, None, shapes)
+    run_estimates(hset, analysis, [CHANNEL, CONTROL_CHANNEL])
+    hset.save(shapes)
+    synced = write_synced(hset, analysis, directory / "synced")
+    categories = [c.name for c in analysis.channel(CHANNEL).categories]
+    for category in categories:
+        plots.plot_control(hset, analysis, category, directory / "plots" / "control" / category)
+    with ThreadPoolExecutor(max_workers=max(1, config.workers)) as pool:  # each category runs in its own CMSSW process
+        fits = list(pool.map(lambda category: fit_category(config.combine, category, synced, directory, analysis.era, measurement.grid), categories))
+    for category, fit in zip(categories, fits):
+        plot_category(directory, category, fit)
+    record = {
+        "vsjet_wp": measurement.vsjet_wp,
+        "vsele_wp": measurement.vsele_wp,
+        "categories": dict(zip(categories, fits)),
+        "provenance": context.provenance(vsjet_wp=measurement.vsjet_wp, vsele_wp=measurement.vsele_wp),
+    }
+    (directory / "results.json").write_text(json.dumps(record, indent=2))
+    problems = [f"{category}: {problem}" for category, fit in record["categories"].items() for problem in fit["problems"]]
+    if problems:
+        raise ValueError(f"tau_id_es {measurement.vsjet_wp}/{measurement.vsele_wp}, see {directory / 'results.json'}:\n" + "\n".join(problems))
+    return directory
+
+
+def merge(context: MeasureContext) -> Path:
+    """The payload of every working-point combination measured below the output directory."""
+    results, sources, problems = {}, {}, []
+    for path in sorted(Path(context.output).glob("*/results.json")):
+        record = json.loads(path.read_text())
+        pair = (record["vsjet_wp"], record["vsele_wp"])
+        results[pair] = {category: (Interval(**fit["sf"]), Interval(**fit["es"])) for category, fit in record["categories"].items()}
+        sources["_".join(pair)] = record["provenance"]
+        problems += [f"{'_'.join(pair)}/{category}: {problem}" for category, fit in record["categories"].items() for problem in fit["problems"]]
+    if not results:
+        raise ValueError(f"tau_id_es: no results.json below {context.output}; run the measurement per working point first")
+    if problems:
+        raise ValueError("tau_id_es: results with problems, not merged:\n" + "\n".join(problems))
+    cset = correction_set(results, context.provenance(working_points=sorted(sources), measurements=sources))
+    path = payloads.write(cset, Path(context.output) / payload_name(context.analysis.era))
+    logger.info(f"payload of {len(results)} working-point combinations: {path}")
+    return path
diff --git a/src/shapesmith/measurements/tau_id_es/combine.py b/src/shapesmith/measurements/tau_id_es/combine.py
new file mode 100644
index 0000000..a0528e2
--- /dev/null
+++ b/src/shapesmith/measurements/tau_id_es/combine.py
@@ -0,0 +1,181 @@
+"""The CMSSW part of the measurement, per category, with the options of the predecessor (smhtt_ul tauID_SFs_dev,
+tau_id_es_measurement/emb_tau_id_sfs_ul.sh): MorphingTauID2017 datacards plus the r_DY_incl rate parameters, the
+multiSignalModel workspace, the 2D likelihood scan over (r_EMB, ES) and the MultiDimFit singles fit, which is the
+result. The singles fit starts at the scan's best fit, inside the ranges the scan gives (singles_inputs).
+
+The POIs: r_EMB_<category> (the scale factor) and ES_<category> (the energy scale in percent).
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+import uproot
+
+from shapesmith.measurements.tau_id_es.grid import CHANNEL, CONTROL_CHANNEL, SIGNAL
+from shapesmith.measurements.tau_id_es.synced import POSTFIX
+
+MASS = "125"
+R_RANGE = (0.1, 2.9)  # of the workspace
+SCAN_R_RANGE = (0.11, 2.89)
+SCAN_POINTS = 289
+SCAN_LEVEL = 4.5  # delta NLL of the scan region that gives the singles ranges: 3 sigma in one parameter
+ROBUST_FIT = "--robustFit=1 --setRobustFitAlgo=Minuit2 --X-rtd FITTER_NEW_CROSSING_ALGO --X-rtd FITTER_NEVER_GIVE_UP"
+
+
+@dataclass(frozen=True)
+class Interval:
+    best: float
+    low: float
+    high: float
+
+
+@dataclass(frozen=True)
+class Scan:
+    """The 2D scan: its initial best fit and the grid points (r, ES, delta NLL)."""
+
+    best: tuple[float, float]
+    r: np.ndarray
+    es: np.ndarray
+    dnll: np.ndarray
+
+
+def pois(category: str) -> tuple[str, str]:
+    return f"r_{SIGNAL}_{category}", f"ES_{category}"
+
+
+def es_range(grid: tuple[int, ...]) -> tuple[float, float]:
+    """The ES range of the datacards (percent): the ends of the grid."""
+    return min(grid) / 10, max(grid) / 10
+
+
+def scan_es_range(grid: tuple[int, ...]) -> tuple[float, float]:
+    low, high = es_range(grid)
+    return low + 0.1, high - 0.1
+
+
+def morphing_command(category: str, synced_dir: Path, era: str, grid: tuple[int, ...]) -> str:
+    """MorphingTauID2017 as in the predecessor; it writes output/<category>/htt_mt_<category>/ below the working
+    directory. Its ES loop runs in float from es_min in steps of the precision and skips 0; the accumulated rounding
+    drops the last grid point (with the +-20 %, 0.2 % grid of the predecessor: +20.0)."""
+    steps = set(np.diff(sorted(grid + (0,))))
+    if len(steps) != 1:
+        raise ValueError(f"MorphingTauID2017 needs a uniform grid, got steps {sorted(steps)}")
+    low, high = es_range(grid)
+    options = {
+        "base_path": Path(synced_dir).resolve(), "input_folder_mt": CHANNEL, "input_folder_mm": CONTROL_CHANNEL,
+        "real_data": "true", "classic_bbb": "false", "binomial_bbb": "false", "jetfakes": 0, "embedding": 1,
+        "verbose": "false", "postfix": POSTFIX, "use_control_region": "true", "auto_rebin": "false",
+        "rebin_categories": "false", "manual_rebin_for_yields": "false", "categories": category, "era": era,
+        "tes_precision": steps.pop() / 10, "es_min": low, "es_max": high, "output": category,
+    }
+    return "MorphingTauID2017 " + " ".join(f"--{name}={value}" for name, value in options.items())
+
+
+def card_dir(work_dir: Path, category: str) -> Path:
+    return Path(work_dir) / "output" / category / f"htt_{CHANNEL}_{category}"
+
+
+def add_rate_parameters(cards: Path, category: str) -> None:
+    """The common normalisation r_DY_incl_<category> of the Z->ll processes in both regions: EMB, ZL and ZJ in mt,
+    the embedded muons in mm."""
+    line = f"r_DY_incl_{category} rateParam * {{}} 1.0 [0.5,1.5]\n"
+    for card in sorted(Path(cards).glob(f"htt_{CHANNEL}_*.txt")):
+        processes = [f"{SIGNAL}_{category}", "ZL", "ZJ"] if "ZL" in card.read_text() else [f"{SIGNAL}_{category}", "ZJ"]
+        with card.open("a") as handle:
+            handle.writelines(line.format(process) for process in processes)
+    for card in sorted(Path(cards).glob(f"htt_{CONTROL_CHANNEL}_*.txt")):
+        with card.open("a") as handle:
+            handle.write(line.format("MUEMB"))
+
+
+def workspace_command(category: str) -> str:
+    r = pois(category)[0]
+    return (f"combineTool.py -M T2W -i . -o workspace.root -m {MASS} -P HiggsAnalysis.CombinedLimit.PhysicsModel:multiSignalModel"
+            f" --PO \"map=^.*/{SIGNAL}_{category}:{r}[1,{R_RANGE[0]},{R_RANGE[1]}]\"")
+
+
+def _ranges(category: str, r_range: tuple[float, float], es: tuple[float, float]) -> str:
+    r, es_poi = pois(category)
+    return f"--setParameterRanges {r}={r_range[0]!r},{r_range[1]!r}:{es_poi}={es[0]!r},{es[1]!r}"
+
+
+def scan_command(category: str, grid: tuple[int, ...]) -> str:
+    r, es = pois(category)
+    return (f"combineTool.py -M MultiDimFit -n .scan_2D_{category} -d workspace.root --setParameters {r}=1.0,{es}=0.0"
+            f" {_ranges(category, SCAN_R_RANGE, scan_es_range(grid))} {ROBUST_FIT}"
+            " --cminFallbackAlgo Minuit2,Migrad,0:0.001 --cminFallbackAlgo Minuit2,Migrad,1:0.01 --cminPreScan"
+            f" --redefineSignalPOIs {r},{es} --floatOtherPOIs=1 --points={SCAN_POINTS} --algo grid -m {MASS}"
+            " --alignEdges=1 --cminDefaultMinimizerStrategy 0")
+
+
+def singles_command(category: str, start: tuple[float, float], r_range: tuple[float, float], es: tuple[float, float]) -> str:
+    r, es_poi = pois(category)
+    return (f"combineTool.py -M MultiDimFit -n .singles_{category} -d workspace.root"
+            f" --setParameters {es_poi}={start[1]!r},{r}={start[0]!r} {_ranges(category, r_range, es)} {ROBUST_FIT}"
+            " --cminFallbackAlgo Minuit2,Migrad,0:0.001,Minuit2,Migrad,0:0.01 --cminPreScan"
+            f" --redefineSignalPOIs {r},{es_poi} --floatOtherPOIs=1 --algo singles -m {MASS} --saveFitResult"
+            " --cminDefaultMinimizerStrategy=1")
+
+
+def postfit_command(category: str) -> str:
+    return (f"PostFitShapesFromWorkspace -m {MASS} -w workspace.root --output postfit_shapes.root"
+            f" -f multidimfit.singles_{category}.root:fit_mdf --postfit")
+
+
+def output_path(cards: Path, name: str) -> Path:
+    return Path(cards) / f"higgsCombine.{name}.MultiDimFit.mH{MASS}.root"
+
+
+def read_scan(path: Path, category: str) -> Scan:
+    r, es = pois(category)
+    with uproot.open(path) as f:
+        rows = f["limit"].arrays([r, es, "deltaNLL"], library="np")
+    return Scan((float(rows[r][0]), float(rows[es][0])), rows[r][1:].astype(float), rows[es][1:].astype(float), rows["deltaNLL"][1:].astype(float))
+
+
+def _box(values: np.ndarray, inside: np.ndarray, scan_range: tuple[float, float]) -> tuple[tuple[float, float], bool]:
+    """The range of `values[inside]` widened by one grid step, clipped to the scan range; True if it was clipped."""
+    step = float(np.min(np.diff(np.unique(values))))
+    low, high = float(values[inside].min()) - step, float(values[inside].max()) + step
+    clipped = low < scan_range[0] or high > scan_range[1]
+    return (max(low, scan_range[0]), min(high, scan_range[1])), clipped
+
+
+def singles_inputs(scan: Scan, grid: tuple[int, ...]) -> tuple[tuple[float, float], tuple[float, float], tuple[float, float], list[str]]:
+    """Start values, r range and ES range of the singles fit, and the problems of the scan.
+
+    The ranges enclose the grid points within SCAN_LEVEL of the lowest one, widened by one grid step: the scan
+    replaces the close-up scans the predecessor took them from. The start is the scan's best fit, or the lowest grid
+    point if the best fit lies outside. A region that reaches the scan boundary is a problem (extend the scan)."""
+    finite = np.isfinite(scan.dnll)
+    inside = finite & (scan.dnll <= scan.dnll[finite].min() + SCAN_LEVEL)
+    r_range, r_clipped = _box(scan.r[finite], inside[finite], SCAN_R_RANGE)
+    es, es_clipped = _box(scan.es[finite], inside[finite], scan_es_range(grid))
+    problems = [f"the {name} scan region reaches the scan boundary" for name, clipped in (("r", r_clipped), ("ES", es_clipped)) if clipped]
+    start = scan.best
+    if not (r_range[0] < start[0] < r_range[1] and es[0] < start[1] < es[1]):
+        lowest = int(np.argmin(np.where(finite, scan.dnll, np.inf)))
+        start = (float(scan.r[lowest]), float(scan.es[lowest]))
+    return start, r_range, es, problems
+
+
+def read_singles(path: Path, category: str) -> tuple[Interval, Interval]:
+    """The singles fit: best fit and 1 sigma interval of r and ES (the limit tree holds the best fit, then the low
+    and high crossing of each POI with the others at their best fit)."""
+    names = pois(category)
+    with uproot.open(path) as f:
+        rows = f["limit"].arrays(list(names), library="np")
+    return tuple(Interval(float(rows[name][0]), float(rows[name][1:].min()), float(rows[name][1:].max())) for name in names)
+
+
+def interval_problems(name: str, interval: Interval, fit_range: tuple[float, float]) -> list[str]:
+    """An interval that failed (an end equals the best fit) or ends at the fit range is a problem."""
+    tolerance = 1e-5 * (fit_range[1] - fit_range[0])
+    problems = []
+    if not interval.low < interval.best < interval.high:
+        problems.append(f"{name}: no interval around the best fit {interval.best:g} ({interval.low:g}, {interval.high:g})")
+    if interval.low - fit_range[0] < tolerance or fit_range[1] - interval.high < tolerance:
+        problems.append(f"{name}: interval [{interval.low:g}, {interval.high:g}] at the fit range [{fit_range[0]:g}, {fit_range[1]:g}]")
+    return problems
diff --git a/src/shapesmith/measurements/tau_id_es/grid.py b/src/shapesmith/measurements/tau_id_es/grid.py
new file mode 100644
index 0000000..0c2738d
--- /dev/null
+++ b/src/shapesmith/measurements/tau_id_es/grid.py
@@ -0,0 +1,54 @@
+"""The tau energy-scale grid and the naming conventions of MorphingTauID2017.
+
+A grid point is an energy-scale shift in integer units of 0.1 %: shift -198 scales the tau four-momentum by 0.802.
+Its column variation is named `es-198`, and MorphingTauID2017 reads its shape under the mass string "-19.8" (the
+shift in percent with one decimal). Integers keep the three names in exact correspondence.
+"""
+from __future__ import annotations
+
+import re
+
+SIGNAL = "EMB"  # the embedded signal of the mt channel, one shape per grid point
+CHANNEL = "mt"
+CONTROL_CHANNEL = "mm"  # the Z->mumu control region, one category
+CONTROL_CATEGORY = "control_region"
+
+# MorphingTauID2017's categories: decay modes and tau pT bin in GeV (None: every tau above 20 GeV)
+CATEGORIES = {
+    "DM0": ((0,), None),
+    "DM1": ((1,), None),
+    "DM1011": ((10, 11), None),
+    "DM0_PT20_40": ((0,), (20.0, 40.0)),
+    "DM1_PT20_40": ((1,), (20.0, 40.0)),
+    "DM1011_PT20_40": ((10, 11), (20.0, 40.0)),
+    "DM0_PT40_200": ((0,), (40.0, 200.0)),
+    "DM1_PT40_200": ((1,), (40.0, 200.0)),
+    "DM1011_PT40_200": ((10, 11), (40.0, 200.0)),
+}
+
+_NAME = re.compile(r"es([+-]\d+)")
+
+
+def grid(low: int, high: int, step: int) -> tuple[int, ...]:
+    """The shifts from `low` to `high` (inclusive) in steps of `step`, without the nominal 0."""
+    return tuple(shift for shift in range(low, high + 1, step) if shift != 0)
+
+
+def grid_name(shift: int) -> str:
+    return f"es{shift:+d}"
+
+
+def shift_of(name: str) -> int | None:
+    """The shift of a grid variation name, None for any other name."""
+    match = _NAME.fullmatch(name)
+    return int(match.group(1)) if match else None
+
+
+def factor(shift: int) -> float:
+    """The scale factor of the tau four-momentum."""
+    return (1000 + shift) / 1000
+
+
+def mass(shift: int) -> str:
+    """MorphingTauID2017's mass string of a shift: the percentage with one decimal."""
+    return f"{shift / 10:.1f}"
diff --git a/src/shapesmith/measurements/tau_id_es/payload.py b/src/shapesmith/measurements/tau_id_es/payload.py
new file mode 100644
index 0000000..9f22193
--- /dev/null
+++ b/src/shapesmith/measurements/tau_id_es/payload.py
@@ -0,0 +1,114 @@
+"""The correctionlib payload of the measurement, with the node structure of the predecessor's
+friends/create_xpog_json_v15.py (the file the embedding configuration of CROWN reads), plus provenance.
+
+- DeepTau2018v2p5VSjet(pt, dm, genmatch, wp, wp_VSe, syst, flag): the scale factor r; flag "pt" in the pT bins of
+  the categories, flag "dm" per decay mode for every pT from 20 GeV.
+- tau_energy_scale and tau_energy_scale_dm_binned(pt, eta, dm, genmatch, id, wp, wp_VSe, syst): the energy scale
+  factor (100 + ES) / 100 of the fitted ES in percent, per decay mode and pT bin or per decay mode, for |eta| < 2.5.
+Every other genmatch gives 1.0. DM10 and DM11 both take the DM1011 result; syst up/down are the ends of the 1 sigma
+interval.
+"""
+from __future__ import annotations
+
+from typing import Callable, Mapping
+
+import correctionlib.schemav2 as cs
+
+from shapesmith import payloads
+from shapesmith.measurements.tau_id_es.combine import Interval
+from shapesmith.measurements.tau_id_es.grid import CATEGORIES
+
+DECAY_MODES = (0, 1, 10, 11)
+OTHER_GENMATCH = (0, 1, 2, 3, 4, 6)
+WORKING_POINTS = ("VVVLoose", "VVLoose", "VLoose", "Loose", "Medium", "Tight", "VTight", "VVTight")
+MAX_PT = 100000.0  # the upper edge of the per-decay-mode bins
+TAU_ID = "DeepTau2018v2p5"
+
+# per working-point combination (vsjet, vsele) and category: the intervals of the scale factor and of ES (percent)
+Results = Mapping[tuple[str, str], Mapping[str, tuple[Interval, Interval]]]
+
+
+def es_factor(percent: float) -> float:
+    return (100 + percent) / 100
+
+
+def _syst(interval: Interval, convert: Callable[[float], float]) -> cs.Category:
+    return payloads.category("syst", {"nom": convert(interval.best), "up": convert(interval.high), "down": convert(interval.low)})
+
+
+def _pt_bins(dm: int, results: Mapping[str, tuple[Interval, Interval]], pt_binned: bool) -> list[tuple[tuple[float, float], str]]:
+    """The (pT bin, category) of a decay mode: its pT categories, or its inclusive category from 20 GeV."""
+    bins = [(pt, name) for name, (modes, pt) in CATEGORIES.items() if dm in modes and (pt is not None) == pt_binned and name in results]
+    if not pt_binned:
+        return [((20.0, MAX_PT), name) for _, name in bins]
+    return sorted(bins)
+
+
+def _by_decay_mode(results: Mapping[str, tuple[Interval, Interval]], index: int, pt_binned: bool, convert: Callable[[float], float], wrap: Callable = lambda node: node) -> cs.Category:
+    content = {}
+    for dm in DECAY_MODES:
+        bins = _pt_bins(dm, results, pt_binned)
+        if bins:
+            edges = [bins[0][0][0]] + [pt[1] for pt, _ in bins]
+            content[dm] = wrap(payloads.binning("pt", edges, [_syst(results[name][index], convert) for _, name in bins]))
+    return payloads.category("dm", content)
+
+
+def _genuine_taus(results: Results, node: Callable[[Mapping[str, tuple[Interval, Interval]]], cs.Category]) -> cs.Category:
+    """1.0 unless genmatch is 5; then per working point, vsEle working point and decay mode."""
+    order = {wp: index for index, wp in enumerate(WORKING_POINTS)}
+    by_wp: dict[str, dict] = {}
+    for vsjet, vsele in sorted(results, key=lambda pair: (order[pair[0]], order[pair[1]])):
+        by_wp.setdefault(vsjet, {})[vsele] = node(results[vsjet, vsele])
+    wps = payloads.category("wp", {vsjet: payloads.category("wp_VSe", by_vsele) for vsjet, by_vsele in by_wp.items()})
+    return payloads.category("genmatch", {**{genmatch: 1.0 for genmatch in OTHER_GENMATCH}, 5: wps})
+
+
+def _inputs(results: Results, *names: str) -> list[cs.Variable]:
+    order = {wp: index for index, wp in enumerate(WORKING_POINTS)}
+    vsjet = ",".join(sorted({pair[0] for pair in results}, key=order.get))
+    vsele = ",".join(sorted({pair[1] for pair in results}, key=order.get))
+    known = {
+        "pt": ("real", "Reconstructed tau pT"),
+        "eta": ("real", "Reconstructed tau eta"),
+        "dm": ("int", "Reconstructed tau decay mode: 0, 1, 10, 11"),
+        "genmatch": ("int", "genmatch: 0 or 6 = unmatched or jet, 1 or 3 = electron, 2 or 4 = muon, 5 = real tau"),
+        "id": ("string", f"{TAU_ID}:{TAU_ID}"),
+        "wp": ("string", f"{TAU_ID}VSjet working point: {vsjet}"),
+        "wp_VSe": ("string", f"{TAU_ID}VSe working point: {vsele}"),
+        "syst": ("string", "Systematic variations: 'nom', 'up', 'down'"),
+        "flag": ("string", "Flag: 'pt' = DM-pT-dependent SFs(20,40,200), 'dm' = DM-dependent SFs with pt>=20"),
+    }
+    return [payloads.variable(name, *known[name]) for name in names]
+
+
+def _abs_eta(node: cs.Binning) -> cs.Transform:
+    return payloads.transform("eta", "abs(x)", payloads.binning("eta", [0.0, 2.5], [node]))
+
+
+def scale_factors(results: Results) -> cs.Correction:
+    data = payloads.category("flag", {
+        "pt": _genuine_taus(results, lambda r: _by_decay_mode(r, 0, True, float)),
+        "dm": _genuine_taus(results, lambda r: _by_decay_mode(r, 0, False, float)),
+    })
+    output = payloads.variable("sf", "real", "DM-pT-dependent or DM-dependent scale factor")
+    return payloads.correction(f"{TAU_ID}VSjet", _inputs(results, "pt", "dm", "genmatch", "wp", "wp_VSe", "syst", "flag"), data, version=0,
+                               description="dm-pt-dependent tau ID scale factor for tau embedded samples", output=output)
+
+
+def energy_scale(results: Results, pt_binned: bool) -> cs.Correction:
+    data = payloads.category("id", {TAU_ID: _genuine_taus(results, lambda r: _by_decay_mode(r, 1, pt_binned, es_factor, _abs_eta))})
+    inputs = _inputs(results, "pt", "eta", "dm", "genmatch", "id", "wp", "wp_VSe", "syst")
+    if pt_binned:
+        output = payloads.variable("tes", "real", "DM-pT-dependent tau energy scale")
+        return payloads.correction("tau_energy_scale", inputs, data, version=0, description="dm-pt-dependent tau ES scale factor for tau embedded samples", output=output)
+    output = payloads.variable("tes", "real", "DM-dependent tau energy scale")
+    return payloads.correction("tau_energy_scale_dm_binned", inputs, data, version=0, description="dm-dependent tau ES scale factor for tau embedded samples", output=output)
+
+
+def correction_set(results: Results, provenance: Mapping) -> cs.CorrectionSet:
+    return payloads.correction_set([scale_factors(results), energy_scale(results, True), energy_scale(results, False)], provenance)
+
+
+def payload_name(era: str) -> str:
+    return f"{TAU_ID}_id_es_embedding{era}UL.json.gz"
diff --git a/src/shapesmith/measurements/tau_id_es/plots.py b/src/shapesmith/measurements/tau_id_es/plots.py
new file mode 100644
index 0000000..cd2753c
--- /dev/null
+++ b/src/shapesmith/measurements/tau_id_es/plots.py
@@ -0,0 +1,101 @@
+"""Plots of the measurement: the 2D likelihood scan per category, the postfit shapes of the singles fit, and prefit
+control plots of m_vis with a family of energy-scale grid points."""
+from __future__ import annotations
+
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+import mplhep as hep  # noqa: E402
+import numpy as np  # noqa: E402
+import uproot  # noqa: E402
+
+from shapesmith.histogram import NOMINAL_VARIATION, HistKey, HistogramSet  # noqa: E402
+from shapesmith.measurements.tau_id_es.combine import Interval, Scan  # noqa: E402
+from shapesmith.measurements.tau_id_es.grid import CHANNEL, SIGNAL, grid_name  # noqa: E402
+from shapesmith.model import NOMINAL, Analysis  # noqa: E402
+
+hep.style.use("CMS")
+ES_FAMILY = (-200, -100, 100, 200)  # grid points drawn in the control plots, if present
+LEVELS = {2.30: "68 % CL", 5.99: "95 % CL"}  # 2 delta NLL of two parameters
+
+
+def _save(fig, path: Path) -> list[Path]:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    paths = [path.with_suffix(ext) for ext in (".pdf", ".png")]
+    for p in paths:
+        fig.savefig(p, bbox_inches="tight")
+    plt.close(fig)
+    return paths
+
+
+def plot_scan(scan: Scan, sf: Interval, es: Interval, category: str, path: Path) -> list[Path]:
+    """2 delta NLL of the scan grid with the 68 % and 95 % contours and the singles result."""
+    finite = np.isfinite(scan.dnll)
+    r, e, z = scan.r[finite], scan.es[finite], 2 * (scan.dnll[finite] - scan.dnll[finite].min())
+    fig, ax = plt.subplots(figsize=(10, 9))
+    filled = ax.tricontourf(r, e, np.minimum(z, 30.0), levels=30, cmap="viridis")
+    lines = ax.tricontour(r, e, z, levels=sorted(LEVELS), colors=["white", "orange"])
+    ax.clabel(lines, fmt={level: label for level, label in LEVELS.items()}, fontsize=14)
+    ax.errorbar([sf.best], [es.best], xerr=[[sf.best - sf.low], [sf.high - sf.best]], yerr=[[es.best - es.low], [es.high - es.best]], fmt="o", color="red", label="singles fit")
+    ax.set_xlabel(f"$r_{{EMB}}$ ({category})")
+    ax.set_ylabel("tau ES shift [%]")
+    ax.legend(loc="upper right")
+    fig.colorbar(filled, ax=ax, label=r"$2\,\Delta$NLL")
+    return _save(fig, path)
+
+
+def plot_postfit(shapes: Path, output_dir: Path) -> list[Path]:
+    """Data and the stacked postfit processes of every bin of the singles fit, with the data/prediction ratio."""
+    written = []
+    with uproot.open(shapes) as f:
+        for directory in (name for name in f.keys(cycle=False, recursive=False) if name.endswith("_postfit")):
+            processes = [name for name in f[directory].keys(cycle=False) if not name.startswith(("Total", "data_obs"))]
+            edges = f[f"{directory}/data_obs"].axis().edges()
+            stack = [f[f"{directory}/{name}"].values() for name in processes]
+            data = f[f"{directory}/data_obs"]
+            total = f[f"{directory}/TotalProcs"]
+            fig, (ax, rax) = plt.subplots(2, 1, figsize=(10, 10), gridspec_kw={"height_ratios": [3, 1], "hspace": 0.06}, sharex=True)
+            hep.histplot(stack, bins=edges, stack=True, histtype="fill", label=processes, ax=ax)
+            hep.histplot(data.values(), bins=edges, yerr=np.sqrt(data.variances()), histtype="errorbar", color="black", label="Data", ax=ax)
+            ax.set_ylabel("Events")
+            ax.legend(ncol=2, fontsize=14, frameon=False)
+            ax.text(0.04, 0.95, directory, transform=ax.transAxes, va="top", fontsize=16)
+            with np.errstate(divide="ignore", invalid="ignore"):
+                rax.errorbar(0.5 * (edges[1:] + edges[:-1]), data.values() / total.values(), yerr=np.sqrt(data.variances()) / total.values(), fmt="o", color="black")
+            rax.axhline(1.0, color="gray")
+            rax.set_ylim(0.8, 1.2)
+            rax.set_ylabel("Data / fit")
+            rax.set_xlabel(r"$m_{vis}$ [GeV]")
+            written += _save(fig, Path(output_dir) / directory)
+    return written
+
+
+def plot_control(hset: HistogramSet, analysis: Analysis, category: str, path: Path) -> list[Path]:
+    """Prefit m_vis of one category: data, the stacked backgrounds with the nominal embedded signal, and the total
+    prediction with the embedded signal at a few grid points."""
+    channel = analysis.channel(CHANNEL)
+    variable = next(c.variable for c in channel.categories if c.name == category)
+
+    def get(process: str, variation: str = NOMINAL_VARIATION):
+        return hset.get(HistKey(CHANNEL, category, process, NOMINAL, variation, variable.name))
+
+    backgrounds = [(name, h) for name in channel.backgrounds() if (h := get(name)) is not None]
+    edges, widths = np.asarray(variable.edges), np.diff(variable.edges)
+    total = np.sum([h.values for _, h in backgrounds], axis=0)
+    signal = get(SIGNAL)
+    fig, ax = plt.subplots(figsize=(10, 8))
+    hep.histplot([h.values / widths for _, h in backgrounds], bins=edges, stack=True, histtype="fill", label=[name for name, _ in backgrounds], ax=ax)
+    for shift in ES_FAMILY:
+        shifted = get(SIGNAL, grid_name(shift))
+        if shifted is not None:
+            hep.histplot((total - signal.values + shifted.values) / widths, bins=edges, histtype="step", linewidth=2, label=f"ES {shift / 10:+.1f} %", ax=ax)
+    data = get(channel.data())
+    hep.histplot(data.values / widths, bins=edges, yerr=np.sqrt(data.variances) / widths, histtype="errorbar", color="black", label="Data", ax=ax)
+    ax.set_ylabel("dN / dm$_{vis}$ [1/GeV]")
+    ax.set_xlabel(r"$m_{vis}$ [GeV]")
+    ax.legend(ncol=2, fontsize=14, frameon=False)
+    ax.text(0.04, 0.95, f"{CHANNEL}, {category}", transform=ax.transAxes, va="top", fontsize=16)
+    return _save(fig, path)
diff --git a/src/shapesmith/measurements/tau_id_es/synced.py b/src/shapesmith/measurements/tau_id_es/synced.py
new file mode 100644
index 0000000..f490c1e
--- /dev/null
+++ b/src/shapesmith/measurements/tau_id_es/synced.py
@@ -0,0 +1,50 @@
+"""The shapes in the input format of MorphingTauID2017 (that of the predecessor's convert_to_synced_shapes.py).
+
+One file per channel, `<channel>/htt_<channel>.inputs-sm-Run<era>-TauID_ES.root`, with one directory per category,
+`<channel>_<category>`, holding data_obs and every background, nominal as `<process>` and varied as
+`<process>_<variation>`. The embedded signal of the mt channel is `EMB_<category>_<mass>` per grid point, the
+nominal with mass "0.0", and its variations `EMB_<category>_<mass>_<variation>`.
+"""
+from __future__ import annotations
+
+from pathlib import Path
+
+from shapesmith.histogram import NOMINAL_VARIATION, TEMPLATE_SEPARATOR, HistKey, Histogram, HistogramSet
+from shapesmith.measurements.tau_id_es.grid import CHANNEL, SIGNAL, mass, shift_of
+from shapesmith.model import NOMINAL, Analysis
+from shapesmith.shapes import shape_name, write_shapes
+
+POSTFIX = "-TauID_ES"
+
+
+def shapes_path(directory: Path, channel: str, era: str) -> Path:
+    return Path(directory) / channel / f"htt_{channel}.inputs-sm-Run{era}{POSTFIX}.root"
+
+
+def signal_name(category: str, variation: str) -> str:
+    """MorphingTauID2017's name of the embedded signal under a variation (a grid point, one on top of it, or none)."""
+    systematic, _, template = variation.partition(TEMPLATE_SEPARATOR)
+    shift = shift_of(template or systematic)
+    if shift is None:
+        return shape_name(f"{SIGNAL}_{category}_{mass(0)}", variation)
+    return f"{SIGNAL}_{category}_{mass(shift)}" + (f"_{systematic}" if template else "")
+
+
+def entries(hset: HistogramSet, analysis: Analysis, channel_name: str) -> list[tuple[str, str, Histogram]]:
+    channel = analysis.channel(channel_name)
+    result = []
+    for category in channel.categories:
+        folder, variable = f"{channel_name}_{category.name}", category.variable.name
+        result.append((folder, "data_obs", hset[HistKey(channel_name, category.name, channel.data(), NOMINAL, NOMINAL_VARIATION, variable)]))
+        for process in channel.backgrounds():
+            for key in hset.select(channel=channel_name, category=category.name, process=process, region=NOMINAL, variable=variable):
+                name = signal_name(category.name, key.variation) if channel_name == CHANNEL and process == SIGNAL else shape_name(process, key.variation)
+                result.append((folder, name, hset[key]))
+    return result
+
+
+def write_synced(hset: HistogramSet, analysis: Analysis, directory: Path) -> Path:
+    """The shapes file of every channel of the analysis under `directory`; returns `directory`."""
+    for channel in analysis.channels:
+        write_shapes(shapes_path(directory, channel, analysis.era), entries(hset, analysis, channel))
+    return Path(directory)
diff --git a/tests/test_tau_id_es.py b/tests/test_tau_id_es.py
new file mode 100644
index 0000000..b77fed6
--- /dev/null
+++ b/tests/test_tau_id_es.py
@@ -0,0 +1,190 @@
+import dataclasses
+import json
+
+import correctionlib
+import numpy as np
+import pytest
+import uproot
+
+from shapesmith import payloads
+from shapesmith.histogram import HistKey, Histogram, HistogramSet
+from shapesmith.measurements import MeasureContext
+from shapesmith.measurements.tau_id_es import TauIdEsMeasurement, check, combine, merge, plots
+from shapesmith.measurements.tau_id_es.combine import Interval, Scan
+from shapesmith.measurements.tau_id_es.grid import CATEGORIES, factor, grid, grid_name, mass, shift_of
+from shapesmith.measurements.tau_id_es.payload import correction_set
+from shapesmith.measurements.tau_id_es.synced import shapes_path, signal_name, write_synced
+from shapesmith.model import Analysis, Category, Channel, ColumnVariation, DataMinus, Process, Sample, Variable
+
+GRID = grid(-200, 200, 2)  # the predecessor's grid: +-20 % in steps of 0.2 %
+M_VIS = Variable("m_vis", "m_vis", (30.0, 60.0, 90.0))
+
+
+def morphing_masses(es_min=-20.0, es_max=20.0, precision=0.2):
+    """The mass strings of MorphingTauID2017's loop: float accumulation, 0 skipped, printed with one decimal."""
+    masses, value = [], np.float32(es_min)
+    while value <= np.float32(es_max):
+        if not -0.005 < value < 0.005:
+            masses.append(f"{float(value):.1f}")
+        value = np.float32(value + np.float32(precision))
+    return masses
+
+
+def test_grid_names_round_trip_and_match_morphing_masses():
+    assert len(GRID) == 200 and 0 not in GRID
+    assert all(shift_of(grid_name(shift)) == shift for shift in GRID) and shift_of("CMS_x_Up") is None
+    assert grid_name(-198) == "es-198" and mass(-198) == "-19.8" and mass(0) == "0.0" and factor(-198) == 0.802
+    assert morphing_masses() == [mass(shift) for shift in GRID[:-1]]  # the float loop drops the last point, +20.0
+
+
+def _channel(name, categories, processes, variations=(), estimators=()):
+    groups = {p.group for p in processes}
+    samples = tuple(Sample(f"{group}_1", group, "data" if group == "data" else "embedding" if group in ("EMB", "MUEMB") else "mc") for group in groups)
+    return Channel(name, samples, {}, {"os": "q_1 * q_2 < 0"}, tuple(processes), categories=tuple(categories), variations=variations, estimators=estimators)
+
+
+def _analysis(shifts=(-2, 2)):
+    data = Process("data", "data", "data", "data")
+    mt = _channel("mt", [Category("DM0", "tau_decaymode_2 == 0", M_VIS)],
+                  [data, Process("EMB", "EMB", "background", "EMB"), Process("ZL", "DY", "background", "Z")],
+                  variations=tuple(ColumnVariation(grid_name(s), derived={"pt_2": f"pt_2 * {factor(s)}"}, applies_to=("embedding",)) for s in shifts),
+                  estimators=(DataMinus("QCD", "same_sign", ("EMB", "ZL")),))
+    mm = _channel("mm", [Category("control_region", "m_vis > 70", M_VIS)], [data, Process("MUEMB", "MUEMB", "background", "MUEMB")])
+    return Analysis("tau_id", "2018", 1.0, "EMB", {"mt": mt, "mm": mm})
+
+
+def _h(value):
+    return Histogram(M_VIS.edges, [value, value], [value, value])
+
+
+def test_synced_shapes_use_the_morphing_names(tmp_path):
+    hset = HistogramSet()
+    for channel, category, process, variation in (
+        ("mt", "DM0", "data", "Nominal"), ("mt", "DM0", "EMB", "Nominal"), ("mt", "DM0", "EMB", "es-2"), ("mt", "DM0", "EMB", "es+2"),
+        ("mt", "DM0", "EMB", "CMS_emb_ttbar_contamination_Run2018Up"), ("mt", "DM0", "EMB", "CMS_emb_ttbar_contamination_Run2018Up@es-2"),
+        ("mt", "DM0", "ZL", "Nominal"), ("mt", "DM0", "ZL", "CMS_scale_j_TotalUp"), ("mt", "DM0", "QCD", "Nominal"),
+        ("mm", "control_region", "data", "Nominal"), ("mm", "control_region", "MUEMB", "Nominal"),
+    ):
+        hset[HistKey(channel, category, process, "nominal", variation, "m_vis")] = _h(1.0)
+    write_synced(hset, _analysis(), tmp_path)
+    with uproot.open(shapes_path(tmp_path, "mt", "2018")) as f:
+        assert sorted(f.keys(cycle=False)) == sorted(["mt_DM0"] + [f"mt_DM0/{name}" for name in (
+            "data_obs", "EMB_DM0_0.0", "EMB_DM0_-0.2", "EMB_DM0_0.2", "EMB_DM0_0.0_CMS_emb_ttbar_contamination_Run2018Up",
+            "EMB_DM0_-0.2_CMS_emb_ttbar_contamination_Run2018Up", "ZL", "ZL_CMS_scale_j_TotalUp", "QCD")])
+    assert (tmp_path / "mm" / "htt_mm.inputs-sm-Run2018-TauID_ES.root").exists()
+    assert signal_name("DM1011_PT40_200", "es+200") == "EMB_DM1011_PT40_200_20.0"
+
+
+def test_check_names_what_the_chain_misses():
+    check(_analysis(), TauIdEsMeasurement("Tight", "VVLoose", (-2, 2)))
+    with pytest.raises(ValueError, match="grid variation es\\+4 is missing"):
+        check(_analysis(), TauIdEsMeasurement("Tight", "VVLoose", (-2, 2, 4)))
+    with pytest.raises(ValueError, match="channel mm is missing"):
+        check(dataclasses.replace(_analysis(), channels={"mt": _analysis().channel("mt")}), TauIdEsMeasurement("Tight", "VVLoose", (-2, 2)))
+
+
+def test_commands_have_the_predecessor_options(tmp_path):
+    assert combine.morphing_command("DM0", tmp_path, "2018", GRID) == (
+        f"MorphingTauID2017 --base_path={tmp_path.resolve()} --input_folder_mt=mt --input_folder_mm=mm --real_data=true --classic_bbb=false"
+        " --binomial_bbb=false --jetfakes=0 --embedding=1 --verbose=false --postfix=-TauID_ES --use_control_region=true --auto_rebin=false"
+        " --rebin_categories=false --manual_rebin_for_yields=false --categories=DM0 --era=2018 --tes_precision=0.2 --es_min=-20.0 --es_max=20.0 --output=DM0")
+    assert combine.workspace_command("DM0") == ("combineTool.py -M T2W -i . -o workspace.root -m 125 -P HiggsAnalysis.CombinedLimit.PhysicsModel:multiSignalModel"
+                                                " --PO \"map=^.*/EMB_DM0:r_EMB_DM0[1,0.1,2.9]\"")
+    scan = combine.scan_command("DM0", GRID)
+    assert "--setParameterRanges r_EMB_DM0=0.11,2.89:ES_DM0=-19.9,19.9" in scan and "--points=289 --algo grid" in scan
+    singles = combine.singles_command("DM0", (0.9, -1.5), (0.7, 1.1), (-8.0, 4.0))
+    assert "--setParameters ES_DM0=-1.5,r_EMB_DM0=0.9 --setParameterRanges r_EMB_DM0=0.7,1.1:ES_DM0=-8.0,4.0" in singles
+    assert "--algo singles" in singles and "--cminFallbackAlgo Minuit2,Migrad,0:0.001,Minuit2,Migrad,0:0.01" in singles
+    with pytest.raises(ValueError, match="uniform grid"):
+        combine.morphing_command("DM0", tmp_path, "2018", (-4, -2, 2, 6))
+
+
+def test_rate_parameters_tie_the_z_normalisation_of_both_regions(tmp_path):
+    (tmp_path / "htt_mt_7_Run2018.txt").write_text("process W ZL EMB_DM0\n")
+    (tmp_path / "htt_mm_100_Run2018.txt").write_text("process W MUEMB\n")
+    combine.add_rate_parameters(tmp_path, "DM0")
+    assert (tmp_path / "htt_mt_7_Run2018.txt").read_text().splitlines()[1:] == [
+        "r_DY_incl_DM0 rateParam * EMB_DM0 1.0 [0.5,1.5]", "r_DY_incl_DM0 rateParam * ZL 1.0 [0.5,1.5]", "r_DY_incl_DM0 rateParam * ZJ 1.0 [0.5,1.5]"]
+    assert (tmp_path / "htt_mm_100_Run2018.txt").read_text().splitlines()[1:] == ["r_DY_incl_DM0 rateParam * MUEMB 1.0 [0.5,1.5]"]
+
+
+def _write_limit(path, **columns):
+    with uproot.recreate(path) as f:
+        f["limit"] = {name: np.asarray(values, dtype=np.float32) for name, values in columns.items()}
+
+
+def test_read_singles_and_its_problems(tmp_path):
+    _write_limit(tmp_path / "fit.root", r_EMB_DM0=[0.9, 0.85, 0.95, 0.9, 0.9], ES_DM0=[-1.8, -1.8, -1.8, -4.4, -0.1], quantileExpected=[-1, -0.32, 0.32, -0.32, 0.32])
+    sf, es = combine.read_singles(tmp_path / "fit.root", "DM0")
+    assert (sf.best, sf.low, sf.high) == pytest.approx((0.9, 0.85, 0.95)) and (es.low, es.high) == pytest.approx((-4.4, -0.1))
+    assert combine.interval_problems("r", sf, (0.7, 1.1)) == []
+    assert combine.interval_problems("r", sf, (0.85, 1.1))[0].startswith("r: interval [0.85, 0.95] at the fit range")
+    assert "no interval" in combine.interval_problems("ES", Interval(1.0, 1.0, 2.0), (-5.0, 5.0))[0]
+
+
+def _scan(best=(0.9, -2.0), sigma=(0.05, 2.0)):
+    r, es = np.meshgrid(np.linspace(0.11, 2.89, 17), np.linspace(-19.9, 19.9, 17))
+    dnll = 0.5 * (((r - best[0]) / sigma[0]) ** 2 + ((es - best[1]) / sigma[1]) ** 2)
+    return Scan(best, r.ravel(), es.ravel(), dnll.ravel())
+
+
+def test_singles_inputs_from_the_scan():
+    start, r_range, es_range, problems = combine.singles_inputs(_scan(sigma=(0.2, 5.0)), GRID)
+    assert start == (0.9, -2.0) and problems == []
+    assert r_range[0] < 0.9 - 0.2 and r_range[1] > 0.9 + 0.2 and es_range[0] < -7.0 and es_range[1] > 3.0  # the 1 sigma interval inside
+    lost = dataclasses.replace(_scan(sigma=(0.2, 5.0)), best=(0.11, -19.9))  # the scan's initial fit ended in a corner
+    assert combine.singles_inputs(lost, GRID)[0] == pytest.approx((0.97875, -2.4875))  # the lowest grid point
+    assert combine.singles_inputs(_scan(best=(0.9, 18.0), sigma=(0.2, 5.0)), GRID)[3] == ["the ES scan region reaches the scan boundary"]
+
+
+def _results(offset=0.0):
+    return {name: (Interval(0.9 + offset, 0.85 + offset, 0.95 + offset), Interval(-1.0, -3.0, 1.0 + index)) for index, name in enumerate(CATEGORIES)}
+
+
+def test_payload_structure_and_values():
+    results = {("Medium", "VVLoose"): _results(), ("Tight", "Tight"): _results(0.1)}
+    cset = correctionlib.CorrectionSet.from_string(payloads.dumps(correction_set(results, {"era": "2018"})))
+    sf, es, es_dm = cset["DeepTau2018v2p5VSjet"], cset["tau_energy_scale"], cset["tau_energy_scale_dm_binned"]
+    assert sf.evaluate(30.0, 0, 6, "Medium", "VVLoose", "nom", "pt") == 1.0  # genmatch != 5
+    assert sf.evaluate(30.0, 0, 5, "Medium", "VVLoose", "nom", "dm") == pytest.approx(0.9)
+    assert sf.evaluate(500.0, 1, 5, "Tight", "Tight", "up", "dm") == pytest.approx(1.05)
+    assert sf.evaluate(25.0, 10, 5, "Medium", "VVLoose", "down", "pt") == sf.evaluate(25.0, 11, 5, "Medium", "VVLoose", "down", "pt")  # both DM1011
+    index = list(CATEGORIES).index
+    assert es.evaluate(50.0, -2.1, 11, 5, "DeepTau2018v2p5", "Medium", "VVLoose", "up") == pytest.approx((100 + 1.0 + index("DM1011_PT40_200")) / 100)
+    assert es.evaluate(25.0, 0.3, 0, 5, "DeepTau2018v2p5", "Medium", "VVLoose", "nom") == pytest.approx(0.99)
+    assert es_dm.evaluate(300.0, 0.3, 1, 5, "DeepTau2018v2p5", "Tight", "Tight", "up") == pytest.approx((100 + 1.0 + index("DM1")) / 100)
+    assert es_dm.evaluate(300.0, 0.3, 1, 2, "DeepTau2018v2p5", "Tight", "Tight", "up") == 1.0
+
+
+def _record(directory, vsjet, vsele, problems=()):
+    fits = {name: {"sf": sf.__dict__, "es": es.__dict__, "problems": list(problems)} for name, (sf, es) in _results().items()}
+    directory.mkdir(parents=True)
+    (directory / "results.json").write_text(json.dumps({"vsjet_wp": vsjet, "vsele_wp": vsele, "categories": fits, "provenance": {"run": f"{vsjet}_{vsele}"}}))
+
+
+def test_merge_writes_the_payload_of_every_working_point(tmp_path, mini_run):
+    config, analysis = mini_run
+    _record(tmp_path / "Medium_VVLoose", "Medium", "VVLoose")
+    _record(tmp_path / "Tight_VVLoose", "Tight", "VVLoose")
+    context = MeasureContext(config, analysis, ["mt"], tmp_path, merge=True)
+    path = merge(context)
+    assert path == tmp_path / "DeepTau2018v2p5_id_es_embedding2018UL.json.gz"
+    back = payloads.read(path)
+    assert payloads.provenance(back)["measurements"] == {"Medium_VVLoose": {"run": "Medium_VVLoose"}, "Tight_VVLoose": {"run": "Tight_VVLoose"}}
+    assert back.corrections[0].inputs[3].description == "DeepTau2018v2p5VSjet working point: Medium,Tight"
+    _record(tmp_path / "Tight_Tight", "Tight", "Tight", problems=["r: interval at the fit range"])
+    with pytest.raises(ValueError, match="Tight_Tight/DM0: r: interval at the fit range"):
+        merge(context)
+
+
+def test_plots_are_written(tmp_path):
+    scan = _scan(sigma=(0.2, 5.0))
+    assert plots.plot_scan(scan, Interval(0.9, 0.7, 1.1), Interval(-2.0, -7.0, 3.0), "DM0", tmp_path / "scan")[0].exists()
+    hset = HistogramSet()
+    for process, variation, value in (("data", "Nominal", 3.0), ("EMB", "Nominal", 2.0), ("EMB", "es-2", 1.8), ("ZL", "Nominal", 1.0)):
+        hset[HistKey("mt", "DM0", process, "nominal", variation, "m_vis")] = _h(value)
+    assert plots.plot_control(hset, _analysis(), "DM0", tmp_path / "control")[0].exists()
+    with uproot.recreate(tmp_path / "postfit_shapes.root") as f:
+        for name, value in (("data_obs", 3.0), ("EMB_DM0", 2.0), ("ZL", 1.0), ("TotalProcs", 3.0)):
+            f[f"htt_mt_7_Run2018_postfit/{name}"] = _h(value).to_root(name)
+    assert [p.name for p in plots.plot_postfit(tmp_path / "postfit_shapes.root", tmp_path / "postfit")] == ["htt_mt_7_Run2018_postfit.pdf", "htt_mt_7_Run2018_postfit.png"]
diff --git a/tests/test_tau_id_es_reference.py b/tests/test_tau_id_es_reference.py
new file mode 100644
index 0000000..9a61bea
--- /dev/null
+++ b/tests/test_tau_id_es_reference.py
@@ -0,0 +1,59 @@
+"""The measurement against the predecessor's 2018 outputs (jvoss's smhtt_ul area); skipped where they are absent."""
+import filecmp
+import json
+from pathlib import Path
+
+import pytest
+import uproot
+
+from shapesmith import cmssw, payloads
+from shapesmith.config import CombineConfig
+from shapesmith.histogram import on_template
+from shapesmith.measurements.tau_id_es import combine
+from shapesmith.measurements.tau_id_es.grid import CATEGORIES, grid, grid_name
+from shapesmith.measurements.tau_id_es.payload import correction_set
+from shapesmith.measurements.tau_id_es.synced import shapes_path, signal_name
+
+AREA = Path("/work/jvoss/smhtt_ul_SFs_v15")
+CMSSW = AREA / "CMSSW_14_1_0_pre4"
+WPS = {"M_VVL": ("Medium", "VVLoose"), "M_T": ("Medium", "Tight"), "T_VVL": ("Tight", "VVLoose"), "T_T": ("Tight", "Tight")}
+GRID = grid(-200, 200, 2)
+pytestmark = pytest.mark.skipif(not AREA.exists(), reason="needs the predecessor's area /work/jvoss/smhtt_ul_SFs_v15")
+
+
+def _cards(tag, category):
+    vsjet, vsele = WPS[tag]
+    return AREA / "output" / "datacards" / "SFs_EMB_Run2_04_08_26__full" / f"{tag}_18_full" / "2018" / vsjet / vsele / f"htt_mt_{category}"
+
+
+def _synced(tag):
+    vsjet, vsele = WPS[tag]
+    return AREA / "output" / "shapes_synced" / "SFs_EMB_Run2_04_08_26__full" / f"2018-{tag}_18_full" / vsjet / vsele
+
+
+def test_the_payload_from_the_predecessor_fits_is_the_predecessor_payload():
+    results = {WPS[tag]: {c: combine.read_singles(_cards(tag, c) / f"higgsCombine.comb_sep_fit_{tag}_18_full_{c}.MultiDimFit.mH125.root", c) for c in CATEGORIES} for tag in WPS}
+    ours = json.loads(payloads.dumps(correction_set(results, {})))
+    reference = json.loads((AREA / "Tau_SFs_DT2p5" / "DeepTau2018v2p5_id_es_embedding2018UL.json").read_text())
+    assert ours["corrections"] == reference["corrections"]
+
+
+def test_signal_names_are_those_of_the_predecessor_shapes():
+    with uproot.open(shapes_path(_synced("T_VVL"), "mt", "2018")) as f:
+        keys = {key.split("/", 1)[1] for key in f.keys(cycle=False) if key.startswith("mt_DM0/EMB_")}
+    grid_points = [grid_name(shift) for shift in GRID]
+    ours = {signal_name("DM0", variation) for variation in ("Nominal", *grid_points)}
+    for direction in ("Up", "Down"):
+        systematic = f"CMS_emb_ttbar_contamination_Run2018{direction}"
+        ours |= {signal_name("DM0", systematic)} | {signal_name("DM0", on_template(systematic, point)) for point in grid_points}
+    assert ours == keys
+
+
+@pytest.mark.skipif(not CMSSW.exists(), reason="needs the predecessor's CMSSW area")
+def test_morphing_datacards_equal_the_predecessor_cards(tmp_path):
+    category = "DM1_PT20_40"
+    cmssw.run([combine.morphing_command(category, _synced("T_T"), "2018", GRID) + " > morphing.log 2>&1"], CombineConfig(cmssw_dir=str(CMSSW)), tmp_path)
+    cards = combine.card_dir(tmp_path, category)
+    combine.add_rate_parameters(cards, category)
+    reference = sorted(_cards("T_T", category).glob("htt_*_Run2018.txt"))
+    assert len(reference) == 2 and all(filecmp.cmp(cards / card.name, card, shallow=False) for card in reference)

From 5f3196bde10ca5fdd080309aded953a121b409bb Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 07:36:40 +0200
Subject: [PATCH 15/33] feat(cmssw): a timeout for CMSSW commands; tau_id_es
 records failed categories

A singles fit with FITTER_NEVER_GIVE_UP can iterate without end (seen in the
reproduction, T_T DM1011 with the scan-derived ranges). cmssw.run takes a
timeout and kills the whole process group when it expires. tau_id_es gives
each CMSSW step of a category an hour; a failed or expired step becomes a
problem of that category, the other categories still finish, and --merge
refuses the result.
---
 src/shapesmith/cmssw.py                       | 18 +++++++--
 .../measurements/tau_id_es/__init__.py        | 39 ++++++++++++-------
 tests/test_limits.py                          | 13 +++++++
 tests/test_tau_id_es.py                       | 10 +++++
 4 files changed, 62 insertions(+), 18 deletions(-)

diff --git a/src/shapesmith/cmssw.py b/src/shapesmith/cmssw.py
index 34d3f29..63e8ebb 100644
--- a/src/shapesmith/cmssw.py
+++ b/src/shapesmith/cmssw.py
@@ -1,12 +1,14 @@
 """Run commands inside a CMSSW environment (combine, CombineHarvester) in a bash subshell.
 
 The subshell starts from a clean environment: the Python environment ShapeSmith runs in (e.g. an LCG view) would
-otherwise shadow CMSSW's libraries and Python packages.
+otherwise shadow CMSSW's libraries and Python packages. With a timeout, the whole process group of the subshell is
+killed when it expires (a fit can iterate without end).
 """
 from __future__ import annotations
 
 import logging
 import os
+import signal
 import subprocess
 from pathlib import Path
 from typing import Sequence
@@ -38,8 +40,18 @@ def clean_environment() -> dict[str, str]:
     return {"PATH": "/usr/bin:/bin", **{name: os.environ[name] for name in KEPT_VARIABLES if name in os.environ}}
 
 
-def run(commands: Sequence[str], combine: CombineConfig | None, cwd: Path) -> None:
+def run(commands: Sequence[str], combine: CombineConfig | None, cwd: Path, timeout: float | None = None) -> None:
+    """Raise CalledProcessError on a failing command, TimeoutExpired after `timeout` seconds."""
     if combine is None:
         raise ValueError("the run configuration needs `combine` (cmssw_dir) to run CMSSW commands")
     logger.info(f"running {len(commands)} CMSSW commands in {cwd}")
-    subprocess.run(["bash", "-c", script(combine, cwd, commands)], check=True, env=clean_environment())
+    arguments = ["bash", "-c", script(combine, cwd, commands)]
+    process = subprocess.Popen(arguments, env=clean_environment(), start_new_session=True)
+    try:
+        code = process.wait(timeout=timeout)
+    except subprocess.TimeoutExpired:
+        os.killpg(process.pid, signal.SIGKILL)
+        process.wait()
+        raise
+    if code:
+        raise subprocess.CalledProcessError(code, arguments)
diff --git a/src/shapesmith/measurements/tau_id_es/__init__.py b/src/shapesmith/measurements/tau_id_es/__init__.py
index 1573c7f..680bc63 100644
--- a/src/shapesmith/measurements/tau_id_es/__init__.py
+++ b/src/shapesmith/measurements/tau_id_es/__init__.py
@@ -9,13 +9,15 @@
 - <vsjet>_<vsele>/: shapes.root (histograms and estimates), synced/ (MorphingTauID2017 inputs), fits/ (per
   category the cards, workspace, scan and singles fit), results.json, plots/;
 - DeepTau2018v2p5_id_es_embedding<era>UL.json.gz, written by --merge.
-A result with a problem (an interval at the fit range, a scan region at the scan boundary, a failed crossing) is an
-error: the run raises after writing results.json, and --merge refuses it.
+A result with a problem (an interval at the fit range, a scan region at the scan boundary, a failed crossing, a CMSSW
+step that failed or ran longer than STEP_TIMEOUT) is an error: the run raises after writing results.json, and
+--merge refuses it.
 """
 from __future__ import annotations
 
 import json
 import logging
+import subprocess
 from concurrent.futures import ThreadPoolExecutor
 from dataclasses import asdict, dataclass
 from pathlib import Path
@@ -33,6 +35,7 @@
 from shapesmith.model import Analysis
 
 logger = logging.getLogger(__name__)
+STEP_TIMEOUT = 3600.0  # seconds per CMSSW step of a category; a singles fit can iterate without end
 
 
 @dataclass(frozen=True)
@@ -72,18 +75,26 @@ def fit_category(combine_config: CombineConfig | None, category: str, synced: Pa
     result and problems."""
     work_dir = Path(directory) / "fits"
     work_dir.mkdir(parents=True, exist_ok=True)
-    cmssw.run([f"{combine.morphing_command(category, synced, era, grid)} > morphing_{category}.log 2>&1"], combine_config, work_dir)
+    cmssw.run([f"{combine.morphing_command(category, synced, era, grid)} > morphing_{category}.log 2>&1"], combine_config, work_dir, STEP_TIMEOUT)
     cards = combine.card_dir(work_dir, category)
     combine.add_rate_parameters(cards, category)
-    cmssw.run([f"{combine.workspace_command(category)} > workspace.log 2>&1", f"{combine.scan_command(category, grid)} > scan.log 2>&1"], combine_config, cards)
+    cmssw.run([f"{combine.workspace_command(category)} > workspace.log 2>&1", f"{combine.scan_command(category, grid)} > scan.log 2>&1"], combine_config, cards, STEP_TIMEOUT)
     scan = combine.read_scan(combine.output_path(cards, f"scan_2D_{category}"), category)
     start, r_range, es_range, problems = combine.singles_inputs(scan, grid)
-    cmssw.run([f"{combine.singles_command(category, start, r_range, es_range)} > singles.log 2>&1", f"{combine.postfit_command(category)} > postfit.log 2>&1"], combine_config, cards)
+    cmssw.run([f"{combine.singles_command(category, start, r_range, es_range)} > singles.log 2>&1", f"{combine.postfit_command(category)} > postfit.log 2>&1"], combine_config, cards, STEP_TIMEOUT)
     sf, es = combine.read_singles(combine.output_path(cards, f"singles_{category}"), category)
     problems += combine.interval_problems("r", sf, r_range) + combine.interval_problems("ES", es, es_range)
     return {"sf": asdict(sf), "es": asdict(es), "start": start, "ranges": {"r": r_range, "ES": es_range}, "problems": problems}
 
 
+def fit_or_failure(combine_config: CombineConfig | None, category: str, synced: Path, directory: Path, era: str, grid: tuple[int, ...]) -> dict:
+    """fit_category, or a result without values whose problem is the failed CMSSW step (see its log)."""
+    try:
+        return fit_category(combine_config, category, synced, directory, era, grid)
+    except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as error:
+        return {"sf": None, "es": None, "problems": [f"CMSSW step failed: {error}"]}
+
+
 def plot_category(directory: Path, category: str, fit: dict) -> None:
     """The 2D scan with the singles result and the postfit shapes of one fitted category."""
     cards = combine.card_dir(Path(directory) / "fits", category)
@@ -105,9 +116,10 @@ def measure(measurement: TauIdEsMeasurement, context: MeasureContext) -> Path:
     for category in categories:
         plots.plot_control(hset, analysis, category, directory / "plots" / "control" / category)
     with ThreadPoolExecutor(max_workers=max(1, config.workers)) as pool:  # each category runs in its own CMSSW process
-        fits = list(pool.map(lambda category: fit_category(config.combine, category, synced, directory, analysis.era, measurement.grid), categories))
+        fits = list(pool.map(lambda category: fit_or_failure(config.combine, category, synced, directory, analysis.era, measurement.grid), categories))
     for category, fit in zip(categories, fits):
-        plot_category(directory, category, fit)
+        if fit["sf"] is not None:
+            plot_category(directory, category, fit)
     record = {
         "vsjet_wp": measurement.vsjet_wp,
         "vsele_wp": measurement.vsele_wp,
@@ -123,17 +135,14 @@ def measure(measurement: TauIdEsMeasurement, context: MeasureContext) -> Path:
 
 def merge(context: MeasureContext) -> Path:
     """The payload of every working-point combination measured below the output directory."""
-    results, sources, problems = {}, {}, []
-    for path in sorted(Path(context.output).glob("*/results.json")):
-        record = json.loads(path.read_text())
-        pair = (record["vsjet_wp"], record["vsele_wp"])
-        results[pair] = {category: (Interval(**fit["sf"]), Interval(**fit["es"])) for category, fit in record["categories"].items()}
-        sources["_".join(pair)] = record["provenance"]
-        problems += [f"{'_'.join(pair)}/{category}: {problem}" for category, fit in record["categories"].items() for problem in fit["problems"]]
-    if not results:
+    records = [json.loads(path.read_text()) for path in sorted(Path(context.output).glob("*/results.json"))]
+    if not records:
         raise ValueError(f"tau_id_es: no results.json below {context.output}; run the measurement per working point first")
+    problems = [f"{r['vsjet_wp']}_{r['vsele_wp']}/{category}: {problem}" for r in records for category, fit in r["categories"].items() for problem in fit["problems"]]
     if problems:
         raise ValueError("tau_id_es: results with problems, not merged:\n" + "\n".join(problems))
+    results = {(r["vsjet_wp"], r["vsele_wp"]): {category: (Interval(**fit["sf"]), Interval(**fit["es"])) for category, fit in r["categories"].items()} for r in records}
+    sources = {f"{r['vsjet_wp']}_{r['vsele_wp']}": r["provenance"] for r in records}
     cset = correction_set(results, context.provenance(working_points=sorted(sources), measurements=sources))
     path = payloads.write(cset, Path(context.output) / payload_name(context.analysis.era))
     logger.info(f"payload of {len(results)} working-point combinations: {path}")
diff --git a/tests/test_limits.py b/tests/test_limits.py
index 762cf46..fd69f8f 100644
--- a/tests/test_limits.py
+++ b/tests/test_limits.py
@@ -1,3 +1,5 @@
+import subprocess
+import time
 from pathlib import Path
 
 import numpy as np
@@ -32,6 +34,17 @@ def test_cmssw_commands_run_in_a_clean_environment(monkeypatch):
     assert "PYTHONPATH" not in environment and environment["HOME"] == "/home/someone" and environment["PATH"] == "/usr/bin:/bin"
 
 
+def test_cmssw_run_stops_the_commands_at_the_timeout(tmp_path, monkeypatch):
+    monkeypatch.setattr(cmssw, "script", lambda combine, cwd, commands: "; ".join([f"cd {cwd}", *commands]))  # no CMSSW needed
+    with pytest.raises(subprocess.TimeoutExpired):
+        cmssw.run(["sleep 30 & echo $! > child", "wait"], CombineConfig(cmssw_dir="/unused"), tmp_path, timeout=1)
+    child = int((tmp_path / "child").read_text())
+    time.sleep(0.2)
+    assert not Path(f"/proc/{child}").exists() or "Z" in Path(f"/proc/{child}/stat").read_text().split()[2]  # killed with its group
+    with pytest.raises(subprocess.CalledProcessError):
+        cmssw.run(["exit 3"], CombineConfig(cmssw_dir="/unused"), tmp_path)
+
+
 def test_cmssw_run_needs_a_combine_configuration(tmp_path):
     with pytest.raises(ValueError, match="needs `combine`"):
         cmssw.run(["true"], None, tmp_path)
diff --git a/tests/test_tau_id_es.py b/tests/test_tau_id_es.py
index b77fed6..f4a0d79 100644
--- a/tests/test_tau_id_es.py
+++ b/tests/test_tau_id_es.py
@@ -188,3 +188,13 @@ def test_plots_are_written(tmp_path):
         for name, value in (("data_obs", 3.0), ("EMB_DM0", 2.0), ("ZL", 1.0), ("TotalProcs", 3.0)):
             f[f"htt_mt_7_Run2018_postfit/{name}"] = _h(value).to_root(name)
     assert [p.name for p in plots.plot_postfit(tmp_path / "postfit_shapes.root", tmp_path / "postfit")] == ["htt_mt_7_Run2018_postfit.pdf", "htt_mt_7_Run2018_postfit.png"]
+
+
+def test_merge_refuses_a_failed_category(tmp_path, mini_run):
+    config, analysis = mini_run
+    _record(tmp_path / "Tight_VVLoose", "Tight", "VVLoose")
+    record = json.loads((tmp_path / "Tight_VVLoose" / "results.json").read_text())
+    record["categories"]["DM0"] = {"sf": None, "es": None, "problems": ["CMSSW step failed: timeout"]}
+    (tmp_path / "Tight_VVLoose" / "results.json").write_text(json.dumps(record))
+    with pytest.raises(ValueError, match="DM0: CMSSW step failed"):
+        merge(MeasureContext(config, analysis, ["mt"], tmp_path, merge=True))

From eb3fd7917a0212920585a70213a2da11a9ac5e8a Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 07:43:08 +0200
Subject: [PATCH 16/33] fix(tau_id_es): distinct colours in the stacked plots

The default colour cycle repeats after ten processes, so ZL took the colour
of the embedded signal in the postfit plots of mt.
---
 src/shapesmith/measurements/tau_id_es/plots.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/src/shapesmith/measurements/tau_id_es/plots.py b/src/shapesmith/measurements/tau_id_es/plots.py
index cd2753c..8f6d578 100644
--- a/src/shapesmith/measurements/tau_id_es/plots.py
+++ b/src/shapesmith/measurements/tau_id_es/plots.py
@@ -22,6 +22,11 @@
 LEVELS = {2.30: "68 % CL", 5.99: "95 % CL"}  # 2 delta NLL of two parameters
 
 
+def _colors(n: int) -> list:
+    """Distinct fill colours for up to 20 stacked processes (the default cycle repeats after 10)."""
+    return [plt.cm.tab20(index % 20) for index in range(n)]
+
+
 def _save(fig, path: Path) -> list[Path]:
     path.parent.mkdir(parents=True, exist_ok=True)
     paths = [path.with_suffix(ext) for ext in (".pdf", ".png")]
@@ -58,10 +63,11 @@ def plot_postfit(shapes: Path, output_dir: Path) -> list[Path]:
             data = f[f"{directory}/data_obs"]
             total = f[f"{directory}/TotalProcs"]
             fig, (ax, rax) = plt.subplots(2, 1, figsize=(10, 10), gridspec_kw={"height_ratios": [3, 1], "hspace": 0.06}, sharex=True)
-            hep.histplot(stack, bins=edges, stack=True, histtype="fill", label=processes, ax=ax)
+            hep.histplot(stack, bins=edges, stack=True, histtype="fill", label=processes, color=_colors(len(stack)), ax=ax)
             hep.histplot(data.values(), bins=edges, yerr=np.sqrt(data.variances()), histtype="errorbar", color="black", label="Data", ax=ax)
             ax.set_ylabel("Events")
-            ax.legend(ncol=2, fontsize=14, frameon=False)
+            ax.set_ylim(0, 1.6 * data.values().max())
+            ax.legend(ncol=3, fontsize=14, frameon=False, loc="upper right")
             ax.text(0.04, 0.95, directory, transform=ax.transAxes, va="top", fontsize=16)
             with np.errstate(divide="ignore", invalid="ignore"):
                 rax.errorbar(0.5 * (edges[1:] + edges[:-1]), data.values() / total.values(), yerr=np.sqrt(data.variances()) / total.values(), fmt="o", color="black")
@@ -87,7 +93,7 @@ def get(process: str, variation: str = NOMINAL_VARIATION):
     total = np.sum([h.values for _, h in backgrounds], axis=0)
     signal = get(SIGNAL)
     fig, ax = plt.subplots(figsize=(10, 8))
-    hep.histplot([h.values / widths for _, h in backgrounds], bins=edges, stack=True, histtype="fill", label=[name for name, _ in backgrounds], ax=ax)
+    hep.histplot([h.values / widths for _, h in backgrounds], bins=edges, stack=True, histtype="fill", label=[name for name, _ in backgrounds], color=_colors(len(backgrounds)), ax=ax)
     for shift in ES_FAMILY:
         shifted = get(SIGNAL, grid_name(shift))
         if shifted is not None:

From 3ca621ba42de04d0907e2d126ac3dd652de189f4 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 07:44:29 +0200
Subject: [PATCH 17/33] feat(smoothing)!: bitwise ROOT kernel sums; hybrids by
 their binwise bin counts

The kernel sums run in ROOT's order with libm's exp, so that the port equals
TGraphSmooth::SmoothKern and FF_Updated's _smooth_function bitwise (the tests
now require equality). smooth() takes binwise_left/binwise_right instead of
TauFakeFactors' option strings; binwise() is the plain bin curve.
---
 src/shapesmith/measurements/smoothing.py | 76 ++++++++++--------------
 tests/test_smoothing.py                  | 32 +++++-----
 2 files changed, 47 insertions(+), 61 deletions(-)

diff --git a/src/shapesmith/measurements/smoothing.py b/src/shapesmith/measurements/smoothing.py
index f2a1901..44a1d8b 100644
--- a/src/shapesmith/measurements/smoothing.py
+++ b/src/shapesmith/measurements/smoothing.py
@@ -4,12 +4,12 @@
 the state that produced the SM 2018 payload): 100 samples per bin from Normal(y, stat_sigma * sigma) with seed 19,
 each smoothed with ROOT's TGraphSmooth::SmoothKern (normal kernel) on the grid linspace(x[start], x[end], 100 n + 1)
 without its last point; the nominal is the mean and the statistical band the standard deviation over the samples.
-Options: `binwise`, `smoothed`, and hybrids such as `binwise#[0]+smoothed` (the listed bins binwise).
+A hybrid keeps the first or last bins binwise (TauFakeFactors' `binwise#[0]+smoothed`: the first bin).
 tests/reference/make_smoothing_reference.py checks the port against ROOT and FF_Updated.
 """
 from __future__ import annotations
 
-import ast
+import math
 from dataclasses import dataclass
 
 import numpy as np
@@ -34,8 +34,8 @@ def smooth_kern(x: np.ndarray, y: np.ndarray, x_out: np.ndarray, bandwidth: floa
 
     A transcription of ROOT's loop, not a textbook kernel smoother: the window of an output point starts at a running
     index that only moves forward and may still include the last point below x0 - cutoff (ROOT sums it), and ends at
-    the last point <= x0 + cutoff; where no point contributes, the result is 0. `y` may have a second axis (samples),
-    which is smoothed in one go.
+    the last point <= x0 + cutoff; where no point contributes, the result is 0. The sums run in ROOT's order with
+    libm's exp, so that the result is bitwise ROOT's. `y` may have a second axis (samples), smoothed in one go.
     """
     x_out = np.asarray(x_out, dtype=np.float64)
     if np.any(np.diff(x_out) < 0):
@@ -46,48 +46,33 @@ def smooth_kern(x: np.ndarray, y: np.ndarray, x_out: np.ndarray, bandwidth: floa
     cutoff = 4 * width
     result = np.zeros((len(x_out), *y.shape[1:]))
     start = int(np.searchsorted(x, x_out[0] - cutoff, side="left"))
-    for j, x0 in enumerate(x_out):
+    points = x.tolist()
+    for j, x0 in enumerate(x_out.tolist()):
         end = int(np.searchsorted(x, x0 + cutoff, side="right"))
-        w = np.exp(-0.5 * (np.abs(x[start:end] - x0) / width) ** 2)
-        if w.sum() > 0:
-            result[j] = w @ y[start:end] / w.sum()
+        numerator, denominator = 0.0, 0.0
+        for i in range(start, end):
+            distance = abs(points[i] - x0) / width
+            weight = math.exp(-0.5 * distance * distance)
+            numerator = numerator + weight * y[i]
+            denominator += weight
+        if denominator > 0:
+            result[j] = numerator / denominator
         start = max(start, int(np.searchsorted(x, x0 - cutoff, side="left")) - 1)
     return result
 
 
-def hybrid_slices(option: str) -> tuple[int, int, slice, slice, slice]:
-    """FF_Updated `_get_index_and_slices`: the first and last smoothed bin, and the slices of the binwise part on the
-    left, on the right and of the smoothed grid, for an option like `binwise#[0,]#[-1,]+smoothed`."""
-    start, end, left, right = 0, -1, False, False
-    if "binwise" in option and "smoothed" in option:
-        binwise = next(part for part in option.split("+") if "binwise" in part)
-        groups = [ast.literal_eval(group) for group in binwise.split("#")[1:]]
-        if len(groups) == 2:
-            start, end, left, right = groups[0][-1] + 1, groups[1][-1] - 1, True, True
-        elif all(index >= 0 for index in groups[0]):
-            start, left = groups[0][-1] + 1, True
-        elif all(index < 0 for index in groups[0]):
-            end, right = groups[0][-1] - 1, True
-        else:
-            raise ValueError(f"{option}: the binwise bins must be counted from one side")
-    return (
-        start,
-        end,
-        slice(None, start) if left else slice(0, 0),
-        slice(end + 1, None) if right else slice(-1, -1),
-        slice(None, -1) if right else slice(None, None),
-    )
-
-
-def smooth(x: np.ndarray, y: np.ndarray, errors: np.ndarray, bin_edges: np.ndarray, option: str, bandwidth: float, stat_sigma: float = 1.0) -> Curve:
-    """The binwise, smoothed or hybrid curve of a histogram with values `y`, symmetric `errors`, bin `x` positions
-    (the centres of mass) and `bin_edges`; negative values are set to 0, the curve covers the full bin range."""
+def binwise(y: np.ndarray, errors: np.ndarray, bin_edges: np.ndarray, stat_sigma: float = 1.0) -> Curve:
+    """The bins themselves as the curve, with their statistical errors; negative values are set to 0."""
+    y, errors, bin_edges = (np.asarray(a, dtype=np.float64) for a in (y, errors, bin_edges))
+    return _clipped(bin_edges, y, y + stat_sigma * errors, y - stat_sigma * errors, bin_edges)
+
+
+def smooth(x: np.ndarray, y: np.ndarray, errors: np.ndarray, bin_edges: np.ndarray, bandwidth: float, binwise_left: int = 0, binwise_right: int = 0, stat_sigma: float = 1.0) -> Curve:
+    """The smoothed curve of a histogram with values `y`, symmetric `errors`, bin `x` positions (the centres of mass)
+    and `bin_edges`; the first `binwise_left` and the last `binwise_right` bins keep their bin values (a hybrid).
+    Negative values are set to 0, the curve covers the full bin range."""
     x, y, errors, bin_edges = (np.asarray(a, dtype=np.float64) for a in (x, y, errors, bin_edges))
-    if option == "binwise":
-        return _clipped(bin_edges, y, y + stat_sigma * errors, y - stat_sigma * errors, bin_edges)
-    if "smoothed" not in option:
-        raise ValueError(f"unknown fit option {option!r}")
-    start, end, left, right, overlap = hybrid_slices(option)
+    start, end = binwise_left, len(y) - 1 - binwise_right  # the first and the last smoothed bin
     rng = np.random.RandomState(SEED)  # the stream of TauFakeFactors' np.random.seed(19), without global state
     samples = np.array([rng.normal(mu, stat_sigma * sigma, SAMPLES) for mu, sigma in zip(y, errors)])
     samples[samples < 0.0] = 0.0
@@ -95,14 +80,17 @@ def smooth(x: np.ndarray, y: np.ndarray, errors: np.ndarray, bin_edges: np.ndarr
     grid = np.linspace(x[start], x[end], n + 1)
     smoothed = smooth_kern(x, samples, grid[:n], bandwidth)
     mean, std = smoothed.mean(axis=1), smoothed.std(axis=1)
-    if option == "smoothed":
+    if not binwise_left and not binwise_right:
         return _clipped(grid, mean, mean + std, mean - std, bin_edges)
+    left = slice(None, start)
+    right = slice(-binwise_right, None) if binwise_right else slice(0, 0)  # counted from the end, as in TauFakeFactors
+    overlap = slice(None, -1) if binwise_right else slice(None, None)  # the grid ends where the right bins begin
     parts = [(bin_edges[left], y[left], stat_sigma * errors[left])]
-    if start != 0:  # the smoothed part begins at the lower edge of bin `start`, with its first value
+    if binwise_left:  # the smoothed part begins at the lower edge of bin `start`, with its first value
         parts.append((bin_edges[start : start + 1], mean[:1], std[:1]))
     parts.append((grid[overlap], mean[overlap], std[overlap]))
-    if end != -1:  # and ends at the lower edge of bin `end + 1`, with its last value
-        parts.append((bin_edges[end : end + 1], mean[-1:], std[-1:]))
+    if binwise_right:  # and ends at the lower edge of bin `end + 1`, with its last value
+        parts.append((bin_edges[end + 1 : end + 2], mean[-1:], std[-1:]))
     parts.append((bin_edges[right], y[right], stat_sigma * errors[right]))
     edges, nominal, band = (np.concatenate(column) for column in zip(*parts))
     return _clipped(edges, nominal, nominal + band, nominal - band, bin_edges)
diff --git a/tests/test_smoothing.py b/tests/test_smoothing.py
index 3f34baa..131ef54 100644
--- a/tests/test_smoothing.py
+++ b/tests/test_smoothing.py
@@ -5,26 +5,33 @@
 import numpy as np
 import pytest
 
-from shapesmith.measurements.smoothing import hybrid_slices, smooth, smooth_kern
+from shapesmith.measurements.smoothing import binwise, smooth, smooth_kern
 
 REFERENCE = json.loads((Path(__file__).parent / "reference" / "smoothing_root.json").read_text())
+BINWISE_BINS = {  # TauFakeFactors option -> the number of binwise bins on the left and on the right
+    "smoothed": (0, 0), "binwise#[0]+smoothed": (1, 0), "binwise#[-1]+smoothed": (0, 1), "binwise#[0,]#[-1,]+smoothed": (1, 1),
+}
 
 
-def _close(a, b) -> bool:
-    a, b = np.asarray(a), np.asarray(b)
-    return a.shape == b.shape and bool(np.all(np.abs(a - b) <= 1e-12 * np.maximum(np.abs(b), 1e-300)))
+def _equal(a, b) -> bool:
+    """Bitwise: the sums run in ROOT's order with libm's exp."""
+    return np.array_equal(np.asarray(a), np.asarray(b))
 
 
 @pytest.mark.parametrize("case", REFERENCE["kernel"], ids=lambda c: f"n{len(c['x'])}_bw{c['bandwidth']}")
 def test_kernel_matches_root(case):
-    assert _close(smooth_kern(np.array(case["x"]), np.array(case["y"]), np.array(case["x_out"]), case["bandwidth"]), case["y_out"])
+    assert _equal(smooth_kern(np.array(case["x"]), np.array(case["y"]), np.array(case["x_out"]), case["bandwidth"]), case["y_out"])
 
 
 @pytest.mark.parametrize("case", REFERENCE["smooth"], ids=lambda c: f"{c['option']}_bw{c['bandwidth']}_n{len(c['y'])}")
 def test_smoothing_matches_tau_fake_factors(case):
-    curve = smooth(case["x"], case["y"], case["errors"], case["bin_edges"], case["option"], case["bandwidth"])
+    if case["option"] == "binwise":
+        curve = binwise(case["y"], case["errors"], case["bin_edges"])
+    else:
+        left, right = BINWISE_BINS[case["option"]]
+        curve = smooth(case["x"], case["y"], case["errors"], case["bin_edges"], case["bandwidth"], left, right)
     for name in ("edges", "nominal", "up", "down"):
-        assert _close(getattr(curve, name), case[name]), name
+        assert _equal(getattr(curve, name), case[name]), name
 
 
 def test_the_kernel_sums_the_last_point_left_of_the_window_as_root_does():
@@ -41,16 +48,7 @@ def test_the_kernel_smooths_all_samples_at_once():
         smooth_kern(x, samples, np.array([3.0, 1.5]), 2.0)
 
 
-def test_hybrid_slices():
-    assert hybrid_slices("smoothed") == (0, -1, slice(0, 0), slice(-1, -1), slice(None, None))
-    assert hybrid_slices("binwise#[0,1]+smoothed") == (2, -1, slice(None, 2), slice(-1, -1), slice(None, None))
-    assert hybrid_slices("binwise#[-1]+smoothed") == (0, -2, slice(0, 0), slice(-1, None), slice(None, -1))
-    assert hybrid_slices("binwise#[0,]#[-1,]+smoothed") == (1, -2, slice(None, 1), slice(-1, None), slice(None, -1))
-    with pytest.raises(ValueError, match="one side"):
-        hybrid_slices("binwise#[0,-1]+smoothed")
-
-
 def test_binwise_keeps_the_bins_and_clips_at_zero():
-    curve = smooth([0.5, 1.5], [0.2, -0.1], [0.3, 0.05], [0.0, 1.0, 2.0], "binwise", 1.0)
+    curve = binwise([0.2, -0.1], [0.3, 0.05], [0.0, 1.0, 2.0])
     assert curve.edges.tolist() == [0.0, 1.0, 2.0] and curve.nominal.tolist() == [0.2, 0.0]
     assert curve.up.tolist() == pytest.approx([0.5, 0.0]) and curve.down.tolist() == [0.0, 0.0]

From 4ae4ec2f082c48c900d555dbe1518c6a6d689896 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 07:44:29 +0200
Subject: [PATCH 18/33] feat(model): an analysis without a signal process

A measurement analysis (fake factors, tau ID) has no signal; Analysis.signal
may be None and validate() checks it only when set.
---
 src/shapesmith/model.py    | 2 +-
 src/shapesmith/validate.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/shapesmith/model.py b/src/shapesmith/model.py
index 649ba5d..1ded261 100644
--- a/src/shapesmith/model.py
+++ b/src/shapesmith/model.py
@@ -250,7 +250,7 @@ class Analysis:
     name: str
     era: str
     lumi_pb: float
-    signal: str  # the signal process of datacards and plots
+    signal: str | None  # the signal process of datacards and plots (None: a measurement analysis without one)
     channels: Mapping[str, Channel]
     lnn: tuple[LnN, ...] = ()
     style: Style | None = None
diff --git a/src/shapesmith/validate.py b/src/shapesmith/validate.py
index fe42697..adc3e2c 100644
--- a/src/shapesmith/validate.py
+++ b/src/shapesmith/validate.py
@@ -88,7 +88,7 @@ def validate(analysis: Analysis) -> None:
     """Raise AnalysisError listing every problem of the analysis."""
     problems = [problem for channel in analysis.channels.values() for problem in channel_problems(channel)]
     processes = {p.name: p.role for channel in analysis.channels.values() for p in channel.processes}
-    if analysis.signal not in processes:
+    if analysis.signal is not None and analysis.signal not in processes:
         problems.append(f"signal {analysis.signal} is not a process")
     if analysis.ml is not None:
         problems += [f"ML export: {name} is an auxiliary process" for name in analysis.ml.processes if processes.get(name) == "auxiliary"]

From 062e02cecd122d45cbe85f380a02adeb4bfdb990 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 07:44:29 +0200
Subject: [PATCH 19/33] feat(payloads): compound corrections

---
 src/shapesmith/payloads.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/src/shapesmith/payloads.py b/src/shapesmith/payloads.py
index c8e7819..880ff3d 100644
--- a/src/shapesmith/payloads.py
+++ b/src/shapesmith/payloads.py
@@ -40,8 +40,14 @@ def correction(name: str, inputs: Sequence[cs.Variable], data: Node, version: in
     return cs.Correction(name=name, version=version, inputs=list(inputs), output=output or variable("weight"), data=data, **fields)
 
 
-def correction_set(corrections: Sequence[cs.Correction], provenance: Mapping) -> cs.CorrectionSet:
-    return cs.CorrectionSet(schema_version=2, description=json.dumps(provenance, sort_keys=True), corrections=list(corrections))
+def compound(name: str, inputs: Sequence[cs.Variable], stack: Sequence[str], output: cs.Variable | None = None) -> cs.CompoundCorrection:
+    """The product of the corrections in `stack`, each evaluated on its own inputs (taken from `inputs` by name)."""
+    return cs.CompoundCorrection(name=name, inputs=list(inputs), output=output or variable("weight"), inputs_update=[], input_op="*", output_op="*", stack=list(stack))
+
+
+def correction_set(corrections: Sequence[cs.Correction], provenance: Mapping, compound_corrections: Sequence[cs.CompoundCorrection] = ()) -> cs.CorrectionSet:
+    fields = {"compound_corrections": list(compound_corrections)} if compound_corrections else {}
+    return cs.CorrectionSet(schema_version=2, description=json.dumps(provenance, sort_keys=True), corrections=list(corrections), **fields)
 
 
 def provenance(cset: cs.CorrectionSet) -> dict:

From 3dd72d7a6530f5b508c8997eace0dc1f94df428c Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 07:44:29 +0200
Subject: [PATCH 20/33] feat(fake_factors): measurement model, histograms and
 fits

The model of the SM fake-factor measurement (FM-12): Fit, Split, Binned,
ProcessFF with its DR->SR correction and data/MC scale, Fractions, Leg and
FakeFactorMeasurement. hist.Hist is TauFakeFactors' patched TH1D without ROOT:
contents, Sumw2, the centre of mass and the MC-suppressed errors (FM-11).
fit.fit turns a ratio into the stored steps with StatShift, SystMCShift,
SystBand{High,Low,Asym}, the compatibility-with-1 reset and sparsify (FM-2,
FM-3, FM-14). Both are checked against ROOT with FF_Updated's patches
(tests/reference/make_fake_factor_reference.py).
---
 .../measurements/fake_factors/__init__.py     |  13 ++
 .../measurements/fake_factors/fit.py          | 137 +++++++++++++++
 .../measurements/fake_factors/hist.py         | 130 ++++++++++++++
 .../measurements/fake_factors/model.py        | 155 +++++++++++++++++
 tests/reference/fake_factor_root.json         |   1 +
 tests/reference/make_fake_factor_reference.py | 161 ++++++++++++++++++
 tests/test_fake_factor_fit.py                 |  68 ++++++++
 tests/test_fake_factor_hist.py                |  60 +++++++
 8 files changed, 725 insertions(+)
 create mode 100644 src/shapesmith/measurements/fake_factors/__init__.py
 create mode 100644 src/shapesmith/measurements/fake_factors/fit.py
 create mode 100644 src/shapesmith/measurements/fake_factors/hist.py
 create mode 100644 src/shapesmith/measurements/fake_factors/model.py
 create mode 100644 tests/reference/fake_factor_root.json
 create mode 100644 tests/reference/make_fake_factor_reference.py
 create mode 100644 tests/test_fake_factor_fit.py
 create mode 100644 tests/test_fake_factor_hist.py

diff --git a/src/shapesmith/measurements/fake_factors/__init__.py b/src/shapesmith/measurements/fake_factors/__init__.py
new file mode 100644
index 0000000..e9025a3
--- /dev/null
+++ b/src/shapesmith/measurements/fake_factors/__init__.py
@@ -0,0 +1,13 @@
+"""The fake-factor measurement (the SM method of TauFakeFactors): model in model.py, the chain in measure.py."""
+from shapesmith.measurements.fake_factors.model import (  # noqa: F401
+    Binned,
+    DataScale,
+    DrSr,
+    Equipopulated,
+    FakeFactorMeasurement,
+    Fit,
+    Fractions,
+    Leg,
+    ProcessFF,
+    Split,
+)
diff --git a/src/shapesmith/measurements/fake_factors/fit.py b/src/shapesmith/measurements/fake_factors/fit.py
new file mode 100644
index 0000000..67253a0
--- /dev/null
+++ b/src/shapesmith/measurements/fake_factors/fit.py
@@ -0,0 +1,137 @@
+"""From a measured ratio to the stored step functions with their uncertainty variations (TauFakeFactors'
+smooth_function, FF_Updated helper/ff_functions.py sha256 31f89add...).
+
+The variations are StatShift (the statistical band), SystMCShift (the ratio with every subtracted process shifted by
++-1 sigma, fitted the same way), SystBandHigh/Low (the curve with bandwidth x1.5/x0.5, Down its mirror around the
+nominal) and SystBandAsym (Up the x1.5 and Down the x0.5 curve). A smoothed ratio that is compatible with 1
+(statistical_check) is reset to 1; smoothed curves are then sparsified. The spec's "Algorithm reference" states
+every rule.
+"""
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass, replace
+
+import numpy as np
+
+from shapesmith.measurements import smoothing
+from shapesmith.measurements.fake_factors.hist import Hist
+from shapesmith.measurements.fake_factors.model import Fit
+
+STAT, MC, BAND_HIGH, BAND_LOW, BAND_ASYM = "StatShift", "SystMCShift", "SystBandHigh", "SystBandLow", "SystBandAsym"
+BANDWIDTH_FACTORS = {BAND_HIGH: 1.5, BAND_LOW: 0.5}
+RESET_P_VALUE = 0.05  # a smoothed ratio with a larger p-value of the compatibility with 1 is set to 1
+SPARSIFY_THRESHOLD = 0.01  # a stored bin edge per 1 % of the y range of the variation
+
+
+@dataclass(frozen=True)
+class Step:
+    """values[i] on [edges[i], edges[i + 1])."""
+
+    edges: np.ndarray
+    values: np.ndarray
+
+
+@dataclass(frozen=True)
+class Fitted:
+    """The fitted ratio: the curve at full resolution (plots) and the stored steps (payload), with variations."""
+
+    curve: Step
+    variations: dict[str, np.ndarray]  # on curve.edges
+    stored: Step
+    stored_variations: dict[str, Step]
+    p_value: float | None = None  # of the compatibility with 1 (smoothed fits only)
+    reset: bool = False  # compatible with 1, set to 1
+
+
+def fit(ratio: Hist, fit: Fit, mc_shifted: tuple[Hist, Hist] | None = None, stat_sigma: float = 1.0) -> Fitted:
+    """The ratio as binwise or smoothed steps with all variations; `mc_shifted` are the ratios with the subtracted
+    processes shifted up and down (None: nothing is subtracted, SystMCShift is the nominal)."""
+    measured = curve(ratio, fit, stat_sigma)
+    nominal = measured.nominal
+    variations = {f"{STAT}Up": measured.up, f"{STAT}Down": measured.down}
+    shifted = [curve(h, fit, stat_sigma).nominal for h in mc_shifted] if mc_shifted else [nominal, nominal]
+    variations.update({f"{MC}Up": shifted[0], f"{MC}Down": shifted[1]})
+    if fit.kind == "binwise":
+        variations.update({f"{band}{direction}": nominal for band in (BAND_HIGH, BAND_LOW, BAND_ASYM) for direction in ("Up", "Down")})
+        steps = {name: Step(measured.edges, values) for name, values in variations.items()}
+        return Fitted(Step(measured.edges, nominal), variations, Step(measured.edges, nominal), steps)
+    bands = {band: curve(ratio, replace(fit, bandwidth=fit.bandwidth * factor), stat_sigma).nominal for band, factor in BANDWIDTH_FACTORS.items()}
+    for band, values in bands.items():
+        variations[f"{band}Up"] = (values - nominal) + nominal
+        variations[f"{band}Down"] = (nominal - values) + nominal
+    variations.update({f"{BAND_ASYM}Up": bands[BAND_HIGH], f"{BAND_ASYM}Down": bands[BAND_LOW]})
+    p_value = compatibility_with_one(ratio)
+    reset = p_value > RESET_P_VALUE
+    if reset:
+        nominal, variations = _reset(ratio, nominal, variations, mc_shifted, stat_sigma)
+    stored = Step(*sparsify(measured.edges, nominal))
+    stored_variations = {name: Step(*sparsify(measured.edges, values)) for name, values in variations.items()}
+    return Fitted(Step(measured.edges, nominal), variations, stored, stored_variations, p_value, reset)
+
+
+def curve(ratio: Hist, fit: Fit, stat_sigma: float) -> smoothing.Curve:
+    """The full-resolution curve of a ratio with its statistical band, at the centres of mass."""
+    if fit.kind == "binwise":
+        return smoothing.binwise(ratio.values, ratio.errors, ratio.edges, stat_sigma)
+    return smoothing.smooth(ratio.means, ratio.values, ratio.errors, ratio.edges, fit.bandwidth, fit.binwise_left, fit.binwise_right, stat_sigma)
+
+
+def compatibility_with_one(ratio: Hist) -> float:
+    """TauFakeFactors' statistical_check: the pulls (y - 1) / sigma with the data-only (suppressed) errors; the
+    smallest two-sided p-value of the pull sum over all windows of adjacent bins, corrected for the number of bins."""
+    errors = ratio.suppressed_errors.clip(min=1e-6)
+    pulls = (ratio.values - 1.0) / errors
+    n = len(pulls)
+    p_min = 1.0
+    for width in range(1, n + 1):
+        for start in range(n - width + 1):
+            z = abs(np.sum(pulls[start : start + width]) / np.sqrt(width))
+            p_min = min(p_min, math.erfc(z / math.sqrt(2.0)))  # 2 * (1 - Phi(z))
+    return 1 - (1 - p_min) ** n
+
+
+def _reset(ratio: Hist, nominal: np.ndarray, variations: dict[str, np.ndarray], mc_shifted: tuple[Hist, Hist] | None, stat_sigma: float) -> tuple[np.ndarray, dict[str, np.ndarray]]:
+    """A ratio compatible with 1: nominal and band variations 1, the statistical band that of the inverse-variance
+    mean, SystMCShift the constant fitted to each shifted ratio (1 without shifted ratios)."""
+    errors = ratio.suppressed_errors.clip(min=1e-6)
+    inclusive = 1.0 / np.sqrt(np.sum(1.0 / errors**2))
+    ones = np.ones_like(nominal)
+    result = {}
+    for name, values in variations.items():
+        if name.startswith((BAND_ASYM, BAND_HIGH, BAND_LOW)):
+            result[name] = ones
+        elif name.startswith(STAT):
+            result[name] = np.full_like(values, 1.0 + (stat_sigma if name.endswith("Up") else -stat_sigma) * inclusive)
+        else:
+            result[name] = values
+    constants = [constant_fit(h) for h in mc_shifted] if mc_shifted else [1.0, 1.0]
+    result[f"{MC}Up"], result[f"{MC}Down"] = (np.full_like(nominal, constant) for constant in constants)
+    return ones, result
+
+
+def constant_fit(hist: Hist) -> float:
+    """ROOT's pol0 chi2 fit of the bin values: the inverse-variance weighted mean of the bins with an error; 1 if
+    there is none (the fit fails)."""
+    errors = hist.errors
+    used = errors > 0
+    if not used.any():
+        return 1.0
+    weights = 1.0 / errors[used] ** 2
+    return float(np.sum(weights * hist.values[used]) / np.sum(weights))
+
+
+def sparsify(edges: np.ndarray, values: np.ndarray, threshold: float = SPARSIFY_THRESHOLD) -> tuple[np.ndarray, np.ndarray]:
+    """TauFakeFactors' sparsify: keep a bin edge wherever the cumulative |change| of the values passes another
+    `threshold` times their range, and the first and the last bin; constant values become one bin."""
+    cumulative = np.concatenate(([0], np.cumsum(np.abs(np.diff(values)))))
+    step = (values.max() - values.min()) * threshold
+    if step == 0:
+        return np.array([edges[0], edges[-1]]), np.array([values[0]])
+    kept = np.unique(np.searchsorted(cumulative, np.arange(0, cumulative[-1], step)))
+    kept = kept[kept < len(values)]
+    if 0 not in kept:
+        kept = np.insert(kept, 0, 0)
+    if len(values) - 1 not in kept:
+        kept = np.append(kept, len(values) - 1)
+    return np.append(edges[kept], edges[-1]), values[kept]
diff --git a/src/shapesmith/measurements/fake_factors/hist.py b/src/shapesmith/measurements/fake_factors/hist.py
new file mode 100644
index 0000000..7e42e7a
--- /dev/null
+++ b/src/shapesmith/measurements/fake_factors/hist.py
@@ -0,0 +1,130 @@
+"""The histogram of the fake-factor measurement: TauFakeFactors' patched ROOT TH1D, without ROOT.
+
+Besides ROOT's bin contents and Sumw2 (`values`, `variances`) a histogram carries per bin the weighted event count
+and mean of x (`counts`, `means`: the centre of mass, the x position of the bin in the fits) and TauFakeFactors'
+"base" values with their errors, of which `suppressed_errors` keep only the errors of the minuend of every
+subtraction (the data errors, the input of the compatibility check). Every operation follows ROOT and FF_Updated
+helper/hooks_and_patches.py (sha256 79acd651..., the state of the SM 2018 payload): `add` and `divide` are its
+patched TH1.Add/Divide, `shifted` is its AddError, `scaled` is ROOT's unpatched TH1.Scale (contents and Sumw2
+only). tests/reference/make_fake_factor_reference.py checks the port against ROOT with the patches.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass, replace
+
+import numpy as np
+
+
+@dataclass(frozen=True)
+class Hist:
+    edges: np.ndarray
+    values: np.ndarray
+    variances: np.ndarray
+    counts: np.ndarray
+    means: np.ndarray
+    base_values: np.ndarray
+    base_errors: np.ndarray
+    suppressed_errors: np.ndarray
+
+    @classmethod
+    def fill(cls, edges, x: np.ndarray, weights: np.ndarray) -> Hist:
+        """ROOT's TH1D::Fill per event (bins [low, high), in event order) and TauFakeFactors' centre of mass
+        (np.histogram, whose last bin includes its upper edge)."""
+        edges = np.asarray(edges, dtype=np.float64)
+        x, weights = np.asarray(x), np.asarray(weights, dtype=np.float64)
+        n = len(edges) - 1
+        index = np.searchsorted(edges, x, side="right") - 1
+        inside = (index >= 0) & (index < n)
+        values = np.bincount(index[inside], weights=weights[inside], minlength=n)
+        variances = np.bincount(index[inside], weights=weights[inside] ** 2, minlength=n)
+        counts, _ = np.histogram(x, bins=edges, weights=weights)
+        sums, _ = np.histogram(x, bins=edges, weights=x * weights)
+        centres = 0.5 * (edges[:-1] + edges[1:])
+        means = np.array([s / c if c != 0 else centre for s, c, centre in zip(sums, counts, centres)])
+        errors = np.sqrt(variances)
+        return cls(edges, values, variances, counts, means, values.copy(), errors, errors.copy())
+
+    @property
+    def errors(self) -> np.ndarray:
+        return np.sqrt(self.variances)
+
+    @property
+    def centres(self) -> np.ndarray:
+        return 0.5 * (self.edges[:-1] + self.edges[1:])
+
+    def add(self, other: Hist, factor: float = 1.0) -> Hist:
+        """self + factor * other; a subtraction (factor < 0) keeps the suppressed errors of self."""
+        counts, means = self._centre_of_mass(other, factor, self.counts + factor * other.counts)
+        base_errors = np.sqrt(self.base_errors**2 + (factor * other.base_errors) ** 2)
+        suppressed = self.suppressed_errors.copy() if factor < 0 else np.sqrt(self.suppressed_errors**2 + (factor * other.suppressed_errors) ** 2)
+        return Hist(
+            self.edges, self.values + factor * other.values, self.variances + factor * factor * other.variances,
+            counts, means, self.base_values + factor * other.base_values, base_errors, suppressed,
+        )
+
+    def divide(self, other: Hist) -> Hist:
+        """self / other bin by bin, 0 where other is 0 (ROOT's TH1::Divide); the centre of mass of both combined."""
+        c0, c1 = self.values, other.values
+        nonzero = c1 != 0
+        values = np.divide(c0, c1, out=np.zeros_like(c0), where=nonzero)
+        c1sq = c1 * c1
+        variances = np.divide(self.variances * c1sq + other.variances * c0 * c0, c1sq * c1sq, out=np.zeros_like(c0), where=nonzero)
+        ratio_counts = np.divide(self.counts, other.counts, out=np.zeros_like(self.counts), where=other.counts != 0)
+        counts, means = self._centre_of_mass(other, 1.0, ratio_counts)
+        base_values = np.divide(self.base_values, other.base_values, out=np.zeros_like(self.base_values), where=other.base_values != 0)
+        return Hist(
+            self.edges, values, variances, counts, means, base_values,
+            _relative_sum(base_values, (self.base_errors, self.base_values), (other.base_errors, other.base_values)),
+            _relative_sum(base_values, (self.suppressed_errors, self.base_values), (other.suppressed_errors, other.base_values)),
+        )
+
+    def scaled(self, factor: float) -> Hist:
+        """ROOT's TH1::Scale: contents and Sumw2 only (TauFakeFactors does not patch it)."""
+        return replace(self, values=self.values * factor, variances=self.variances * (factor * factor))
+
+    def shifted(self, sigmas: float) -> Hist:
+        """Every bin moved by `sigmas` times its error and clipped at 0 (TauFakeFactors' AddError); the error of a
+        clipped bin is 0, and so is its count, whose mean moves to the bin centre. The base values are not clipped."""
+        errors = self.errors
+        values = np.maximum(0.0, self.values + sigmas * errors)
+        variances = np.where(values > 0, errors, 0.0) ** 2
+        moved = sigmas * (abs(sigmas) * errors)
+        counts = self.counts + moved
+        means = np.divide(self.counts * self.means + moved * self.means, counts, out=self.centres.copy(), where=counts != 0)
+        means = np.where(counts < 0, self.centres, _clip_means(means, self.edges))
+        return Hist(
+            self.edges, values, variances, np.maximum(0.0, counts), means,
+            self.base_values + sigmas * self.base_errors, self.base_errors.copy(), self.suppressed_errors.copy(),
+        )
+
+    def with_negative_bins_zeroed(self) -> Hist:
+        """Negative contents set to 0 (errors, centre of mass and base values unchanged)."""
+        return replace(self, values=np.maximum(self.values, 0.0))
+
+    def with_empty_bins_set_to_one(self) -> Hist:
+        """Bins <= 0 become 1 +- 1 with the bin centre as their mean and count 1 (TauFakeFactors'
+        calculate_non_closure_correction); the base values are unchanged."""
+        empty = self.values <= 0.0
+        return replace(
+            self, values=np.where(empty, 1.0, self.values), variances=np.where(empty, 1.0, self.variances),
+            counts=np.where(empty, 1.0, self.counts), means=np.where(empty, self.centres, self.means),
+        )
+
+    def _centre_of_mass(self, other: Hist, factor: float, counts: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        """TauFakeFactors' calc_center_of_mass: the means weighted with the counts of both, inside the bin."""
+        total = self.counts + factor * other.counts
+        weighted = self.counts * self.means + factor * other.counts * other.means
+        means = np.divide(weighted, total, out=self.centres.copy(), where=total != 0)
+        return counts, np.where(total != 0, _clip_means(means, self.edges), means)
+
+
+def _clip_means(means: np.ndarray, edges: np.ndarray) -> np.ndarray:
+    """Means kept strictly inside their bins."""
+    low, high = edges[:-1], edges[1:]
+    return np.maximum(np.minimum(means, np.nextafter(high, low)), np.nextafter(low, high))
+
+
+def _relative_sum(values: np.ndarray, *parts: tuple[np.ndarray, np.ndarray]) -> np.ndarray:
+    """|values| times the quadratic sum of the relative errors error / value of the parts (0 where value is 0)."""
+    squares = sum(np.divide(error, value, out=np.zeros_like(error), where=value != 0) ** 2 for error, value in parts)
+    return np.abs(values) * np.sqrt(squares)
diff --git a/src/shapesmith/measurements/fake_factors/model.py b/src/shapesmith/measurements/fake_factors/model.py
new file mode 100644
index 0000000..f840f75
--- /dev/null
+++ b/src/shapesmith/measurements/fake_factors/model.py
@@ -0,0 +1,155 @@
+"""The fake-factor measurement as data: the SM method of TauFakeFactors (QCD and ttbar fake factors, their fractions,
+the QCD DR->SR correction and the non-closure corrections), built by an analysis from its regions and tables.
+
+Regions and processes are names in the analysis channel; every quantity is measured per category of one split
+variable (n_jets), in bins of its own variable.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Literal, Mapping
+
+
+@dataclass(frozen=True)
+class Fit:
+    """How a measured ratio becomes the stored function: `binwise` keeps the bins; `smoothed` is the kernel
+    smoothing with `bandwidth`, where the first `binwise_left` and the last `binwise_right` bins keep their values."""
+
+    kind: Literal["binwise", "smoothed"]
+    bandwidth: float = 0.0
+    binwise_left: int = 0
+    binwise_right: int = 0
+
+
+@dataclass(frozen=True)
+class Equipopulated:
+    """The --suggest-binning options of a quantity (TauFakeFactors' equipopulated_binning_options): `n_bins` per
+    category over [low, high], edges rounded to `rounding` digits, `add_left` edges put in front."""
+
+    n_bins: tuple[int, ...]
+    low: float
+    high: float
+    rounding: int = 2
+    add_left: tuple[float, ...] = ()
+
+
+@dataclass(frozen=True)
+class Split:
+    """Category i holds the events with edges[i] <= variable < edges[i + 1]; the payload bins on the same edges."""
+
+    variable: str
+    edges: tuple[float, ...]
+
+    @property
+    def categories(self) -> range:
+        return range(len(self.edges) - 1)
+
+
+@dataclass(frozen=True)
+class Binned:
+    """A quantity in bins of `variable`: edges[i] and fits[i] in category i of the split (fractions have no fits)."""
+
+    variable: str
+    edges: tuple[tuple[float, ...], ...]
+    fits: tuple[Fit, ...] = ()
+    equipopulated: Equipopulated | None = None
+
+
+@dataclass(frozen=True)
+class DrSr:
+    """The QCD DR->SR correction. Orthogonal fake factors (the QCD variable, bins and fits) from data in
+    `sr_like`/`ar_like`, with their own `non_closures` there; the correction is the SR-like yield over the
+    orthogonal-FF weighted AR-like yield in `sr`/`ar`. Every step subtracts `subtract`."""
+
+    sr_like: str
+    ar_like: str
+    sr: str
+    ar: str
+    subtract: tuple[str, ...]
+    correction: Binned
+    non_closures: tuple[Binned, ...] = ()
+
+
+@dataclass(frozen=True)
+class DataScale:
+    """The global data/MC factor of an MC fake factor: (data - the MC but the target - QCD)(sr_like) / (...)(ar_like)
+    over the same ratio of the target, from event totals; QCD is data minus all MC in the same-sign version of each
+    region. `subtract` lists all MC processes, the target among them, in subtraction order."""
+
+    sr_like: str
+    ar_like: str
+    sr_like_same_sign: str
+    ar_like_same_sign: str
+    subtract: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class ProcessFF:
+    """The fake factors of one process, `target`(sr_like) / `target`(ar_like) with target = the `target` process
+    minus the `subtract` processes: from data (QCD), or from MC scaled by `scale` (ttbar). The non-closures are
+    measured in the same regions in table order, each on top of the fake factors, the DR->SR correction and the
+    earlier non-closures."""
+
+    name: str  # the payload process: QCD or ttbar
+    target: str
+    subtract: tuple[str, ...]
+    sr_like: str
+    ar_like: str
+    split: Split
+    fake_factors: Binned
+    non_closures: tuple[Binned, ...] = ()
+    dr_sr: DrSr | None = None
+    scale: DataScale | None = None
+
+
+@dataclass(frozen=True)
+class Fractions:
+    """The QCD and ttbar fractions in `region`: QCD = data - `subtract` (negative bins 0), ttbar = `ttbar`."""
+
+    region: str
+    subtract: tuple[str, ...]
+    ttbar: str
+    split: Split
+    binned: Binned
+
+
+@dataclass(frozen=True)
+class Leg:
+    """The fake factors of one hadronic tau: payload names carry `suffix` ("" or "_subleading")."""
+
+    suffix: str
+    qcd: ProcessFF
+    ttbar: ProcessFF
+    fractions: Fractions
+
+    @property
+    def processes(self) -> tuple[ProcessFF, ProcessFF]:
+        return (self.qcd, self.ttbar)
+
+
+@dataclass(frozen=True)
+class FakeFactorMeasurement:
+    """`shapesmith measure` for the fake factors of every channel: the payloads fake_factors_<ch>.json.gz and
+    FF_corrections_<ch>.json.gz in <output_dir>/fake_factors/<era>/."""
+
+    legs: Mapping[str, tuple[Leg, ...]]  # channel -> legs
+    version: int = 1  # Correction.version of the payloads
+    stat_sigma: float = 1.0
+    name: str = field(default="fake_factors", init=False)
+
+    def columns(self, channel: str) -> set[str]:
+        """The columns every quantity of the channel is measured and evaluated in."""
+        columns = set()
+        for leg in self.legs[channel]:
+            columns |= {leg.fractions.split.variable, leg.fractions.binned.variable}
+            for process in leg.processes:
+                binned = [process.fake_factors, *process.non_closures]
+                if process.dr_sr is not None:
+                    binned += [process.dr_sr.correction, *process.dr_sr.non_closures]
+                columns |= {process.split.variable, *(b.variable for b in binned)}
+        return columns
+
+    def run(self, context) -> None:
+        from shapesmith.measurements.fake_factors.measure import run  # plots and correctionlib load only for a run
+
+        run(self, context)
diff --git a/tests/reference/fake_factor_root.json b/tests/reference/fake_factor_root.json
new file mode 100644
index 0000000..baac5b8
--- /dev/null
+++ b/tests/reference/fake_factor_root.json
@@ -0,0 +1 @@
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"SystBandAsymUp": {"edges": [0.0, 1.0, 2.0, 3.5, 4.1251999999999995, 4.2008, 4.2764, 4.3435999999999995, 4.4108, 4.4738, 4.5367999999999995, 4.5956, 4.6544, 4.709, 4.7636, 4.8182, 4.8686, 4.919, 4.9694, 5.0198, 5.0702, 5.1206, 5.171, 5.2214, 5.2718, 5.3222000000000005, 5.3726, 5.423, 5.4776, 5.5322000000000005, 5.591, 5.6498, 5.7128, 5.78, 5.8472, 5.9228000000000005, 6.0068, 6.095000000000001, 6.2, 8.0], "values": [1.25, 1.1, 0.95, 0.9037957052517102, 0.9020897622162675, 0.8968304758395369, 0.8913375393990784, 0.8862390388647785, 0.8809229378495389, 0.8757326678884892, 0.8703380445608678, 0.8651182910350411, 0.8597233327015585, 0.8545627185243034, 0.8492651526577436, 0.8438417350590932, 0.8387357208369108, 0.8335470803651023, 0.8282903969568229, 0.8229817897713333, 0.8176386679293931, 0.8122794446999663, 0.8069232204870453, 0.8015894451071247, 0.7962975710265858, 0.7910667097205755, 0.7859153030785287, 0.7808608208402936, 0.7755132981512546, 0.7703172376037495, 0.7649096396139194, 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0.7801730537213021, 0.7737566092179066, 0.7674948107600374, 0.7614315886502212, 0.75560499083441, 0.7500464369951633, 0.744780339580792, 0.7398240735016686, 0.7351882507967461, 0.7288433646670924, 0.723220181479221, 0.7182889999590636, 0.7127108443215465, 0.707151498137123, 0.7015095626551203, 0.6961802836283632, 0.6906986735528369]}}}, "constants": [1.1587108537086872, 1.0492714802016148]}]}
\ No newline at end of file
diff --git a/tests/reference/make_fake_factor_reference.py b/tests/reference/make_fake_factor_reference.py
new file mode 100644
index 0000000..c29f629
--- /dev/null
+++ b/tests/reference/make_fake_factor_reference.py
@@ -0,0 +1,161 @@
+"""Write fake_factor_root.json: the reference of tests/test_fake_factor_hist.py and tests/test_fake_factor_fit.py,
+computed once with ROOT and jvoss's TauFakeFactors copy /work/jvoss/FF_Updated (run manually, like
+make_smoothing_reference.py):
+
+    python tests/reference/make_fake_factor_reference.py
+
+- `hists`: events of data and three MC samples in an SR-like and an AR-like region, filled with FF_Updated's patched
+  Histo1D (centre of mass); the data minus MC yields (also with the MC shifted by +-1 sigma, AddError), their ratio,
+  a scaled ratio (unpatched Scale), QCD_SS_estimate and calculate_non_closure_correction (bins <= 0 set to 1 +- 1).
+- `fits`: FF_Updated smooth_function on such ratios for every fit option, with and without MC-shifted ratios,
+  including ratios compatible with 1 (reset by statistical_check); and fit_to_constant.
+"""
+import hashlib
+import json
+import sys
+import types
+from pathlib import Path
+
+import numpy as np
+import ROOT
+
+FF_UPDATED = Path("/work/jvoss/FF_Updated")
+EDGES = [0.0, 1.0, 2.0, 3.5, 5.0, 8.0]
+SAMPLES = ("data", "mc_a", "mc_b", "mc_c")
+
+
+def import_tau_fake_factors():
+    wurlitzer = types.ModuleType("wurlitzer")  # imported by ff_functions for log capturing only
+    wurlitzer.STDOUT, wurlitzer.pipes = None, None
+    sys.modules["wurlitzer"] = wurlitzer
+    sys.path.insert(0, str(FF_UPDATED))
+    import helper.ff_functions as ff_functions
+    import helper.hooks_and_patches as patches
+
+    return ff_functions, patches
+
+
+def events(rng, n: int, data: bool) -> dict:
+    x = rng.uniform(-0.5, 8.5, n).astype(np.float32)
+    x[:3] = np.array([EDGES[1], EDGES[-1], np.float32(3.5)], dtype=np.float32)  # on bin edges, the last one included by numpy only
+    weights = np.ones(n) if data else rng.normal(0.3, 0.25, n)  # MC with some negative weights
+    return {"x": x, "w": weights}
+
+
+def fill(patches, sample: dict, name: str):
+    frame = ROOT.RDF.FromNumpy({"x": sample["x"], "w": sample["w"].astype(np.float64)})
+    return patches.Histo1DPatchedRDataFrame(frame).Histo1D(("x", name, len(EDGES) - 1, np.array(EDGES)), "x", "w").GetValue()
+
+
+def dump(patches, hist) -> dict:
+    n = hist.GetNbinsX()
+    return {
+        "values": [hist.GetBinContent(i) for i in range(1, n + 1)],
+        "errors": [hist.GetBinError(i) for i in range(1, n + 1)],
+        "counts": list(map(float, getattr(hist, patches._EXTRA_PARAM_COUNTS))),
+        "means": list(map(float, getattr(hist, patches._EXTRA_PARAM_MEANS))),
+        "base_values": getattr(hist, patches._EXTRA_PARAM_BASE_VALUES).tolist(),
+        "base_errors": getattr(hist, patches._EXTRA_PARAM_BASE_ERRORS_STD).tolist(),
+        "suppressed_errors": getattr(hist, patches._EXTRA_PARAM_BASE_ERRORS_MC_SUPPRESSED).tolist(),
+    }
+
+
+def subtracted(hists: dict, shift: float | None):
+    result = hists["data"].Clone()
+    for name in SAMPLES[1:]:
+        result.Add(hists[name].Clone() if shift is None else hists[name].Clone().AddError(shift), -1)
+    return result
+
+
+def hist_cases(ff_functions, patches) -> list[dict]:
+    rng = np.random.default_rng(11)
+    cases = []
+    for n_data in (400, 60):  # the small one has bins <= 0 after the subtraction
+        inputs = {region: {s: events(rng, n_data if s == "data" else 150, s == "data") for s in SAMPLES} for region in ("sr", "ar")}
+        hists = {region: {s: fill(patches, sample, f"{region}_{s}_{n_data}") for s, sample in samples.items()} for region, samples in inputs.items()}
+        yields = {region: {label: subtracted(h, shift) for label, shift in (("nominal", None), ("up", 1.0), ("down", -1.0))} for region, h in hists.items()}
+        ratio = yields["sr"]["nominal"].Clone()
+        ratio.Divide(yields["ar"]["nominal"])
+        scaled = ratio.Clone()
+        scaled.Scale(1.7)
+        closure, _ = ff_functions.calculate_non_closure_correction({"data_subtracted": yields["sr"]["nominal"]}, {"data_ff": yields["ar"]["nominal"]}, skip_frac=True)
+        cases.append({
+            "edges": EDGES,
+            "events": {region: {s: {"x": v["x"].tolist(), "w": v["w"].tolist()} for s, v in samples.items()} for region, samples in inputs.items()},
+            "filled": {region: {s: dump(patches, h) for s, h in samples.items()} for region, samples in hists.items()},
+            "yields": {region: {label: dump(patches, h) for label, h in values.items()} for region, values in yields.items()},
+            "ratio": dump(patches, ratio),
+            "scaled": dump(patches, scaled),
+            "qcd": dump(patches, ff_functions.QCD_SS_estimate(dict(hists["ar"]))),
+            "closure": dump(patches, closure),
+        })
+    return cases
+
+
+def ratio_hist(patches, values, errors, means, suppressed, name: str):
+    hist = ROOT.TH1D(name, "", len(values), np.array(EDGES[: len(values) + 1]))
+    for i, (value, error) in enumerate(zip(values, errors)):
+        hist.SetBinContent(i + 1, value)
+        hist.SetBinError(i + 1, error)
+    setattr(hist, patches._EXTRA_PARAM_FLAG, True)
+    setattr(hist, patches._EXTRA_PARAM_MEANS, list(means))
+    setattr(hist, patches._EXTRA_PARAM_COUNTS, [1.0] * len(values))
+    setattr(hist, patches._EXTRA_PARAM_BASE_VALUES, np.array(values, dtype=float))
+    setattr(hist, patches._EXTRA_PARAM_BASE_ERRORS_STD, np.array(errors, dtype=float))
+    setattr(hist, patches._EXTRA_PARAM_BASE_ERRORS_MC_SUPPRESSED, np.array(suppressed, dtype=float))
+    return hist
+
+
+def dump_result(result: dict) -> dict:
+    """The stored (sparsified) steps; they sample the full-resolution curves."""
+    default, stored = result["default"], result["downsampled"]
+    return {
+        "p_value": default.get("p_value"), "reset": default.get("_auto_skipped", False),
+        "stored": {"edges": list(map(float, stored["nominal"]["edges"])), "values": list(map(float, stored["nominal"]["content"]))},
+        "stored_variations": {k: {"edges": list(map(float, v["edges"])), "values": list(map(float, v["content"]))} for k, v in stored["variations"].items()},
+    }
+
+
+def fit_cases(ff_functions, patches) -> list[dict]:
+    rng = np.random.default_rng(5)
+    means = [0.4, 1.6, 2.9, 4.1, 6.2]
+    shapes = {
+        "falling": ([0.30, 0.26, 0.21, 0.18, 0.12], [0.01, 0.012, 0.015, 0.02, 0.03]),
+        "compatible_with_one": ([1.03, 0.97, 1.05, 0.99, 1.01], [0.08, 0.09, 0.1, 0.12, 0.2]),
+        "non_closure": ([1.25, 1.1, 0.95, 0.9, 0.7], [0.03, 0.04, 0.05, 0.06, 0.1]),
+    }
+    options = ["binwise", "smoothed", "binwise#[0]+smoothed", "binwise#[-1]+smoothed", "binwise#[2]+smoothed"]
+    cases = []
+    for shape, (values, errors) in shapes.items():
+        suppressed = [e * 0.8 for e in errors]
+        for option in options:
+            for with_mc in (False, True):
+                name = f"{shape}_{option}_{with_mc}"
+                hist = ratio_hist(patches, values, errors, means, suppressed, name)
+                shifted = [[v * (1 + s * 0.05) + rng.normal(0, 0.002) for v in values] for s in (1, -1)]
+                mc = {label: ratio_hist(patches, v, errors, means, suppressed, f"{name}_{label}") for label, v in zip(("MCShiftUp", "MCShiftDown"), shifted)} if with_mc else None
+                _, result = ff_functions.smooth_function(hist.Clone(), EDGES, option, 2.0, mc_shifted_hist=mc)
+                cases.append({
+                    "option": option, "bandwidth": 2.0, "values": values, "errors": errors, "means": means, "suppressed": suppressed,
+                    "mc_shifted": shifted if with_mc else None, "result": dump_result(result),
+                    "constants": [ff_functions.fit_to_constant(mc[k])[0] for k in ("MCShiftUp", "MCShiftDown")] if with_mc else None,
+                })
+    return cases
+
+
+def main() -> None:
+    ff_functions, patches = import_tau_fake_factors()
+    reference = {
+        "root_version": ROOT.gROOT.GetVersion(),
+        "ff_functions_sha256": hashlib.sha256((FF_UPDATED / "helper" / "ff_functions.py").read_bytes()).hexdigest(),
+        "hooks_and_patches_sha256": hashlib.sha256((FF_UPDATED / "helper" / "hooks_and_patches.py").read_bytes()).hexdigest(),
+        "hists": hist_cases(ff_functions, patches),
+        "fits": fit_cases(ff_functions, patches),
+    }
+    path = Path(__file__).with_name("fake_factor_root.json")
+    path.write_text(json.dumps(reference))
+    print(f"wrote {path}: {len(reference['hists'])} histogram and {len(reference['fits'])} fit cases")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/test_fake_factor_fit.py b/tests/test_fake_factor_fit.py
new file mode 100644
index 0000000..eb8f8ba
--- /dev/null
+++ b/tests/test_fake_factor_fit.py
@@ -0,0 +1,68 @@
+"""Fits of measured ratios against TauFakeFactors' smooth_function (tests/reference/make_fake_factor_reference.py)."""
+import json
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from shapesmith.measurements.fake_factors import Fit
+from shapesmith.measurements.fake_factors.fit import constant_fit, fit, sparsify
+from shapesmith.measurements.fake_factors.hist import Hist
+
+REFERENCE = json.loads((Path(__file__).parent / "reference" / "fake_factor_root.json").read_text())
+EDGES = np.array([0.0, 1.0, 2.0, 3.5, 5.0, 8.0])
+FITS = {  # TauFakeFactors option -> Fit
+    "binwise": Fit("binwise"), "smoothed": Fit("smoothed", 2.0), "binwise#[0]+smoothed": Fit("smoothed", 2.0, binwise_left=1),
+    "binwise#[-1]+smoothed": Fit("smoothed", 2.0, binwise_right=1), "binwise#[2]+smoothed": Fit("smoothed", 2.0, binwise_left=3),
+}
+
+
+def ratio(values, errors, means, suppressed) -> Hist:
+    values, errors = np.array(values), np.array(errors)
+    return Hist(EDGES, values, errors**2, np.ones_like(values), np.array(means), values, errors, np.array(suppressed))
+
+
+def _close(a, b) -> bool:
+    return np.allclose(np.asarray(a), np.asarray(b), rtol=1e-12, atol=1e-15) and len(a) == len(b)
+
+
+@pytest.mark.parametrize("case", REFERENCE["fits"], ids=lambda c: f"{c['option']}_{c['values'][0]}_{c['mc_shifted'] is not None}")
+def test_fit_matches_tau_fake_factors(case):
+    mc = [ratio(v, case["errors"], case["means"], case["suppressed"]) for v in case["mc_shifted"]] if case["mc_shifted"] else None
+    fitted = fit(ratio(case["values"], case["errors"], case["means"], case["suppressed"]), FITS[case["option"]], mc)
+    expected = case["result"]
+    assert fitted.reset == expected["reset"]
+    assert (fitted.p_value is None) == (expected["p_value"] is None)
+    if expected["p_value"] is not None:
+        assert fitted.p_value == pytest.approx(expected["p_value"], rel=1e-9)
+    assert _close(fitted.stored.edges, expected["stored"]["edges"]) and _close(fitted.stored.values, expected["stored"]["values"])
+    assert list(fitted.stored_variations) == list(expected["stored_variations"])
+    for name, step in expected["stored_variations"].items():
+        assert _close(fitted.stored_variations[name].edges, step["edges"]), name
+        assert _close(fitted.stored_variations[name].values, step["values"]), name
+
+
+def test_the_reference_covers_resets_with_and_without_mc_shifts():
+    resets = [(c["mc_shifted"] is not None) for c in REFERENCE["fits"] if c["result"]["reset"]]
+    assert True in resets and False in resets
+
+
+@pytest.mark.parametrize("case", [c for c in REFERENCE["fits"] if c["constants"]][:3])
+def test_constant_fit_is_roots_pol0(case):
+    for values, constant in zip(case["mc_shifted"], case["constants"]):
+        assert constant_fit(ratio(values, case["errors"], case["means"], case["suppressed"])) == pytest.approx(constant, rel=1e-12)
+
+
+def test_a_constant_fit_without_errors_fails_to_one():
+    assert constant_fit(ratio([2.0, 3.0, 1.0, 1.0, 1.0], [0.0] * 5, [0.5, 1.5, 2.5, 4.0, 6.0], [0.0] * 5)) == 1.0
+
+
+def test_a_reset_ratio_is_stored_as_one_bin_of_one():
+    fitted = fit(ratio([1.01, 0.99, 1.0, 1.02, 0.98], [0.2] * 5, [0.5, 1.5, 2.7, 4.2, 6.1], [0.2] * 5), Fit("smoothed", 2.0))
+    assert fitted.reset and fitted.stored.edges.tolist() == [0.0, 8.0] and fitted.stored.values.tolist() == [1.0]
+    assert fitted.stored_variations["SystMCShiftUp"].values.tolist() == [1.0]  # nothing subtracted: 1
+
+
+def test_sparsify_keeps_an_edge_per_step_of_the_range_and_the_last_bin():
+    edges, values = sparsify(np.arange(6.0), np.array([0.0, 0.0, 0.5, 1.0, 1.0]))
+    assert edges.tolist() == [0.0, 2.0, 3.0, 4.0, 5.0] and values.tolist() == [0.0, 0.5, 1.0, 1.0]
diff --git a/tests/test_fake_factor_hist.py b/tests/test_fake_factor_hist.py
new file mode 100644
index 0000000..461aef6
--- /dev/null
+++ b/tests/test_fake_factor_hist.py
@@ -0,0 +1,60 @@
+"""The fake-factor histogram against ROOT with TauFakeFactors' patches (tests/reference/make_fake_factor_reference.py)."""
+import json
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from shapesmith.measurements.fake_factors.hist import Hist
+
+REFERENCE = json.loads((Path(__file__).parent / "reference" / "fake_factor_root.json").read_text())
+FIELDS = ("values", "errors", "counts", "means", "base_values", "base_errors", "suppressed_errors")
+
+
+def assert_matches(hist: Hist, reference: dict) -> None:
+    for name in FIELDS:
+        assert np.allclose(getattr(hist, name), reference[name], rtol=1e-12, atol=1e-15), name
+
+
+def filled(case: dict, region: str) -> dict[str, Hist]:
+    return {s: Hist.fill(case["edges"], np.array(e["x"], dtype=np.float32), np.array(e["w"])) for s, e in case["events"][region].items()}
+
+
+def subtracted(hists: dict[str, Hist], shift: float | None) -> Hist:
+    result = hists["data"]
+    for name in ("mc_a", "mc_b", "mc_c"):
+        result = result.add(hists[name] if shift is None else hists[name].shifted(shift), -1.0)
+    return result
+
+
+@pytest.fixture(params=range(len(REFERENCE["hists"])), ids=lambda i: f"case{i}")
+def case(request):
+    return REFERENCE["hists"][request.param]
+
+
+def test_fill_matches_root_and_the_centre_of_mass(case):
+    for region in ("sr", "ar"):
+        for sample, hist in filled(case, region).items():
+            assert_matches(hist, case["filled"][region][sample])
+
+
+def test_subtraction_and_shifts(case):
+    for region in ("sr", "ar"):
+        hists = filled(case, region)
+        for label, shift in (("nominal", None), ("up", 1.0), ("down", -1.0)):
+            assert_matches(subtracted(hists, shift), case["yields"][region][label])
+
+
+def test_ratio_scale_and_the_bin_rules(case):
+    sr, ar = subtracted(filled(case, "sr"), None), subtracted(filled(case, "ar"), None)
+    ratio = sr.divide(ar)
+    assert_matches(ratio, case["ratio"])
+    assert_matches(ratio.scaled(1.7), case["scaled"])
+    assert_matches(ratio.with_empty_bins_set_to_one(), case["closure"])
+    hists = filled(case, "ar")
+    assert_matches(subtracted(hists, None).with_negative_bins_zeroed(), case["qcd"])
+
+
+def test_the_small_case_has_bins_set_to_one():
+    closure = REFERENCE["hists"][1]["closure"]
+    assert 1.0 in closure["values"] and 1.0 in closure["errors"]

From 5c19b6dfd4c835c753992b42f1f68e7ca1fcdd5e Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 07:44:29 +0200
Subject: [PATCH 21/33] feat(fake_factors): the measurement chain, payloads and
 --suggest-binning

shapesmith measure runs the QCD and ttbar fake factors, the fractions, the
orthogonal fake factors and the DR->SR correction and the non-closures in
TauFakeFactors' order. Every data-driven step subtracts its own process list
(FM-1); applied fake factors and corrections are evaluated per event with
correctionlib from the payload models of the run (FM-5). The payloads follow
the conventions of the CROWN fake-factor friend (FM-15); measurement_<ch>.json
records every ratio, fit input and result, and the plots show the fits and the
closures. --suggest-binning is adjust_binning's equipopulated binning (FM-13).
---
 README.md                                     |  12 +
 .../measurements/fake_factors/binning.py      |  60 ++++
 .../measurements/fake_factors/measure.py      | 277 ++++++++++++++++++
 .../measurements/fake_factors/payload.py      |  77 +++++
 .../measurements/fake_factors/plots.py        |  64 ++++
 tests/ff_mini.py                              |  86 ++++++
 tests/test_fake_factors.py                    | 163 +++++++++++
 7 files changed, 739 insertions(+)
 create mode 100644 src/shapesmith/measurements/fake_factors/binning.py
 create mode 100644 src/shapesmith/measurements/fake_factors/measure.py
 create mode 100644 src/shapesmith/measurements/fake_factors/payload.py
 create mode 100644 src/shapesmith/measurements/fake_factors/plots.py
 create mode 100644 tests/ff_mini.py
 create mode 100644 tests/test_fake_factors.py

diff --git a/README.md b/README.md
index bdb03ec..5c2b8cb 100644
--- a/README.md
+++ b/README.md
@@ -114,6 +114,18 @@ it with a `MeasureContext` (configuration, analysis, channels, output directory,
   (`tests/reference/make_smoothing_reference.py`).
 - `shapesmith.cmssw`: run commands in a CMSSW environment (`combine.cmssw_dir`).
 
+`shapesmith.measurements.fake_factors` is the fake-factor measurement (the SM method of
+TauFakeFactors): an analysis sets `Analysis.measurement = FakeFactorMeasurement(legs)`, built from
+its region and process names and per-channel tables (`Binned`, `Fit`, `Split`, `ProcessFF`,
+`DrSr`, `DataScale`, `Fractions`, `Leg`). `shapesmith measure` writes `fake_factors_<ch>.json.gz`
+and `FF_corrections_<ch>.json.gz` (the conventions of the CROWN fake-factor friend), the
+intermediate DR->SR payload, `measurement_<ch>.json` (every ratio, fit input and result) and plots;
+`--suggest-binning` prints equipopulated edges instead. The histograms (`hist.py`: centre of mass,
+MC-suppressed errors), fits (`fit.py`: bands, SystMCShift, the compatibility-with-1 reset,
+sparsify) and the kernel are checked against ROOT and TauFakeFactors
+(`tests/reference/make_fake_factor_reference.py`); the normative algorithm is the "Algorithm
+reference" of the design spec.
+
 ## Run configuration
 
 ```yaml
diff --git a/src/shapesmith/measurements/fake_factors/binning.py b/src/shapesmith/measurements/fake_factors/binning.py
new file mode 100644
index 0000000..e3be7c2
--- /dev/null
+++ b/src/shapesmith/measurements/fake_factors/binning.py
@@ -0,0 +1,60 @@
+"""`measure --suggest-binning`: equipopulated bin edges from data, as TauFakeFactors' adjust_binning.py
+(FF_Updated _equipopulated_binned_variable and get_binning).
+
+Per category: the |weight|-weighted quantiles (inverted CDF) of the variable in [low, high], the outer edges set to
+low and high, rounded, the `add_left` edges put in front. The data are those of the quantity's region: the SR-like
+region of the process for its fake factors and non-closures (the data/MC scale region for an MC fake factor), the
+orthogonal SR-like region for the DR->SR correction and its non-closures, the fraction region for the fractions.
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Iterator
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from shapesmith.measurements.fake_factors.measure import EventSource
+    from shapesmith.measurements.fake_factors.model import Binned, Equipopulated, FakeFactorMeasurement, Split
+
+
+def equipopulated(x: np.ndarray, weights: np.ndarray, options: Equipopulated, n_bins: int) -> list[float]:
+    inside = (x >= options.low) & (x <= options.high)
+    edges = np.quantile(x[inside], np.linspace(0, 1, n_bins + 1), weights=np.abs(weights[inside]), method="inverted_cdf") if inside.any() else np.array([options.low, options.high])
+    edges[0], edges[-1] = options.low, options.high
+    return [*options.add_left, *(round(edge, options.rounding) for edge in np.round(edges, options.rounding).tolist())]
+
+
+def quantities(measurement: FakeFactorMeasurement, channel: str) -> Iterator[tuple[str, Binned, Split, str]]:
+    """(name, quantity, split, region) of every quantity with equipopulated options."""
+    for leg in measurement.legs[channel]:
+        for process in leg.processes:
+            name = process.name + leg.suffix
+            region = process.scale.sr_like if process.scale is not None else process.sr_like
+            yield f"{name}_fake_factors", process.fake_factors, process.split, region
+            yield from ((f"{name}_non_closure_{b.variable}", b, process.split, region) for b in process.non_closures)
+            if process.dr_sr is not None:
+                dr_sr = process.dr_sr
+                yield f"{name}_DR_SR", dr_sr.correction, process.split, dr_sr.sr_like
+                yield from ((f"{name}_for_DRtoSR_non_closure_{b.variable}", b, process.split, dr_sr.sr_like) for b in dr_sr.non_closures)
+        yield f"process_fractions{leg.suffix}", leg.fractions.binned, leg.fractions.split, leg.fractions.region
+
+
+def suggestions(measurement: FakeFactorMeasurement, source: EventSource) -> list[tuple[str, int, list[float], list[float]]]:
+    """(quantity, category, suggested edges, current edges)."""
+    result = []
+    for name, binned, split, region in quantities(measurement, source.channel):
+        if binned.equipopulated is None:
+            continue
+        events = source.events(source.data, region)
+        split_values = events.frame[split.variable].to_numpy()
+        for category in split.categories:
+            selected = (split_values >= split.edges[category]) & (split_values < split.edges[category + 1])
+            x = events.frame[binned.variable].to_numpy()[selected]
+            suggested = equipopulated(x, events.weights[selected], binned.equipopulated, binned.equipopulated.n_bins[category])
+            result.append((name, category, suggested, list(binned.edges[category])))
+    return result
+
+
+def print_suggestions(measurement: FakeFactorMeasurement, source: EventSource) -> None:
+    for name, category, suggested, current in suggestions(measurement, source):
+        print(f"{source.channel} {name} category {category}: suggested {suggested}, current {current}")
diff --git a/src/shapesmith/measurements/fake_factors/measure.py b/src/shapesmith/measurements/fake_factors/measure.py
new file mode 100644
index 0000000..9cea16e
--- /dev/null
+++ b/src/shapesmith/measurements/fake_factors/measure.py
@@ -0,0 +1,277 @@
+"""The fake-factor measurement of one channel, in TauFakeFactors' order (FF_Updated ff_calculation.py and
+ff_corrections.py):
+
+1. per leg the QCD and ttbar fake factors and the process fractions -> fake_factors_<ch>.json.gz;
+2. per QCD leg the orthogonal fake factors and their non-closures in the DR->SR regions, then the DR->SR correction
+   (the intermediate payloads -> FF_for_DRtoSR_<ch>.json.gz);
+3. per process the non-closures in table order, each measured with the fake factors, the DR->SR correction (QCD) and
+   the earlier non-closures applied per event -> FF_corrections_<ch>.json.gz.
+
+Fake factors and corrections are applied per event with correctionlib, from the payload models of this run, as the
+CROWN friend applies them. A "yield" is the histogram of the target process minus the subtracted processes, nominal
+and with every subtracted histogram shifted by +-1 sigma (SystMCShift).
+"""
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass, replace
+from typing import TYPE_CHECKING, Callable
+
+import correctionlib.schemav2 as cs
+import numpy as np
+
+from shapesmith import payloads
+from shapesmith.events import Events, Query
+from shapesmith.measurements.fake_factors import payload, plots
+from shapesmith.measurements.fake_factors.binning import print_suggestions
+from shapesmith.measurements.fake_factors.fit import Fitted, fit
+from shapesmith.measurements.fake_factors.hist import Hist
+
+if TYPE_CHECKING:
+    from shapesmith.measurements import MeasureContext
+    from shapesmith.measurements.fake_factors.model import Binned, FakeFactorMeasurement, Fractions, Leg, ProcessFF, Split
+
+logger = logging.getLogger(__name__)
+
+Yields = tuple[Hist, Hist, Hist]  # nominal, subtracted shifted up, subtracted shifted down
+
+
+@dataclass(frozen=True)
+class Ratio:
+    """A measured ratio of the nominal yields, the ratios with shifted subtraction (None without subtraction) and the
+    nominal yields themselves (the closure plots)."""
+
+    nominal: Hist
+    mc_shifted: tuple[Hist, Hist] | None
+    numerator: Hist
+    denominator: Hist
+
+
+class EventSource:
+    """The selected events of the processes of one channel per region, each read once."""
+
+    def __init__(self, context: MeasureContext, channel: str, columns: set[str]):
+        self.channel = channel
+        self.data = context.analysis.channel(channel).data()
+        self._context, self._columns, self._events = context, columns, {}
+
+    def events(self, process: str, region: str) -> Events:
+        if (process, region) not in self._events:
+            self._events[process, region] = self._context.events(Query(self.channel, process, region), self._columns)
+        return self._events[process, region]
+
+    def hist(self, process: str, region: str, variable: str, edges, split: Split, category: int, weights: np.ndarray | None = None) -> Hist:
+        """`variable` in one category of the split, with the event weights or the given per-event weights."""
+        events = self.events(process, region)
+        values = events.frame[split.variable].to_numpy()
+        selected = (values >= split.edges[category]) & (values < split.edges[category + 1])
+        weights = events.weights if weights is None else weights
+        return Hist.fill(edges, events.frame[variable].to_numpy()[selected], weights[selected])
+
+    def weighted(self, process: str, region: str, corrections: list[Callable]) -> np.ndarray:
+        """The event weights times the corrections evaluated per event, multiplied in order."""
+        events = self.events(process, region)
+        weights = events.weights
+        for correction in corrections:
+            weights = weights * correction(events.frame)
+        return weights
+
+
+def evaluator(correction: cs.Correction) -> Callable:
+    """The nominal of a payload correction per event, with the inputs as float (as TauFakeFactors and the friend)."""
+    evaluate = payloads.correction_set([correction], {}).to_evaluator()[correction.name].evaluate
+    columns = [variable.name for variable in correction.inputs[:-1]]  # the last input is syst
+    return lambda frame: evaluate(*(np.asarray(frame[c], dtype=np.float32).astype(np.float64) for c in columns), "nominal")
+
+
+class ChannelMeasurement:
+    """The measurement of one channel: the payload corrections and a record of every measured ratio and fit."""
+
+    def __init__(self, measurement: FakeFactorMeasurement, source: EventSource):
+        self.measurement, self.source = measurement, source
+        self.fake_factors: list[cs.Correction] = []
+        self.intermediate: list[cs.Correction] = []  # the orthogonal fake factors and their non-closures
+        self.corrections: list[cs.Correction] = []
+        self.compounds: list[cs.CompoundCorrection] = []
+        self.record: dict[str, list[dict]] = {}
+
+    def measure(self, legs: tuple[Leg, ...]) -> None:
+        for leg in legs:
+            self.fake_factors += [self.process_fake_factors(p.name + leg.suffix, p, p.sr_like, p.ar_like, p.subtract) for p in leg.processes]
+            self.fake_factors.append(self.fractions(f"process_fractions{leg.suffix}", leg.fractions))
+        by_name = {c.name: c for c in self.fake_factors}
+        for leg in legs:
+            for process in leg.processes:
+                name = process.name + leg.suffix
+                applied = [by_name[f"{name}_fake_factors"]]
+                if process.dr_sr is not None:
+                    applied.append(self.dr_sr(name, process))
+                closures = self.non_closures(name, process, process.non_closures, process.sr_like, process.ar_like, process.subtract, applied)
+                self.corrections += closures
+                if closures:
+                    self.compounds.append(payload.compound(name, process.non_closures, process.split))
+
+    def dr_sr(self, name: str, process: ProcessFF) -> cs.Correction:
+        """The DR->SR correction, measured with the orthogonal fake factors and their non-closures."""
+        dr_sr = process.dr_sr
+        orthogonal = self.process_fake_factors(name, process, dr_sr.sr_like, dr_sr.ar_like, dr_sr.subtract, stage="for_DRtoSR/")
+        closures = self.non_closures(name, process, dr_sr.non_closures, dr_sr.sr_like, dr_sr.ar_like, dr_sr.subtract, [orthogonal], stage="for_DRtoSR/")
+        self.intermediate += [orthogonal, *closures]
+        correction = self.closure(name, "DR_SR", process, dr_sr.correction, dr_sr.sr, dr_sr.ar, dr_sr.subtract, [orthogonal, *closures])
+        self.corrections.append(correction)
+        return correction
+
+    def process_fake_factors(self, name: str, process: ProcessFF, sr_like: str, ar_like: str, subtract: tuple[str, ...], stage: str = "") -> cs.Correction:
+        """{name}_fake_factors, the target yield in `sr_like` over that in `ar_like`, times the data/MC factor of an
+        MC fake factor."""
+        scale = None if process.scale is None else self.data_scale(process)
+        if scale is not None:
+            self.record[f"{stage}{name}_data_scale"] = [{"factor": scale}]
+
+        def measure(category: int) -> Ratio:
+            ratio = self.ratio(process, process.fake_factors, category, (sr_like, ar_like), subtract, from_data=scale is None)
+            return ratio if scale is None else replace(ratio, nominal=ratio.nominal.scaled(scale))
+
+        fitted = self.fitted(f"{stage}{name}_fake_factors", process.fake_factors, process.split, measure)
+        return payload.fake_factors(name, process.fake_factors, process.split, fitted, self.measurement.version)
+
+    def closure(self, name: str, correction_name: str, process: ProcessFF, binned: Binned, sr_like: str, ar_like: str, subtract: tuple[str, ...], applied: list[cs.Correction], stage: str = "") -> cs.Correction:
+        """{name}_{correction_name}_correction: the target yield in `sr_like` over that in `ar_like` weighted per event
+        with the `applied` fake factors and corrections. From data, bins <= 0 are set to 1 +- 1."""
+        from_data = process.scale is None
+        evaluators = [evaluator(correction) for correction in applied]
+        weights = {p: self.source.weighted(p, ar_like, evaluators) for p in (process.target, *subtract)}
+
+        def measure(category: int) -> Ratio:
+            ratio = self.ratio(process, binned, category, (sr_like, ar_like), subtract, from_data, weights)
+            if not from_data:
+                return ratio
+            return replace(ratio, nominal=ratio.nominal.with_empty_bins_set_to_one(), mc_shifted=tuple(h.with_empty_bins_set_to_one() for h in ratio.mc_shifted))
+
+        fitted = self.fitted(f"{stage}{name}_{correction_name}", binned, process.split, measure)
+        return payload.correction(name, correction_name, binned, process.split, fitted, self.measurement.version)
+
+    def non_closures(self, name: str, process: ProcessFF, non_closures: tuple[Binned, ...], sr_like: str, ar_like: str, subtract: tuple[str, ...], applied: list[cs.Correction], stage: str = "") -> list[cs.Correction]:
+        """The non-closures in table order, each on top of `applied` and the earlier ones."""
+        result = []
+        for binned in non_closures:
+            result.append(self.closure(name, f"non_closure_{binned.variable}", process, binned, sr_like, ar_like, subtract, applied + result, stage))
+        return result
+
+    def ratio(self, process: ProcessFF, binned: Binned, category: int, regions: tuple[str, str], subtract: tuple[str, ...], from_data: bool, ar_weights: dict[str, np.ndarray] | None = None) -> Ratio:
+        """The yield in the SR-like over the (weighted) yield in the AR-like region; from data also the ratios of the
+        shifted yields."""
+        sr_like, ar_like = regions
+        numerator = self.yields(process.target, subtract, sr_like, binned, process.split, category)
+        denominator = self.yields(process.target, subtract, ar_like, binned, process.split, category, ar_weights)
+        nominal, up, down = (n.divide(d) for n, d in zip(numerator, denominator))
+        return Ratio(nominal, (up, down) if from_data else None, numerator[0], denominator[0])
+
+    def yields(self, target: str, subtract: tuple[str, ...], region: str, binned: Binned, split: Split, category: int, weights: dict[str, np.ndarray] | None = None) -> Yields:
+        edges = binned.edges[category]
+
+        def hist(process: str) -> Hist:
+            return self.source.hist(process, region, binned.variable, edges, split, category, weights[process] if weights else None)
+
+        nominal = up = down = hist(target)
+        for process in subtract:
+            h = hist(process)
+            nominal, up, down = nominal.add(h, -1.0), up.add(h.shifted(1.0), -1.0), down.add(h.shifted(-1.0), -1.0)
+        return nominal, up, down
+
+    def data_scale(self, process: ProcessFF) -> float:
+        """The global data/MC factor of an MC fake factor, from the event totals of its four scale regions (summed and
+        subtracted in order, as TauFakeFactors' one-bin histograms)."""
+        scale, data = process.scale, self.source.data
+
+        def total(name: str, region: str) -> float:
+            weights = self.source.events(name, region).weights
+            return float(np.cumsum(weights)[-1]) if len(weights) else 0.0
+
+        def minus(value: float, names, region: str) -> float:
+            for name in names:
+                value -= total(name, region)
+            return value
+
+        def data_minus_mc(region: str, same_sign: str) -> float:
+            qcd = max(0.0, minus(total(data, same_sign), scale.subtract, same_sign))
+            return minus(total(data, region), (p for p in scale.subtract if p != process.target), region) - qcd
+
+        ratio = data_minus_mc(scale.sr_like, scale.sr_like_same_sign) / data_minus_mc(scale.ar_like, scale.ar_like_same_sign)
+        return ratio / (total(process.target, scale.sr_like) / total(process.target, scale.ar_like))
+
+    def fractions(self, name: str, fractions: Fractions) -> cs.Correction:
+        """process_fractions{s}: QCD and ttbar over their sum, nominal and with each of them shifted by +-1 sigma."""
+        values: dict[str, dict[str, list[np.ndarray]]] = {}
+        for category in fractions.split.categories:
+            hists = self.fraction_hists(fractions, category)
+            variations = {"nominal": hists}
+            for varied in hists:
+                variations[f"frac_{varied}_up"] = {**hists, varied: hists[varied].shifted(1.0)}
+                variations[f"frac_{varied}_down"] = {**hists, varied: hists[varied].shifted(-1.0)}
+            for variation, shifted in variations.items():
+                for process, h in shifted.items():
+                    total = h
+                    for other in (p for p in shifted if p != process):
+                        total = total.add(shifted[other])
+                    values.setdefault(variation, {}).setdefault(process, []).append(h.divide(total).values)
+            self.record.setdefault(name, []).append({
+                "category": category, "edges": list(fractions.binned.edges[category]),
+                "yields": {p: h.values.tolist() for p, h in hists.items()}, "fractions": {p: values["nominal"][p][-1].tolist() for p in hists},
+            })
+        return payload.fractions(name, fractions.binned, fractions.split, values, self.measurement.version)
+
+    def fraction_hists(self, fractions: Fractions, category: int) -> dict[str, Hist]:
+        """QCD = data - all MC with negative bins 0, and ttbar, in one category."""
+        def hist(process: str) -> Hist:
+            return self.source.hist(process, fractions.region, fractions.binned.variable, fractions.binned.edges[category], fractions.split, category)
+
+        qcd = hist(self.source.data)
+        for process in fractions.subtract:
+            qcd = qcd.add(hist(process), -1.0)
+        return {"QCD": qcd.with_negative_bins_zeroed(), "ttbar_J": hist(fractions.ttbar)}
+
+    def fitted(self, name: str, binned: Binned, split: Split, measure: Callable[[int], Ratio]) -> list[Fitted]:
+        """The fit per category of the ratio `measure(category)`, recorded under `name`."""
+        result = []
+        for category in split.categories:
+            ratio = measure(category)
+            fitted = fit(ratio.nominal, binned.fits[category], ratio.mc_shifted, self.measurement.stat_sigma)
+            self.record.setdefault(name, []).append(_record(binned.variable, split, category, ratio, fitted))
+            result.append(fitted)
+        return result
+
+
+def run(measurement: FakeFactorMeasurement, context: MeasureContext) -> None:
+    for channel in context.channels:
+        source = EventSource(context, channel, measurement.columns(channel))
+        if context.suggest_binning:
+            print_suggestions(measurement, source)
+            continue
+        logger.info(f"{channel}: measuring the fake factors")
+        result = ChannelMeasurement(measurement, source)
+        result.measure(measurement.legs[channel])
+        provenance, output = context.provenance(channel=channel), context.output
+        payloads.write(payloads.correction_set(result.fake_factors, provenance), output / f"fake_factors_{channel}.json.gz")
+        payloads.write(payloads.correction_set(result.corrections, provenance, result.compounds), output / f"FF_corrections_{channel}.json.gz")
+        payloads.write(payloads.correction_set(result.intermediate, provenance), output / f"FF_for_DRtoSR_{channel}.json.gz")
+        (output / f"measurement_{channel}.json").write_text(json.dumps(result.record, indent=1))
+        plots.plot_record(result.record, output / "plots" / channel)
+        logger.info(f"{channel}: payloads written to {output}")
+
+
+def _points(h: Hist) -> dict:
+    return {"x": h.means.tolist(), "y": h.values.tolist(), "errors": h.errors.tolist()}
+
+
+def _record(variable: str, split: Split, category: int, ratio: Ratio, fitted: Fitted) -> dict:
+    """The fit inputs (the ratio and the shifted ratios as points at their centres of mass, the yields) and results."""
+    return {
+        "variable": variable, "category": category, "split": [split.edges[category], split.edges[category + 1]],
+        "edges": ratio.nominal.edges.tolist(), **_points(ratio.nominal),
+        "mc_shifted": [_points(h) for h in ratio.mc_shifted] if ratio.mc_shifted else None,
+        "numerator": ratio.numerator.values.tolist(), "denominator": ratio.denominator.values.tolist(),
+        "p_value": fitted.p_value, "reset": fitted.reset,
+        "curve": {"edges": fitted.curve.edges.tolist(), "nominal": fitted.curve.values.tolist(), **{k: v.tolist() for k, v in fitted.variations.items()}},
+    }
diff --git a/src/shapesmith/measurements/fake_factors/payload.py b/src/shapesmith/measurements/fake_factors/payload.py
new file mode 100644
index 0000000..22c5f71
--- /dev/null
+++ b/src/shapesmith/measurements/fake_factors/payload.py
@@ -0,0 +1,77 @@
+"""The correctionlib models of the fake-factor payloads, in the conventions the CROWN fake-factor friend reads.
+
+| correction | inputs | syst keys (default: the nominal) |
+|---|---|---|
+| `{p}{s}_fake_factors` | variable, split, syst | `{p}{s}{StatShift,SystMCShift,SystBandHigh,SystBandLow,SystBandAsym}{Up,Down}` |
+| `process_fractions{s}` | process, variable, split, syst | `process_fractions{s}{nominal,frac_QCD_up/down,frac_ttbar_J_up/down}`; processes QCD, ttbar |
+| `QCD{s}_DR_SR_correction` | variable, split, syst | `QCD{s}_DR_SR_Corr{StatShift,SystMCShift,SystBandAsym}{Up,Down}`, `nominal` |
+| `{p}{s}_non_closure_<var>_correction` | variable, split, syst | `{p}{s}_non_closure_<var>_Corr...` and the global `{p}{s}_non_closure_Corr...`, `nominal` |
+| `{p}{s}_compound_correction` | the non-closure variables in order, split, syst | the product of the non-closures of {p}{s} |
+
+{p} is QCD or ttbar, {s} the leg suffix. Every correction is binned in the split categories first, then in its
+variable (flow clamp), below a `syst` category.
+"""
+from __future__ import annotations
+
+from typing import Mapping, Sequence
+
+import correctionlib.schemav2 as cs
+
+from shapesmith import payloads
+from shapesmith.measurements.fake_factors.fit import BAND_ASYM, MC, STAT, Fitted, Step
+from shapesmith.measurements.fake_factors.model import Binned, Split
+
+CORRECTION_VARIATIONS = tuple(f"{name}{direction}" for name in (STAT, MC, BAND_ASYM) for direction in ("Up", "Down"))
+FRACTION_PROCESSES = {"QCD": "QCD", "ttbar_J": "ttbar"}  # TauFakeFactors process -> payload category
+
+
+def _by_category(split: Split, variable: str, steps: Sequence[Step]) -> cs.Binning:
+    return payloads.binning(split.variable, split.edges, [payloads.binning(variable, step.edges, step.values) for step in steps])
+
+
+def _inputs(*names: str) -> list[cs.Variable]:
+    return [payloads.variable(name) for name in names] + [payloads.variable("syst", "string")]
+
+
+def fake_factors(name: str, binned: Binned, split: Split, fitted: Sequence[Fitted], version: int) -> cs.Correction:
+    """`name` = {p}{s}; the stored fit per category."""
+    keys = fitted[0].stored_variations
+    content = {name + key: _by_category(split, binned.variable, [f.stored_variations[key] for f in fitted]) for key in keys}
+    data = payloads.category("syst", content, default=_by_category(split, binned.variable, [f.stored for f in fitted]))
+    return payloads.correction(f"{name}_fake_factors", _inputs(binned.variable, split.variable), data, version, output=payloads.variable(f"{name}_ff"))
+
+
+def fractions(name: str, binned: Binned, split: Split, values: Mapping[str, Mapping[str, Sequence]], version: int) -> cs.Correction:
+    """`name` = process_fractions{s}; values[variation][process] holds the fraction values per category, variation
+    nominal or frac_<process>_up/down."""
+
+    def by_process(variation: str) -> cs.Category:
+        return payloads.category("process", {
+            label: _by_category(split, binned.variable, [Step(edges, category) for edges, category in zip(binned.edges, values[variation][process])])
+            for process, label in FRACTION_PROCESSES.items()
+        })
+
+    data = payloads.category("syst", {name + variation: by_process(variation) for variation in values}, default=by_process("nominal"))
+    return payloads.correction(name, [payloads.variable("process", "string"), *_inputs(binned.variable, split.variable)], data, version, output=payloads.variable("fraction"))
+
+
+def correction(process: str, correction_name: str, binned: Binned, split: Split, fitted: Sequence[Fitted], version: int) -> cs.Correction:
+    """`process` = {p}{s}, `correction_name` DR_SR or non_closure_<var>; a non-closure repeats its keys under the
+    global non_closure name, which the compound correction shifts as one."""
+    names = [f"_{correction_name}"] + (["_non_closure"] if correction_name.startswith("non_closure") else [])
+    nominal = _by_category(split, binned.variable, [f.stored for f in fitted])
+    content = {
+        f"{process}{name}_Corr{variation}": _by_category(split, binned.variable, [f.stored_variations[variation] for f in fitted])
+        for variation in CORRECTION_VARIATIONS for name in names
+    }
+    content["nominal"] = nominal
+    full_name = f"{process}_{correction_name}_correction"
+    data = payloads.category("syst", content, default=nominal)
+    return payloads.correction(full_name, _inputs(binned.variable, split.variable), data, version, output=payloads.variable(full_name))
+
+
+def compound(process: str, non_closures: Sequence[Binned], split: Split) -> cs.CompoundCorrection:
+    """The product of the non-closure corrections of {p}{s} = `process`, in table order."""
+    name = f"{process}_compound_correction"
+    stack = [f"{process}_non_closure_{binned.variable}_correction" for binned in non_closures]
+    return payloads.compound(name, _inputs(*(b.variable for b in non_closures), split.variable), stack, output=payloads.variable(name))
diff --git a/src/shapesmith/measurements/fake_factors/plots.py b/src/shapesmith/measurements/fake_factors/plots.py
new file mode 100644
index 0000000..654ea44
--- /dev/null
+++ b/src/shapesmith/measurements/fake_factors/plots.py
@@ -0,0 +1,64 @@
+"""Plots of the fake-factor measurement from its record: every fitted ratio with its curve and bands per category,
+the SR-like yield against the (weighted) AR-like yield below it (the closure), and the fractions."""
+from __future__ import annotations
+
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+import mplhep as hep  # noqa: E402
+import numpy as np  # noqa: E402
+
+hep.style.use("CMS")
+
+
+def plot_record(record: dict[str, list[dict]], directory: Path) -> list[Path]:
+    directory.mkdir(parents=True, exist_ok=True)
+    paths = []
+    for name, entries in record.items():
+        for entry in entries:
+            path = directory / f"{name.replace('/', '_')}_category{entry.get('category', 0)}.png"
+            if "curve" in entry:
+                paths.append(_plot_fit(name, entry, path))
+            elif "fractions" in entry:
+                paths.append(_plot_fractions(name, entry, path))
+    return paths
+
+
+def _plot_fit(name: str, entry: dict, path: Path) -> Path:
+    figure, (top, bottom) = plt.subplots(2, 1, figsize=(10, 10), sharex=True, gridspec_kw={"height_ratios": (2, 1)})
+    curve = entry["curve"]
+    edges = np.asarray(curve["edges"])
+    for band, colour in (("SystBandAsym", "#f89c20"), ("StatShift", "#5790fc")):
+        top.stairs(np.asarray(curve[f"{band}Up"]), edges, baseline=np.asarray(curve[f"{band}Down"]), fill=True, alpha=0.4, color=colour, label=band)
+    top.stairs(np.asarray(curve["nominal"]), edges, baseline=None, color="black", label="fit")
+    top.errorbar(entry["x"], entry["y"], yerr=entry["errors"], fmt="o", color="black", label="measured")
+    top.set_ylabel("ratio")
+    top.set_title(f"{name} {entry['split']}" + (" (reset to 1)" if entry["reset"] else ""), fontsize=14)
+    top.legend(fontsize=12)
+    bin_edges = entry["edges"]
+    bottom.stairs(entry["numerator"], bin_edges, color="black", label="SR-like")
+    bottom.stairs(entry["denominator"], bin_edges, color="#e42536", label="AR-like (weighted)")
+    bottom.set_ylabel("yield")
+    bottom.set_xlabel(entry["variable"])
+    bottom.legend(fontsize=12)
+    figure.savefig(path)
+    plt.close(figure)
+    return path
+
+
+def _plot_fractions(name: str, entry: dict, path: Path) -> Path:
+    figure, ax = plt.subplots(figsize=(10, 8))
+    bottom = np.zeros(len(entry["edges"]) - 1)
+    for (process, values), colour in zip(entry["fractions"].items(), ("#5790fc", "#e42536")):
+        ax.stairs(bottom + np.asarray(values), entry["edges"], baseline=bottom, fill=True, color=colour, label=process)
+        bottom = bottom + np.asarray(values)
+    ax.set_ylim(0, 1.3)
+    ax.set_ylabel("fraction")
+    ax.set_title(f"{name} category {entry['category']}", fontsize=14)
+    ax.legend(fontsize=12)
+    figure.savefig(path)
+    plt.close(figure)
+    return path
diff --git a/tests/ff_mini.py b/tests/ff_mini.py
new file mode 100644
index 0000000..9569505
--- /dev/null
+++ b/tests/ff_mini.py
@@ -0,0 +1,86 @@
+"""A synthetic fake-factor measurement: data, a genuine-tau MC process (ZT) and ttbar jet fakes (TTJ) in one
+lepton-tau channel, written straight into skims. The regions follow the SM measurement: same sign for the QCD fake
+factors, a lepton-isolation sideband for the DR->SR correction, OS with inverted lepton vetoes for the ttbar scale.
+"""
+import numpy as np
+import pandas as pd
+
+from shapesmith.measurements.fake_factors import Binned, DataScale, DrSr, Equipopulated, FakeFactorMeasurement, Fit, Fractions, Leg, ProcessFF, Split
+from shapesmith.model import Analysis, Channel, Process, Region, Sample, Selection
+from shapesmith.store import skim_path, write_skim
+
+PASS, FAIL = "(id_pass > 0.5)", "(id_pass < 0.5) & (id_loose > 0.5)"
+SS, OS = "((q_1 * q_2) > 0)", "((q_1 * q_2) < 0)"
+ISOLATED, SIDEBAND = "(iso_1 < 0.15)", "(iso_1 >= 0.15)"
+SUBTRACT = ("ZT", "TTJ")
+
+
+def _events(rng: np.random.Generator, n: int, kind: str) -> pd.DataFrame:
+    frame = pd.DataFrame({
+        "q_1": rng.choice([-1, 1], n).astype(np.int32), "q_2": rng.choice([-1, 1], n).astype(np.int32),
+        "id_pass": (rng.random(n) < (0.7 if kind == "mc" else 0.25)).astype(np.int32), "id_loose": (rng.random(n) < 0.97).astype(np.int32),
+        "iso_1": rng.uniform(0, 0.5, n).astype(np.float32), "veto": (rng.random(n) < 0.2).astype(np.int32),
+        "n_jets": rng.choice([2, 3, 4], n, p=[0.5, 0.3, 0.2]).astype(np.int32),
+        "pt_2": rng.uniform(30, 150, n).astype(np.float32), "pt_tautau": rng.uniform(0, 150, n).astype(np.float32),
+        "tau_decaymode_2": rng.choice([0, 1, 10], n).astype(np.int32), "met": rng.exponential(40, n).astype(np.float32),
+        "sf": rng.normal(1.0, 0.05, n).astype(np.float32),
+    })
+    frame["norm_weight"] = 1.0 if kind == "data" else 0.04
+    for column, sample_kind in (("is_data", "data"), ("is_mc", "mc"), ("is_embedding", "embedding")):
+        frame[column] = kind == sample_kind
+    return frame
+
+
+def write_skims(skim_dir, seed: int = 3) -> None:
+    rng = np.random.default_rng(seed)
+    for nick, kind, n in (("DATA", "data", 30000), ("DY", "mc", 30000), ("TT", "mc", 60000)):
+        frame = _events(rng, n, kind)
+        frame["sample_nick"] = pd.array([nick] * n, dtype="string")
+        write_skim(frame, skim_path(skim_dir, "mt", nick, "file_0.root"))
+
+
+def _regions() -> tuple[Region, ...]:
+    def region(name: str, **cuts: str) -> Region:
+        return Region(name, replace_cuts=cuts)
+
+    qcd = {"charge": SS, "jets": "(n_jets >= 2)"}
+    return (
+        region("QCD_sr_like", **qcd), region("QCD_ar_like", **qcd, tau_iso=FAIL),
+        region("QCD_orthogonal_sr_like", **qcd, lepton_iso=SIDEBAND), region("QCD_orthogonal_ar_like", **qcd, lepton_iso=SIDEBAND, tau_iso=FAIL),
+        region("QCD_dr_sr_sr_like", lepton_iso=SIDEBAND), region("QCD_dr_sr_ar_like", lepton_iso=SIDEBAND, tau_iso=FAIL),
+        region("ttbar_sr"), region("ttbar_ar", tau_iso=FAIL),
+        region("ttbar_scale_sr_like", vetoes="(veto > 0.5)"), region("ttbar_scale_ar_like", vetoes="(veto > 0.5)", tau_iso=FAIL),
+        region("ttbar_scale_sr_like_ss", vetoes="(veto > 0.5)", charge=SS), region("ttbar_scale_ar_like_ss", vetoes="(veto > 0.5)", charge=SS, tau_iso=FAIL),
+    )
+
+
+def binned(variable: str, edges: tuple[float, ...], fit: Fit, categories: int = 2, n_bins: int = 3) -> Binned:
+    return Binned(variable, (edges,) * categories, (fit,) * categories, Equipopulated((n_bins,) * categories, edges[0], edges[-1]))
+
+
+def measurement() -> FakeFactorMeasurement:
+    split = Split("n_jets", (1.5, 2.5, 5.5))
+    smoothed = Fit("smoothed", 30.0)
+    non_closures = (binned("tau_decaymode_2", (-0.5, 0.5, 9.5, 10.5), Fit("binwise")), binned("met", (0.0, 30.0, 60.0, 200.0), Fit("smoothed", 40.0, binwise_left=1)))
+    dr_sr = DrSr(
+        "QCD_orthogonal_sr_like", "QCD_orthogonal_ar_like", "QCD_dr_sr_sr_like", "QCD_dr_sr_ar_like", SUBTRACT,
+        binned("pt_tautau", (0.0, 50.0, 100.0, 150.0), Fit("smoothed", 25.0, binwise_right=1)), non_closures[:1],
+    )
+    qcd = ProcessFF("QCD", "data", SUBTRACT, "QCD_sr_like", "QCD_ar_like", split, binned("pt_2", (30.0, 50.0, 80.0, 150.0), smoothed), non_closures, dr_sr)
+    scale = DataScale("ttbar_scale_sr_like", "ttbar_scale_ar_like", "ttbar_scale_sr_like_ss", "ttbar_scale_ar_like_ss", SUBTRACT)
+    ttbar = ProcessFF("ttbar", "TTJ", (), "ttbar_sr", "ttbar_ar", split, binned("pt_2", (30.0, 60.0, 150.0), smoothed), non_closures[1:], scale=scale)
+    fractions = Fractions("ttbar_ar", SUBTRACT, "TTJ", Split("n_jets", (1.5, 2.5, 5.5)), Binned("pt_2", ((30.0, 60.0, 150.0),) * 2))
+    return FakeFactorMeasurement({"mt": (Leg("", qcd, ttbar, fractions),)})
+
+
+def build(config=None) -> Analysis:
+    weights = Selection(weights={"sf": "sf"})
+    channel = Channel(
+        name="mt",
+        samples=(Sample("DATA", "data", "data"), Sample("DY", "DY", "mc"), Sample("TT", "TT", "mc")),
+        skim={},
+        cuts={"charge": OS, "vetoes": "(veto < 0.5)", "lepton_iso": ISOLATED, "jets": "(n_jets >= 2)", "tau_iso": PASS},
+        processes=(Process("data", "data", "data", "data"), Process("ZT", "DY", "background", "Z", weights), Process("TTJ", "TT", "background", "TT", weights)),
+        regions=_regions(),
+    )
+    return Analysis("ff_mini", "2018", 1.0, None, {"mt": channel}, measurement=measurement())
diff --git a/tests/test_fake_factors.py b/tests/test_fake_factors.py
new file mode 100644
index 0000000..00d74ba
--- /dev/null
+++ b/tests/test_fake_factors.py
@@ -0,0 +1,163 @@
+"""The fake-factor measurement chain on the synthetic tests/ff_mini.py analysis: ratios, SystMCShift, fractions,
+the chaining of the corrections and the payload conventions of the CROWN fake-factor friend."""
+import gzip
+import json
+
+import correctionlib
+import numpy as np
+import pytest
+
+from shapesmith.config import NtupleConfig, RunConfig
+from shapesmith.events import Query, load
+from shapesmith.measurements import run_measure
+from shapesmith.measurements.fake_factors.hist import Hist
+from shapesmith.validate import validate
+from tests import ff_mini
+
+DIRECTIONS = ("Up", "Down", "_up", "_down")
+
+
+@pytest.fixture(scope="module")
+def measured(tmp_path_factory):
+    root = tmp_path_factory.mktemp("ff")
+    ff_mini.write_skims(root / "skims")
+    config = RunConfig(analysis="tests.ff_mini:build", era="2018", channels=["mt"], ntuples=NtupleConfig(base=str(root)), skim_dir=root / "skims", output_dir=root / "out", workers=1)
+    analysis = ff_mini.build(config)
+    validate(analysis)
+    output = run_measure(config, analysis, ["mt"])
+    payloads = {name: json.loads(gzip.decompress((output / f"{name}_mt.json.gz").read_bytes())) for name in ("fake_factors", "FF_corrections")}
+    record = json.loads((output / "measurement_mt.json").read_text())
+    return config, analysis, output, payloads, record
+
+
+def hist(config, analysis, process, region, variable, edges, category_edges, weights=None) -> Hist:
+    events = load(config, analysis, Query("mt", process, region), [variable, "n_jets"])
+    n_jets = events.frame["n_jets"].to_numpy()
+    selected = (n_jets >= category_edges[0]) & (n_jets < category_edges[1])
+    w = events.weights if weights is None else weights
+    return Hist.fill(edges, events.frame[variable].to_numpy()[selected], w[selected])
+
+
+def evaluator(payloads, name):
+    for payload in payloads.values():
+        cset = correctionlib.CorrectionSet.from_string(json.dumps(payload))
+        if name in {c["name"] for c in payload["corrections"]}:
+            return cset[name]
+        if name in {c["name"] for c in payload.get("compound_corrections") or []}:
+            return cset.compound[name]
+    raise KeyError(name)
+
+
+def test_the_qcd_fake_factors_are_data_minus_mc_over_data_minus_mc(measured):
+    config, analysis, _, _, record = measured
+    edges, category = (30.0, 50.0, 80.0, 150.0), (1.5, 2.5)
+    yields = {region: hist(config, analysis, "data", region, "pt_2", edges, category) for region in ("QCD_sr_like", "QCD_ar_like")}
+    for region in yields:
+        for process in ff_mini.SUBTRACT:
+            yields[region] = yields[region].add(hist(config, analysis, process, region, "pt_2", edges, category), -1.0)
+    expected = yields["QCD_sr_like"].values / yields["QCD_ar_like"].values
+    assert np.allclose(record["QCD_fake_factors"][0]["y"], expected, rtol=1e-12)
+
+
+def test_systmcshift_subtracts_the_mc_shifted_by_its_statistical_error_not_a_scaled_mc(measured):
+    config, analysis, _, payloads, _ = measured
+    edges, category = (30.0, 50.0, 80.0, 150.0), (1.5, 2.5)
+    shifted = {}
+    for region in ("QCD_sr_like", "QCD_ar_like"):
+        h = hist(config, analysis, "data", region, "pt_2", edges, category)
+        for process in ff_mini.SUBTRACT:
+            h = h.add(hist(config, analysis, process, region, "pt_2", edges, category).shifted(1.0), -1.0)
+        shifted[region] = h
+    ratio = shifted["QCD_sr_like"].values / shifted["QCD_ar_like"].values
+    ff = evaluator(payloads, "QCD_fake_factors")
+    nominal = ff.evaluate(40.0, 2.0, "nominal")
+    up = ff.evaluate(40.0, 2.0, "QCDSystMCShiftUp")
+    assert up != nominal and np.isclose(up, ratio[0], rtol=0.15)  # the smoothed curve follows the shifted ratio
+    assert (up - nominal) * (ratio[0] - nominal) > 0
+
+
+def test_the_ttbar_fake_factors_carry_the_data_scale_and_no_mc_shift(measured):
+    _, _, _, payloads, record = measured
+    ff = evaluator(payloads, "ttbar_fake_factors")
+    for pt in (35.0, 90.0):
+        assert ff.evaluate(pt, 3.0, "ttbarSystMCShiftUp") == ff.evaluate(pt, 3.0, "nominal") == ff.evaluate(pt, 3.0, "ttbarSystMCShiftDown")
+    scale = record["ttbar_data_scale"][0]["factor"]
+    raw = np.array(record["ttbar_fake_factors"][0]["numerator"]) / np.array(record["ttbar_fake_factors"][0]["denominator"])
+    assert np.allclose(record["ttbar_fake_factors"][0]["y"], raw * scale, rtol=1e-12) and scale != 1.0
+
+
+def test_the_fractions_sum_to_one_in_every_variation(measured):
+    _, _, _, payloads, _ = measured
+    fractions = evaluator(payloads, "process_fractions")
+    for key in ("nominal", "process_fractionsfrac_QCD_up", "process_fractionsfrac_ttbar_J_down"):
+        for x, n_jets in ((40.0, 2.0), (100.0, 4.0)):
+            assert fractions.evaluate("QCD", x, n_jets, key) + fractions.evaluate("ttbar", x, n_jets, key) == pytest.approx(1.0)
+
+
+def test_each_category_of_the_split_reads_its_own_payload_bin(measured):
+    _, _, _, payloads, record = measured
+    ff = evaluator(payloads, "QCD_fake_factors")
+    values = [ff.evaluate(35.0, n_jets, "nominal") for n_jets in (2.0, 3.0, 5.0)]
+    assert values[0] != values[1] and values[1] == values[2]
+
+
+def test_the_compound_correction_is_the_product_of_the_non_closures(measured):
+    _, _, _, payloads, _ = measured
+    compound = evaluator(payloads, "QCD_compound_correction")
+    parts = [evaluator(payloads, "QCD_non_closure_tau_decaymode_2_correction"), evaluator(payloads, "QCD_non_closure_met_correction")]
+    for dm, met, n_jets in ((0.0, 20.0, 2.0), (10.0, 90.0, 3.0)):
+        product = parts[0].evaluate(dm, n_jets, "nominal") * parts[1].evaluate(met, n_jets, "nominal")
+        assert compound.evaluate(dm, met, n_jets, "nominal") == pytest.approx(product)
+
+
+def test_the_non_closures_are_measured_on_top_of_the_dr_sr_correction(measured):
+    config, analysis, _, payloads, record = measured
+    edges, category = (-0.5, 0.5, 9.5, 10.5), (1.5, 2.5)
+    ff, dr_sr = evaluator(payloads, "QCD_fake_factors"), evaluator(payloads, "QCD_DR_SR_correction")
+    predicted = None
+    for process in ("data", *ff_mini.SUBTRACT):
+        events = load(config, analysis, Query("mt", process, "QCD_ar_like"), ["pt_2", "pt_tautau", "n_jets", "tau_decaymode_2"])
+        frame = events.frame
+        weights = events.weights * ff.evaluate(frame["pt_2"].to_numpy(np.float64), frame["n_jets"].to_numpy(np.float64), "nominal")
+        weights = weights * dr_sr.evaluate(frame["pt_tautau"].to_numpy(np.float64), frame["n_jets"].to_numpy(np.float64), "nominal")
+        h = hist(config, analysis, process, "QCD_ar_like", "tau_decaymode_2", edges, category, weights)
+        predicted = h if predicted is None else predicted.add(h, -1.0)
+    assert np.allclose(record["QCD_non_closure_tau_decaymode_2"][0]["denominator"], predicted.values, rtol=1e-9)
+
+
+def _keys(node) -> list[str]:
+    return [item["key"] for item in node["content"]]
+
+
+def test_the_payloads_follow_the_friend_conventions(measured):
+    _, _, output, payloads, _ = measured
+    names = {c["name"] for p in payloads.values() for c in p["corrections"]}
+    assert {"QCD_fake_factors", "ttbar_fake_factors", "process_fractions", "QCD_DR_SR_correction", "QCD_non_closure_met_correction", "ttbar_non_closure_met_correction"} <= names
+    for payload in payloads.values():
+        assert "$schema" not in payload and json.loads(payload["description"])["channel"] == "mt"
+        for correction in payload["corrections"]:
+            inputs = correction["inputs"]
+            assert inputs[-1] == {"name": "syst", "type": "string"} and correction["data"]["nodetype"] == "category" and correction["data"]["input"] == "syst"
+            assert all(v["type"] == ("string" if v["name"] == "process" else "real") for v in inputs[:-1])
+            assert correction["data"].get("default") is not None
+            keys = [k for k in _keys(correction["data"]) if not k.endswith("nominal")]
+            assert all(k.endswith(DIRECTIONS) for k in keys)
+            for key in keys:
+                partner = next(key[: -len(d)] + o for d, o in (("Up", "Down"), ("Down", "Up"), ("_up", "_down"), ("_down", "_up")) if key.endswith(d))
+                assert partner in keys, key
+            if correction["name"] == "process_fractions":
+                assert _keys(correction["data"]["default"]) == ["QCD", "ttbar"]
+    by_name = {c["name"]: c for c in payloads["FF_corrections"]["corrections"]}
+    for compound in payloads["FF_corrections"]["compound_corrections"]:
+        process = compound["name"].removesuffix("_compound_correction")
+        for member in compound["stack"]:
+            assert f"{process}_non_closure_CorrStatShiftUp" in _keys(by_name[member]["data"])
+    assert (output / "FF_for_DRtoSR_mt.json.gz").exists() and list((output / "plots" / "mt").glob("*.png"))
+
+
+def test_suggest_binning_writes_nothing(measured, capsys, tmp_path):
+    config, analysis, _, _, _ = measured
+    config = config.model_copy(update={"output_dir": tmp_path / "suggest"})
+    run_measure(config, analysis, ["mt"], suggest_binning=True)
+    printed = capsys.readouterr().out
+    assert "mt QCD_fake_factors category 0: suggested [30.0," in printed and not (tmp_path / "suggest").exists()

From ffc06d2f2820b59542181c7518b0d6f895fa299b Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 07:58:27 +0200
Subject: [PATCH 22/33] fix(fake_factors): label the fake-factor denominator as
 the plain AR-like yield

---
 src/shapesmith/measurements/fake_factors/plots.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/shapesmith/measurements/fake_factors/plots.py b/src/shapesmith/measurements/fake_factors/plots.py
index 654ea44..c9b4720 100644
--- a/src/shapesmith/measurements/fake_factors/plots.py
+++ b/src/shapesmith/measurements/fake_factors/plots.py
@@ -39,8 +39,9 @@ def _plot_fit(name: str, entry: dict, path: Path) -> Path:
     top.set_title(f"{name} {entry['split']}" + (" (reset to 1)" if entry["reset"] else ""), fontsize=14)
     top.legend(fontsize=12)
     bin_edges = entry["edges"]
+    closure = not name.endswith("_fake_factors")  # a correction's denominator is the prediction from the AR-like region
     bottom.stairs(entry["numerator"], bin_edges, color="black", label="SR-like")
-    bottom.stairs(entry["denominator"], bin_edges, color="#e42536", label="AR-like (weighted)")
+    bottom.stairs(entry["denominator"], bin_edges, color="#e42536", label="AR-like x fake factors" if closure else "AR-like")
     bottom.set_ylabel("yield")
     bottom.set_xlabel(entry["variable"])
     bottom.legend(fontsize=12)

From 00ca7e3dcb1e32a004211959368d7b50a77b976d Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 11:45:14 +0200
Subject: [PATCH 23/33] fix(fake_factors): refuse --merge

One run measures every channel, there is nothing to merge.
---
 src/shapesmith/measurements/fake_factors/model.py | 2 ++
 tests/test_fake_factors.py                        | 7 +++++++
 2 files changed, 9 insertions(+)

diff --git a/src/shapesmith/measurements/fake_factors/model.py b/src/shapesmith/measurements/fake_factors/model.py
index f840f75..7fb2964 100644
--- a/src/shapesmith/measurements/fake_factors/model.py
+++ b/src/shapesmith/measurements/fake_factors/model.py
@@ -150,6 +150,8 @@ def columns(self, channel: str) -> set[str]:
         return columns
 
     def run(self, context) -> None:
+        if context.merge:
+            raise ValueError("fake_factors: --merge is not supported, one run measures every channel")
         from shapesmith.measurements.fake_factors.measure import run  # plots and correctionlib load only for a run
 
         run(self, context)
diff --git a/tests/test_fake_factors.py b/tests/test_fake_factors.py
index 00d74ba..444754c 100644
--- a/tests/test_fake_factors.py
+++ b/tests/test_fake_factors.py
@@ -161,3 +161,10 @@ def test_suggest_binning_writes_nothing(measured, capsys, tmp_path):
     run_measure(config, analysis, ["mt"], suggest_binning=True)
     printed = capsys.readouterr().out
     assert "mt QCD_fake_factors category 0: suggested [30.0," in printed and not (tmp_path / "suggest").exists()
+
+
+def test_merge_is_refused(measured, tmp_path):
+    config, analysis, _, _, _ = measured
+    config = config.model_copy(update={"output_dir": tmp_path / "merge"})
+    with pytest.raises(ValueError, match="--merge is not supported"):
+        run_measure(config, analysis, ["mt"], merge=True)

From 312d9c224648573d0bfa33bcd3ffdbb9f681e95e Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 11:53:55 +0200
Subject: [PATCH 24/33] feat(tau_id_es)!: close-up scans set the singles start
 values and ranges

As the predecessor (emb_tau_id_sfs_ul.sh, plot_2D_scan.py): close-up 1D
scans of each POI, the other profiled, start at the lowest grid point of
the 2D scan within its 2 sigma intervals (2 dNLL = 6.18) widened by
0.2 / 2 %; the singles fit starts at the lowest points of the close-up
scans within their 2 sigma intervals widened by 0.05 / 0.5 %. The
close-up ranges come from the profiles of the 2D scan, where the
predecessor used coarse 1D scans and hand-tuned margins. This replaces
the 2D-scan box and its boundary problem; the singles interval check
stays.
---
 README.md                                     |  10 +-
 .../measurements/tau_id_es/__init__.py        |  24 ++--
 .../measurements/tau_id_es/combine.py         | 130 +++++++++++++-----
 tests/test_tau_id_es.py                       |  46 +++++--
 4 files changed, 148 insertions(+), 62 deletions(-)

diff --git a/README.md b/README.md
index e9ab821..03b7d4f 100644
--- a/README.md
+++ b/README.md
@@ -112,10 +112,12 @@ the results of earlier runs into one payload). Measurements:
 - `shapesmith.measurements.tau_id_es`: tau-ID scale factor and energy scale of embedded taus, the
   chain of smhtt_ul `tauID_SFs_dev`. One run per working-point combination fills the mt channel
   and the mm control region, writes the shapes in the input format of MorphingTauID2017 and runs
-  per category in CMSSW the datacards, T2W, a 2D likelihood scan and the MultiDimFit singles fit,
-  the result (`results.json`, plots). The energy scale is a grid of template column variations in
-  units of 0.1 % (`grid.py`); `--merge` writes the correctionlib payload of all combinations. A
-  1 sigma interval at the fit range or a scan region at the scan boundary is an error.
+  per category in CMSSW the datacards, T2W, a 2D likelihood scan, close-up 1D scans of both POIs
+  and the MultiDimFit singles fit, the result (`results.json`, plots). As in the predecessor, the
+  close-up scans and the singles fit start at the lowest grid point of the step before, within its
+  2 sigma intervals widened by a margin. The energy scale is a grid of template column variations
+  in units of 0.1 % (`grid.py`); `--merge` writes the correctionlib payload of all combinations.
+  A 1 sigma interval at the fit range or a failed crossing is an error.
 - `shapesmith.measurements.fake_factors`: the fake-factor measurement (the SM method of
   TauFakeFactors). An analysis sets `Analysis.measurement = FakeFactorMeasurement(legs)`, built
   from its region and process names and per-channel tables (`Binned`, `Fit`, `Split`,
diff --git a/src/shapesmith/measurements/tau_id_es/__init__.py b/src/shapesmith/measurements/tau_id_es/__init__.py
index 680bc63..4401c80 100644
--- a/src/shapesmith/measurements/tau_id_es/__init__.py
+++ b/src/shapesmith/measurements/tau_id_es/__init__.py
@@ -7,11 +7,10 @@
 
 Output, below <output_dir>/tau_id_es/<era>/:
 - <vsjet>_<vsele>/: shapes.root (histograms and estimates), synced/ (MorphingTauID2017 inputs), fits/ (per
-  category the cards, workspace, scan and singles fit), results.json, plots/;
+  category the cards, workspace, scans and singles fit), results.json, plots/;
 - DeepTau2018v2p5_id_es_embedding<era>UL.json.gz, written by --merge.
-A result with a problem (an interval at the fit range, a scan region at the scan boundary, a failed crossing, a CMSSW
-step that failed or ran longer than STEP_TIMEOUT) is an error: the run raises after writing results.json, and
---merge refuses it.
+A result with a problem (an interval at the fit range, a failed crossing, a CMSSW step that failed or ran longer than
+STEP_TIMEOUT) is an error: the run raises after writing results.json, and --merge refuses it.
 """
 from __future__ import annotations
 
@@ -71,8 +70,8 @@ def check(analysis: Analysis, measurement: TauIdEsMeasurement) -> None:
 
 
 def fit_category(combine_config: CombineConfig | None, category: str, synced: Path, directory: Path, era: str, grid: tuple[int, ...]) -> dict:
-    """Datacards, workspace, 2D scan, singles fit and postfit shapes of one category below `directory`/fits; its
-    result and problems."""
+    """Datacards, workspace, 2D scan, close-up scans, singles fit and postfit shapes of one category below
+    `directory`/fits; its result and problems."""
     work_dir = Path(directory) / "fits"
     work_dir.mkdir(parents=True, exist_ok=True)
     cmssw.run([f"{combine.morphing_command(category, synced, era, grid)} > morphing_{category}.log 2>&1"], combine_config, work_dir, STEP_TIMEOUT)
@@ -80,11 +79,18 @@ def fit_category(combine_config: CombineConfig | None, category: str, synced: Pa
     combine.add_rate_parameters(cards, category)
     cmssw.run([f"{combine.workspace_command(category)} > workspace.log 2>&1", f"{combine.scan_command(category, grid)} > scan.log 2>&1"], combine_config, cards, STEP_TIMEOUT)
     scan = combine.read_scan(combine.output_path(cards, f"scan_2D_{category}"), category)
-    start, r_range, es_range, problems = combine.singles_inputs(scan, grid)
+    closeup = combine.closeup_inputs(scan, grid)
+    cmssw.run([f"{combine.closeup_command(category, poi, *closeup)} > closeup_{poi}.log 2>&1" for poi in combine.pois(category)], combine_config, cards, STEP_TIMEOUT)
+    profiles = tuple(combine.read_profile(combine.output_path(cards, f"closeup_{poi}"), poi) for poi in combine.pois(category))
+    start, r_range, es_range = combine.singles_inputs(profiles, grid)
     cmssw.run([f"{combine.singles_command(category, start, r_range, es_range)} > singles.log 2>&1", f"{combine.postfit_command(category)} > postfit.log 2>&1"], combine_config, cards, STEP_TIMEOUT)
     sf, es = combine.read_singles(combine.output_path(cards, f"singles_{category}"), category)
-    problems += combine.interval_problems("r", sf, r_range) + combine.interval_problems("ES", es, es_range)
-    return {"sf": asdict(sf), "es": asdict(es), "start": start, "ranges": {"r": r_range, "ES": es_range}, "problems": problems}
+    problems = combine.interval_problems("r", sf, r_range) + combine.interval_problems("ES", es, es_range)
+    return {"sf": asdict(sf), "es": asdict(es), **_inputs(start, r_range, es_range), "closeup": _inputs(*closeup), "problems": problems}
+
+
+def _inputs(start: tuple[float, float], r_range: tuple[float, float], es_range: tuple[float, float]) -> dict:
+    return {"start": start, "ranges": {"r": r_range, "ES": es_range}}
 
 
 def fit_or_failure(combine_config: CombineConfig | None, category: str, synced: Path, directory: Path, era: str, grid: tuple[int, ...]) -> dict:
diff --git a/src/shapesmith/measurements/tau_id_es/combine.py b/src/shapesmith/measurements/tau_id_es/combine.py
index a0528e2..a223a2f 100644
--- a/src/shapesmith/measurements/tau_id_es/combine.py
+++ b/src/shapesmith/measurements/tau_id_es/combine.py
@@ -1,7 +1,10 @@
 """The CMSSW part of the measurement, per category, with the options of the predecessor (smhtt_ul tauID_SFs_dev,
 tau_id_es_measurement/emb_tau_id_sfs_ul.sh): MorphingTauID2017 datacards plus the r_DY_incl rate parameters, the
-multiSignalModel workspace, the 2D likelihood scan over (r_EMB, ES) and the MultiDimFit singles fit, which is the
-result. The singles fit starts at the scan's best fit, inside the ranges the scan gives (singles_inputs).
+multiSignalModel workspace, the 2D likelihood scan over (r_EMB, ES), the close-up 1D scans of each POI with the other
+profiled and the MultiDimFit singles fit, which is the result. As in the predecessor (plot_2D_scan.py), each step
+starts at the lowest grid point of the previous one and its ranges are the 2 sigma intervals of that step, widened
+by a margin: the 2D scan gives the close-up ranges (the predecessor used coarse 1D scans and hand-tuned margins for
+them), the close-up scans give the singles ranges.
 
 The POIs: r_EMB_<category> (the scale factor) and ES_<category> (the energy scale in percent).
 """
@@ -20,8 +23,13 @@
 R_RANGE = (0.1, 2.9)  # of the workspace
 SCAN_R_RANGE = (0.11, 2.89)
 SCAN_POINTS = 289
-SCAN_LEVEL = 4.5  # delta NLL of the scan region that gives the singles ranges: 3 sigma in one parameter
+CLOSEUP_POINTS = 17
+LEVEL = 6.18 / 2  # delta NLL of the predecessor's 2 sigma intervals (2 delta NLL = 6.18, the 2 sigma level of two parameters)
+CLOSEUP_MARGIN = (0.2, 2.0)  # (r, ES): widening of the 2D scan's intervals to the close-up ranges
+SINGLES_MARGIN = (0.05, 0.5)  # (r, ES): widening of the close-up scans' intervals to the singles ranges
 ROBUST_FIT = "--robustFit=1 --setRobustFitAlgo=Minuit2 --X-rtd FITTER_NEW_CROSSING_ALGO --X-rtd FITTER_NEVER_GIVE_UP"
+SCAN_FIT = f"{ROBUST_FIT} --cminFallbackAlgo Minuit2,Migrad,0:0.001 --cminFallbackAlgo Minuit2,Migrad,1:0.01 --cminPreScan"
+Pair = tuple[float, float]
 
 
 @dataclass(frozen=True)
@@ -33,13 +41,58 @@ class Interval:
 
 @dataclass(frozen=True)
 class Scan:
-    """The 2D scan: its initial best fit and the grid points (r, ES, delta NLL)."""
+    """The grid points of the 2D scan (r, ES, delta NLL)."""
 
-    best: tuple[float, float]
     r: np.ndarray
     es: np.ndarray
     dnll: np.ndarray
 
+    def profile(self, axis: int) -> Profile:
+        """The profile of r (axis 0) or ES (axis 1) on the grid: the lowest delta NLL over the other POI."""
+        values = (self.r, self.es)[axis]
+        dnll = np.where(np.isfinite(self.dnll), self.dnll, np.inf)
+        grid = np.unique(values)
+        return Profile.of(grid, np.array([dnll[values == value].min() for value in grid]))
+
+
+@dataclass(frozen=True)
+class Profile:
+    """A 1D likelihood scan: sorted grid values and their delta NLL, relative to the fit or, if a grid point lies
+    lower (a fit that missed the minimum), to that point."""
+
+    values: np.ndarray
+    dnll: np.ndarray
+
+    @classmethod
+    def of(cls, values: np.ndarray, dnll: np.ndarray) -> Profile:
+        finite = np.isfinite(dnll)
+        order = np.argsort(values[finite], kind="stable")
+        values, dnll = values[finite][order], dnll[finite][order]
+        return cls(values, dnll - min(0.0, dnll.min()))
+
+    @property
+    def minimum(self) -> float:
+        """The lowest grid point, the start value of the next step (the predecessor's workaround for failed fits)."""
+        return float(self.values[np.argmin(self.dnll)])
+
+    def crossings(self, level: float) -> tuple[float | None, float | None]:
+        """The outermost crossings of `level` on each side of the minimum, linearly interpolated; None if there is
+        none (plot_2D_scan.extract_confidence_interval)."""
+        x, y, best = self.values, self.dnll, int(np.argmin(self.dnll))
+
+        def crossing(i: int) -> float:  # on the segment from grid point i to i + 1
+            return float(x[i] + (level - y[i]) * (x[i + 1] - x[i]) / (y[i + 1] - y[i]))
+
+        low = [i for i in range(best) if y[i] > level > y[i + 1]]
+        high = [i for i in range(best, len(x) - 1) if y[i] < level < y[i + 1]]
+        return (crossing(low[0]) if low else None), (crossing(high[-1]) if high else None)
+
+    def window(self, margin: float, bounds: Pair) -> Pair:
+        """The 2 sigma interval (an end of the scan where it does not cross), widened by `margin`, within `bounds`."""
+        low, high = self.crossings(LEVEL)
+        low, high = self.values[0] if low is None else low, self.values[-1] if high is None else high
+        return max(float(low) - margin, bounds[0]), min(float(high) + margin, bounds[1])
+
 
 def pois(category: str) -> tuple[str, str]:
     return f"r_{SIGNAL}_{category}", f"ES_{category}"
@@ -96,7 +149,7 @@ def workspace_command(category: str) -> str:
             f" --PO \"map=^.*/{SIGNAL}_{category}:{r}[1,{R_RANGE[0]},{R_RANGE[1]}]\"")
 
 
-def _ranges(category: str, r_range: tuple[float, float], es: tuple[float, float]) -> str:
+def _ranges(category: str, r_range: Pair, es: Pair) -> str:
     r, es_poi = pois(category)
     return f"--setParameterRanges {r}={r_range[0]!r},{r_range[1]!r}:{es_poi}={es[0]!r},{es[1]!r}"
 
@@ -104,13 +157,20 @@ def _ranges(category: str, r_range: tuple[float, float], es: tuple[float, float]
 def scan_command(category: str, grid: tuple[int, ...]) -> str:
     r, es = pois(category)
     return (f"combineTool.py -M MultiDimFit -n .scan_2D_{category} -d workspace.root --setParameters {r}=1.0,{es}=0.0"
-            f" {_ranges(category, SCAN_R_RANGE, scan_es_range(grid))} {ROBUST_FIT}"
-            " --cminFallbackAlgo Minuit2,Migrad,0:0.001 --cminFallbackAlgo Minuit2,Migrad,1:0.01 --cminPreScan"
+            f" {_ranges(category, SCAN_R_RANGE, scan_es_range(grid))} {SCAN_FIT}"
             f" --redefineSignalPOIs {r},{es} --floatOtherPOIs=1 --points={SCAN_POINTS} --algo grid -m {MASS}"
             " --alignEdges=1 --cminDefaultMinimizerStrategy 0")
 
 
-def singles_command(category: str, start: tuple[float, float], r_range: tuple[float, float], es: tuple[float, float]) -> str:
+def closeup_command(category: str, poi: str, start: Pair, r_range: Pair, es: Pair) -> str:
+    """The close-up scan of `poi`, the other POI profiled."""
+    r, es_poi = pois(category)
+    return (f"combineTool.py -M MultiDimFit -n .closeup_{poi} -d workspace.root --setParameters {es_poi}={start[1]!r},{r}={start[0]!r}"
+            f" {_ranges(category, r_range, es)} {SCAN_FIT} --redefineSignalPOIs {poi} --algo grid -m {MASS}"
+            f" --cminDefaultMinimizerStrategy 0 --floatOtherPOIs=1 --points={CLOSEUP_POINTS} --alignEdges=1")
+
+
+def singles_command(category: str, start: Pair, r_range: Pair, es: Pair) -> str:
     r, es_poi = pois(category)
     return (f"combineTool.py -M MultiDimFit -n .singles_{category} -d workspace.root"
             f" --setParameters {es_poi}={start[1]!r},{r}={start[0]!r} {_ranges(category, r_range, es)} {ROBUST_FIT}"
@@ -129,36 +189,34 @@ def output_path(cards: Path, name: str) -> Path:
 
 
 def read_scan(path: Path, category: str) -> Scan:
+    """The grid points of the 2D scan (its first entry is the fit)."""
     r, es = pois(category)
     with uproot.open(path) as f:
         rows = f["limit"].arrays([r, es, "deltaNLL"], library="np")
-    return Scan((float(rows[r][0]), float(rows[es][0])), rows[r][1:].astype(float), rows[es][1:].astype(float), rows["deltaNLL"][1:].astype(float))
-
-
-def _box(values: np.ndarray, inside: np.ndarray, scan_range: tuple[float, float]) -> tuple[tuple[float, float], bool]:
-    """The range of `values[inside]` widened by one grid step, clipped to the scan range; True if it was clipped."""
-    step = float(np.min(np.diff(np.unique(values))))
-    low, high = float(values[inside].min()) - step, float(values[inside].max()) + step
-    clipped = low < scan_range[0] or high > scan_range[1]
-    return (max(low, scan_range[0]), min(high, scan_range[1])), clipped
-
-
-def singles_inputs(scan: Scan, grid: tuple[int, ...]) -> tuple[tuple[float, float], tuple[float, float], tuple[float, float], list[str]]:
-    """Start values, r range and ES range of the singles fit, and the problems of the scan.
-
-    The ranges enclose the grid points within SCAN_LEVEL of the lowest one, widened by one grid step: the scan
-    replaces the close-up scans the predecessor took them from. The start is the scan's best fit, or the lowest grid
-    point if the best fit lies outside. A region that reaches the scan boundary is a problem (extend the scan)."""
-    finite = np.isfinite(scan.dnll)
-    inside = finite & (scan.dnll <= scan.dnll[finite].min() + SCAN_LEVEL)
-    r_range, r_clipped = _box(scan.r[finite], inside[finite], SCAN_R_RANGE)
-    es, es_clipped = _box(scan.es[finite], inside[finite], scan_es_range(grid))
-    problems = [f"the {name} scan region reaches the scan boundary" for name, clipped in (("r", r_clipped), ("ES", es_clipped)) if clipped]
-    start = scan.best
-    if not (r_range[0] < start[0] < r_range[1] and es[0] < start[1] < es[1]):
-        lowest = int(np.argmin(np.where(finite, scan.dnll, np.inf)))
-        start = (float(scan.r[lowest]), float(scan.es[lowest]))
-    return start, r_range, es, problems
+    return Scan(rows[r][1:].astype(float), rows[es][1:].astype(float), rows["deltaNLL"][1:].astype(float))
+
+
+def read_profile(path: Path, poi: str) -> Profile:
+    """A close-up scan (its first entry is the fit)."""
+    with uproot.open(path) as f:
+        rows = f["limit"].arrays([poi, "deltaNLL"], library="np")
+    return Profile.of(rows[poi][1:].astype(float), rows["deltaNLL"][1:].astype(float))
+
+
+def closeup_inputs(scan: Scan, grid: tuple[int, ...]) -> tuple[Pair, Pair, Pair]:
+    """Start values, r range and ES range of the close-up scans, from the profiles of the 2D scan."""
+    return _next_inputs((scan.profile(0), scan.profile(1)), CLOSEUP_MARGIN, grid)
+
+
+def singles_inputs(closeups: tuple[Profile, Profile], grid: tuple[int, ...]) -> tuple[Pair, Pair, Pair]:
+    """Start values, r range and ES range of the singles fit, from the close-up scans of r and ES."""
+    return _next_inputs(closeups, SINGLES_MARGIN, grid)
+
+
+def _next_inputs(profiles: tuple[Profile, Profile], margins: Pair, grid: tuple[int, ...]) -> tuple[Pair, Pair, Pair]:
+    start = (profiles[0].minimum, profiles[1].minimum)
+    r_range, es = (p.window(margin, bounds) for p, margin, bounds in zip(profiles, margins, (SCAN_R_RANGE, scan_es_range(grid))))
+    return start, r_range, es
 
 
 def read_singles(path: Path, category: str) -> tuple[Interval, Interval]:
diff --git a/tests/test_tau_id_es.py b/tests/test_tau_id_es.py
index f4a0d79..5c94cec 100644
--- a/tests/test_tau_id_es.py
+++ b/tests/test_tau_id_es.py
@@ -10,7 +10,7 @@
 from shapesmith.histogram import HistKey, Histogram, HistogramSet
 from shapesmith.measurements import MeasureContext
 from shapesmith.measurements.tau_id_es import TauIdEsMeasurement, check, combine, merge, plots
-from shapesmith.measurements.tau_id_es.combine import Interval, Scan
+from shapesmith.measurements.tau_id_es.combine import Interval, Profile, Scan
 from shapesmith.measurements.tau_id_es.grid import CATEGORIES, factor, grid, grid_name, mass, shift_of
 from shapesmith.measurements.tau_id_es.payload import correction_set
 from shapesmith.measurements.tau_id_es.synced import shapes_path, signal_name, write_synced
@@ -92,6 +92,10 @@ def test_commands_have_the_predecessor_options(tmp_path):
                                                 " --PO \"map=^.*/EMB_DM0:r_EMB_DM0[1,0.1,2.9]\"")
     scan = combine.scan_command("DM0", GRID)
     assert "--setParameterRanges r_EMB_DM0=0.11,2.89:ES_DM0=-19.9,19.9" in scan and "--points=289 --algo grid" in scan
+    closeup = combine.closeup_command("DM0", "ES_DM0", (0.9, -1.5), (0.6, 1.2), (-10.0, 6.0))
+    assert closeup.startswith("combineTool.py -M MultiDimFit -n .closeup_ES_DM0 -d workspace.root --setParameters ES_DM0=-1.5,r_EMB_DM0=0.9"
+                              " --setParameterRanges r_EMB_DM0=0.6,1.2:ES_DM0=-10.0,6.0")
+    assert closeup.endswith("--redefineSignalPOIs ES_DM0 --algo grid -m 125 --cminDefaultMinimizerStrategy 0 --floatOtherPOIs=1 --points=17 --alignEdges=1")
     singles = combine.singles_command("DM0", (0.9, -1.5), (0.7, 1.1), (-8.0, 4.0))
     assert "--setParameters ES_DM0=-1.5,r_EMB_DM0=0.9 --setParameterRanges r_EMB_DM0=0.7,1.1:ES_DM0=-8.0,4.0" in singles
     assert "--algo singles" in singles and "--cminFallbackAlgo Minuit2,Migrad,0:0.001,Minuit2,Migrad,0:0.01" in singles
@@ -122,19 +126,35 @@ def test_read_singles_and_its_problems(tmp_path):
     assert "no interval" in combine.interval_problems("ES", Interval(1.0, 1.0, 2.0), (-5.0, 5.0))[0]
 
 
-def _scan(best=(0.9, -2.0), sigma=(0.05, 2.0)):
+def _scan(best=(0.9, -2.0), sigma=(0.2, 5.0)):
     r, es = np.meshgrid(np.linspace(0.11, 2.89, 17), np.linspace(-19.9, 19.9, 17))
     dnll = 0.5 * (((r - best[0]) / sigma[0]) ** 2 + ((es - best[1]) / sigma[1]) ** 2)
-    return Scan(best, r.ravel(), es.ravel(), dnll.ravel())
-
-
-def test_singles_inputs_from_the_scan():
-    start, r_range, es_range, problems = combine.singles_inputs(_scan(sigma=(0.2, 5.0)), GRID)
-    assert start == (0.9, -2.0) and problems == []
-    assert r_range[0] < 0.9 - 0.2 and r_range[1] > 0.9 + 0.2 and es_range[0] < -7.0 and es_range[1] > 3.0  # the 1 sigma interval inside
-    lost = dataclasses.replace(_scan(sigma=(0.2, 5.0)), best=(0.11, -19.9))  # the scan's initial fit ended in a corner
-    assert combine.singles_inputs(lost, GRID)[0] == pytest.approx((0.97875, -2.4875))  # the lowest grid point
-    assert combine.singles_inputs(_scan(best=(0.9, 18.0), sigma=(0.2, 5.0)), GRID)[3] == ["the ES scan region reaches the scan boundary"]
+    return Scan(r.ravel(), es.ravel(), dnll.ravel())
+
+
+def test_profile_intervals_as_the_predecessor():
+    profile = Profile.of(np.array([4.0, 0.0, 1.0, 2.0, 3.0, np.nan]), np.array([6.0, 5.0, 1.0, 0.0, 2.0, np.inf]))
+    assert profile.values.tolist() == [0.0, 1.0, 2.0, 3.0, 4.0] and profile.minimum == 2.0
+    assert profile.crossings(3.09) == pytest.approx((0.4775, 3.2725))  # linear between the grid points
+    assert profile.window(0.1, (0.4, 10.0)) == pytest.approx((0.4, 3.3725))  # widened by the margin, within the bounds
+    outermost = Profile.of(np.arange(6.0), np.array([5.0, 2.0, 4.0, 0.0, 1.0, 7.0]))
+    assert outermost.crossings(3.09)[0] == pytest.approx(1 - 1.09 / 3)
+    no_crossing = Profile.of(np.arange(3.0), np.array([1.0, -0.5, 1.0]))  # a grid point below the fit
+    assert no_crossing.dnll.tolist() == [1.5, 0.0, 1.5] and no_crossing.window(0.1, (-5.0, 5.0)) == pytest.approx((-0.1, 2.1))
+
+
+def test_scan_profiles_and_the_inputs_of_the_next_steps():
+    small = Scan(np.array([1.0, 2.0, 1.0, 2.0]), np.array([0.0, 0.0, 5.0, 5.0]), np.array([0.5, 3.0, 1.0, np.nan]))
+    assert small.profile(0).dnll.tolist() == [0.5, 3.0] and small.profile(1).dnll.tolist() == [0.5, 1.0]  # the lowest over the other POI
+    scan = _scan()
+    start, r_range, es_range = combine.closeup_inputs(scan, GRID)
+    assert start == pytest.approx((0.97875, -2.4875))  # the lowest grid point
+    two_sigma = 0.2 * np.sqrt(6.18 - 2 * scan.profile(0).dnll.min())
+    assert r_range == pytest.approx((0.9 - two_sigma - 0.2, 0.9 + two_sigma + 0.2), abs=0.01)  # interpolated on the grid, plus the margin
+    assert combine.closeup_inputs(_scan(best=(0.9, 18.0)), GRID)[2][1] == 19.9  # no crossing: the scan end, within the scan range
+    closeups = (Profile.of(np.linspace(0.6, 1.2, 17), 0.5 * ((np.linspace(0.6, 1.2, 17) - 0.9) / 0.1) ** 2), scan.profile(1))
+    start, r_range, _ = combine.singles_inputs(closeups, GRID)
+    assert start[0] == pytest.approx(0.9) and r_range == pytest.approx((0.9 - 0.2486 - 0.05, 0.9 + 0.2486 + 0.05), abs=2e-3)
 
 
 def _results(offset=0.0):
@@ -178,7 +198,7 @@ def test_merge_writes_the_payload_of_every_working_point(tmp_path, mini_run):
 
 
 def test_plots_are_written(tmp_path):
-    scan = _scan(sigma=(0.2, 5.0))
+    scan = _scan()
     assert plots.plot_scan(scan, Interval(0.9, 0.7, 1.1), Interval(-2.0, -7.0, 3.0), "DM0", tmp_path / "scan")[0].exists()
     hset = HistogramSet()
     for process, variation, value in (("data", "Nominal", 3.0), ("EMB", "Nominal", 2.0), ("EMB", "es-2", 1.8), ("ZL", "Nominal", 1.0)):

From aaf8d5431d20571536b5d9ec29bd57b221f0ab53 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 11:56:37 +0200
Subject: [PATCH 25/33] docs(fake_factors): SystMCShiftDown at the centres of
 mass is decision U13

---
 src/shapesmith/measurements/fake_factors/fit.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/shapesmith/measurements/fake_factors/fit.py b/src/shapesmith/measurements/fake_factors/fit.py
index 67253a0..c01ddd9 100644
--- a/src/shapesmith/measurements/fake_factors/fit.py
+++ b/src/shapesmith/measurements/fake_factors/fit.py
@@ -2,7 +2,9 @@
 smooth_function, FF_Updated helper/ff_functions.py sha256 31f89add...).
 
 The variations are StatShift (the statistical band), SystMCShift (the ratio with every subtracted process shifted by
-+-1 sigma, fitted the same way), SystBandHigh/Low (the curve with bandwidth x1.5/x0.5, Down its mirror around the
++-1 sigma, fitted the same way: both sides smoothed at their own centres of mass, user decision U13; TauFakeFactors
+smooths the Down side of the corrections at the bin centres, because a deepcopy drops the centres of mass),
+SystBandHigh/Low (the curve with bandwidth x1.5/x0.5, Down its mirror around the
 nominal) and SystBandAsym (Up the x1.5 and Down the x0.5 curve). A smoothed ratio that is compatible with 1
 (statistical_check) is reset to 1; smoothed curves are then sparsified. The spec's "Algorithm reference" states
 every rule.

From a5dbfd9705ef4ee56370dafe0234f0ef7dbabf54 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 12:18:16 +0200
Subject: [PATCH 26/33] docs(tau_id_es): where the predecessor took its
 close-up ranges from

---
 src/shapesmith/measurements/tau_id_es/combine.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/shapesmith/measurements/tau_id_es/combine.py b/src/shapesmith/measurements/tau_id_es/combine.py
index a223a2f..509133f 100644
--- a/src/shapesmith/measurements/tau_id_es/combine.py
+++ b/src/shapesmith/measurements/tau_id_es/combine.py
@@ -3,8 +3,8 @@
 multiSignalModel workspace, the 2D likelihood scan over (r_EMB, ES), the close-up 1D scans of each POI with the other
 profiled and the MultiDimFit singles fit, which is the result. As in the predecessor (plot_2D_scan.py), each step
 starts at the lowest grid point of the previous one and its ranges are the 2 sigma intervals of that step, widened
-by a margin: the 2D scan gives the close-up ranges (the predecessor used coarse 1D scans and hand-tuned margins for
-them), the close-up scans give the singles ranges.
+by a margin: the profiles of the 2D scan give the close-up ranges (the predecessor took them from 1D scans over the
+full range, some with margins set by hand), the close-up scans give the singles ranges.
 
 The POIs: r_EMB_<category> (the scale factor) and ES_<category> (the energy scale in percent).
 """

From fab578735cf316d22230cbd0b5a7749774629a97 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 12:31:35 +0200
Subject: [PATCH 27/33] fix(validate): the signal must be a process with the
 role signal

A background named as the signal (the tau-ID measurement named EMB) was
accepted, and sync, datacards and plots then list it twice.
---
 src/shapesmith/validate.py | 4 ++--
 tests/test_validate.py     | 9 +++++++++
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/src/shapesmith/validate.py b/src/shapesmith/validate.py
index f964a9d..d7a4013 100644
--- a/src/shapesmith/validate.py
+++ b/src/shapesmith/validate.py
@@ -91,8 +91,8 @@ def validate(analysis: Analysis) -> None:
     """Raise AnalysisError listing every problem of the analysis."""
     problems = [problem for channel in analysis.channels.values() for problem in channel_problems(channel)]
     processes = {p.name: p.role for channel in analysis.channels.values() for p in channel.processes}
-    if analysis.signal is not None and analysis.signal not in processes:
-        problems.append(f"signal {analysis.signal} is not a process")
+    if analysis.signal is not None and processes.get(analysis.signal) != "signal":
+        problems.append(f"signal {analysis.signal} is not a process with the role signal")
     if analysis.ml is not None:
         problems += [f"ML export: {name} is an auxiliary process" for name in analysis.ml.processes if processes.get(name) == "auxiliary"]
     if problems:
diff --git a/tests/test_validate.py b/tests/test_validate.py
index d2c6f4d..9977af9 100644
--- a/tests/test_validate.py
+++ b/tests/test_validate.py
@@ -93,6 +93,15 @@ def test_validate_checks_estimators_and_auxiliary_processes():
         validate(_with_channel(analysis, samples=mixed))
 
 
+def test_the_signal_is_a_process_with_the_signal_role():
+    analysis = build()
+    with pytest.raises(model.AnalysisError, match="signal ZTT is not a process with the role signal"):
+        validate(dataclasses.replace(analysis, signal="ZTT"))
+    with pytest.raises(model.AnalysisError, match="signal nope is not a process"):
+        validate(dataclasses.replace(analysis, signal="nope"))
+    validate(dataclasses.replace(analysis, signal=None))
+
+
 def test_dataclasses_are_frozen():
     with pytest.raises(dataclasses.FrozenInstanceError):
         build().signal = "other"

From 9948e4870a9be3c2eb698256ffbad076d74c2fed Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 12:31:35 +0200
Subject: [PATCH 28/33] refactor(validate): collect the weight names of a
 channel once

---
 src/shapesmith/validate.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/shapesmith/validate.py b/src/shapesmith/validate.py
index d7a4013..f24737d 100644
--- a/src/shapesmith/validate.py
+++ b/src/shapesmith/validate.py
@@ -16,6 +16,10 @@ def _partner(name: str) -> str | None:
     return None
 
 
+def _weight_names(channel: Channel) -> set[str]:
+    return {name for p in channel.processes for name in p.selection.weights}
+
+
 def _process_problems(channel: Channel) -> list[str]:
     problems = _duplicates("process", [p.name for p in channel.processes])
     problems += _duplicates("sample", [s.nick for s in channel.samples])
@@ -32,7 +36,7 @@ def _process_problems(channel: Channel) -> list[str]:
 
 
 def _region_problems(channel: Channel) -> list[str]:
-    weights = {name for p in channel.processes for name in p.selection.weights}
+    weights = _weight_names(channel)
     problems = _duplicates("region", [NOMINAL, *(r.name for r in channel.regions)])
     for region in channel.regions:
         problems += [f"region {region.name}: replaces unknown cut {cut}" for cut in region.replace_cuts if cut not in channel.cuts]
@@ -42,7 +46,7 @@ def _region_problems(channel: Channel) -> list[str]:
 
 
 def _variation_problems(channel: Channel, region_names: set[str]) -> list[str]:
-    weights = {name for p in channel.processes for name in p.selection.weights}
+    weights = _weight_names(channel)
     groups = {s.group for s in channel.samples}
     names = [v.name for v in channel.variations]
     problems = _duplicates("variation", names)

From 8cb3e4bb2b01cd03d477d65812c43f1d12bdff8e Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 12:31:49 +0200
Subject: [PATCH 29/33] perf(expressions): parse each distinct expression once
 in columns_of

needed_columns collects the selections of every process, region and weight
variation, mostly the same expressions: the 72 tt samples of sm2018_binned_v3
took 6.9 s, now 0.3 s.
---
 src/shapesmith/expressions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/shapesmith/expressions.py b/src/shapesmith/expressions.py
index 9ec17f0..9128b3c 100644
--- a/src/shapesmith/expressions.py
+++ b/src/shapesmith/expressions.py
@@ -30,7 +30,7 @@ def columns_in(expr: str) -> set[str]:
 
 def columns_of(exprs: Iterable[str]) -> set[str]:
     columns: set[str] = set()
-    for expr in exprs:
+    for expr in set(exprs):  # the selections of many regions and variations repeat the same expressions
         columns |= columns_in(expr)
     return columns
 

From 81b232c5763c9c172e8790f5de8168e4d6b6f34c Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 12:32:32 +0200
Subject: [PATCH 30/33] refactor(payloads): one conversion of numeric leaves

---
 src/shapesmith/payloads.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/src/shapesmith/payloads.py b/src/shapesmith/payloads.py
index a8be9c9..c6cf5f0 100644
--- a/src/shapesmith/payloads.py
+++ b/src/shapesmith/payloads.py
@@ -18,6 +18,11 @@
 Node = cs.Binning | cs.Category | cs.Transform | float
 
 
+def _leaf(value):
+    """A number (also a numpy scalar) as a plain float; a node unchanged."""
+    return float(value) if isinstance(value, (int, float, np.number)) else value
+
+
 def variable(name: str, type: str = "real", description: str | None = None) -> cs.Variable:
     fields = {"description": description} if description is not None else {}
     return cs.Variable(name=name, type=type, **fields)
@@ -25,13 +30,12 @@ def variable(name: str, type: str = "real", description: str | None = None) -> c
 
 def binning(input: str, edges: Sequence[float], content: Sequence[Node], flow: str | Node = "clamp") -> cs.Binning:
     """A binned node; numbers (also numpy arrays) become plain floats."""
-    content = [float(c) if isinstance(c, (int, float, np.number)) else c for c in content]
-    return cs.Binning(nodetype="binning", input=input, edges=[float(e) for e in edges], content=content, flow=flow)
+    return cs.Binning(nodetype="binning", input=input, edges=[float(e) for e in edges], content=[_leaf(c) for c in content], flow=flow)
 
 
 def category(input: str, content: Mapping[str | int, Node], default: Node | None = None) -> cs.Category:
-    items = [cs.CategoryItem(key=key, value=float(value) if isinstance(value, (int, float, np.number)) else value) for key, value in content.items()]
-    fields = {"default": float(default) if isinstance(default, (int, float, np.number)) else default} if default is not None else {}
+    items = [cs.CategoryItem(key=key, value=_leaf(value)) for key, value in content.items()]
+    fields = {"default": _leaf(default)} if default is not None else {}
     return cs.Category(nodetype="category", input=input, content=items, **fields)
 
 

From 3d4da2123a14d165f7939bf8f10bc33c47726987 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 12:32:49 +0200
Subject: [PATCH 31/33] refactor(tau_id_es): one working-point order for the
 payload

---
 src/shapesmith/measurements/tau_id_es/payload.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/src/shapesmith/measurements/tau_id_es/payload.py b/src/shapesmith/measurements/tau_id_es/payload.py
index 9f22193..8bd11eb 100644
--- a/src/shapesmith/measurements/tau_id_es/payload.py
+++ b/src/shapesmith/measurements/tau_id_es/payload.py
@@ -21,6 +21,7 @@
 DECAY_MODES = (0, 1, 10, 11)
 OTHER_GENMATCH = (0, 1, 2, 3, 4, 6)
 WORKING_POINTS = ("VVVLoose", "VVLoose", "VLoose", "Loose", "Medium", "Tight", "VTight", "VVTight")
+WP_ORDER = {wp: index for index, wp in enumerate(WORKING_POINTS)}
 MAX_PT = 100000.0  # the upper edge of the per-decay-mode bins
 TAU_ID = "DeepTau2018v2p5"
 
@@ -56,18 +57,16 @@ def _by_decay_mode(results: Mapping[str, tuple[Interval, Interval]], index: int,
 
 def _genuine_taus(results: Results, node: Callable[[Mapping[str, tuple[Interval, Interval]]], cs.Category]) -> cs.Category:
     """1.0 unless genmatch is 5; then per working point, vsEle working point and decay mode."""
-    order = {wp: index for index, wp in enumerate(WORKING_POINTS)}
     by_wp: dict[str, dict] = {}
-    for vsjet, vsele in sorted(results, key=lambda pair: (order[pair[0]], order[pair[1]])):
+    for vsjet, vsele in sorted(results, key=lambda pair: (WP_ORDER[pair[0]], WP_ORDER[pair[1]])):
         by_wp.setdefault(vsjet, {})[vsele] = node(results[vsjet, vsele])
     wps = payloads.category("wp", {vsjet: payloads.category("wp_VSe", by_vsele) for vsjet, by_vsele in by_wp.items()})
     return payloads.category("genmatch", {**{genmatch: 1.0 for genmatch in OTHER_GENMATCH}, 5: wps})
 
 
 def _inputs(results: Results, *names: str) -> list[cs.Variable]:
-    order = {wp: index for index, wp in enumerate(WORKING_POINTS)}
-    vsjet = ",".join(sorted({pair[0] for pair in results}, key=order.get))
-    vsele = ",".join(sorted({pair[1] for pair in results}, key=order.get))
+    vsjet = ",".join(sorted({pair[0] for pair in results}, key=WP_ORDER.get))
+    vsele = ",".join(sorted({pair[1] for pair in results}, key=WP_ORDER.get))
     known = {
         "pt": ("real", "Reconstructed tau pT"),
         "eta": ("real", "Reconstructed tau eta"),

From de26894239168ce719f5ca47a0f12b2ea242da2c Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 12:37:32 +0200
Subject: [PATCH 32/33] refactor(tau_id_es): the synced shapes through
 shapes.synced_entries

The MorphingTauID2017 input differed from the synced layout only in the name
of the embedded signal; synced_entries takes the naming function instead of a
second copy of the loop. The test analysis has no signal, as the tau-ID
analysis.
---
 .../measurements/tau_id_es/synced.py          | 23 ++++++-------------
 src/shapesmith/shapes.py                      | 13 +++++++----
 tests/test_tau_id_es.py                       |  2 +-
 3 files changed, 17 insertions(+), 21 deletions(-)

diff --git a/src/shapesmith/measurements/tau_id_es/synced.py b/src/shapesmith/measurements/tau_id_es/synced.py
index f490c1e..acb96b3 100644
--- a/src/shapesmith/measurements/tau_id_es/synced.py
+++ b/src/shapesmith/measurements/tau_id_es/synced.py
@@ -9,10 +9,10 @@
 
 from pathlib import Path
 
-from shapesmith.histogram import NOMINAL_VARIATION, TEMPLATE_SEPARATOR, HistKey, Histogram, HistogramSet
-from shapesmith.measurements.tau_id_es.grid import CHANNEL, SIGNAL, mass, shift_of
-from shapesmith.model import NOMINAL, Analysis
-from shapesmith.shapes import shape_name, write_shapes
+from shapesmith.histogram import TEMPLATE_SEPARATOR, HistogramSet
+from shapesmith.measurements.tau_id_es.grid import SIGNAL, mass, shift_of
+from shapesmith.model import Analysis
+from shapesmith.shapes import shape_name, synced_entries, write_shapes
 
 POSTFIX = "-TauID_ES"
 
@@ -30,21 +30,12 @@ def signal_name(category: str, variation: str) -> str:
     return f"{SIGNAL}_{category}_{mass(shift)}" + (f"_{systematic}" if template else "")
 
 
-def entries(hset: HistogramSet, analysis: Analysis, channel_name: str) -> list[tuple[str, str, Histogram]]:
-    channel = analysis.channel(channel_name)
-    result = []
-    for category in channel.categories:
-        folder, variable = f"{channel_name}_{category.name}", category.variable.name
-        result.append((folder, "data_obs", hset[HistKey(channel_name, category.name, channel.data(), NOMINAL, NOMINAL_VARIATION, variable)]))
-        for process in channel.backgrounds():
-            for key in hset.select(channel=channel_name, category=category.name, process=process, region=NOMINAL, variable=variable):
-                name = signal_name(category.name, key.variation) if channel_name == CHANNEL and process == SIGNAL else shape_name(process, key.variation)
-                result.append((folder, name, hset[key]))
-    return result
+def _name(category: str, process: str, variation: str) -> str:
+    return signal_name(category, variation) if process == SIGNAL else shape_name(process, variation)
 
 
 def write_synced(hset: HistogramSet, analysis: Analysis, directory: Path) -> Path:
     """The shapes file of every channel of the analysis under `directory`; returns `directory`."""
     for channel in analysis.channels:
-        write_shapes(shapes_path(directory, channel, analysis.era), entries(hset, analysis, channel))
+        write_shapes(shapes_path(directory, channel, analysis.era), synced_entries(hset, analysis, channel, _name))
     return Path(directory)
diff --git a/src/shapesmith/shapes.py b/src/shapesmith/shapes.py
index 4f51af0..3e0a574 100644
--- a/src/shapesmith/shapes.py
+++ b/src/shapesmith/shapes.py
@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 from pathlib import Path
-from typing import Iterable
+from typing import Callable, Iterable
 
 import uproot
 
@@ -24,8 +24,13 @@ def shape_name(process: str, variation: str) -> str:
     return process if variation == NOMINAL_VARIATION else f"{process}_{variation}"
 
 
-def synced_entries(hset: HistogramSet, analysis: Analysis, channel_name: str) -> list[tuple[str, str, Histogram]]:
-    """data_obs and every nominal-region histogram of the signal and the backgrounds, per category."""
+def _synced_name(category: str, process: str, variation: str) -> str:
+    return shape_name(process, variation)
+
+
+def synced_entries(hset: HistogramSet, analysis: Analysis, channel_name: str, name: Callable[[str, str, str], str] = _synced_name) -> list[tuple[str, str, Histogram]]:
+    """data_obs and every nominal-region histogram of the signal and the backgrounds, per category, each named
+    `name(category, process, variation)`."""
     channel = analysis.channel(channel_name)
     entries = []
     for category in channel.categories:
@@ -36,7 +41,7 @@ def synced_entries(hset: HistogramSet, analysis: Analysis, channel_name: str) ->
             entries.append((folder, "data_obs", hset[data_key]))
         for process in (*channel.backgrounds(), analysis.signal):
             for key in hset.select(channel=channel_name, category=category.name, process=process, region=NOMINAL, variable=variable):
-                entries.append((folder, shape_name(process, key.variation), hset[key]))
+                entries.append((folder, name(category.name, process, key.variation), hset[key]))
     return entries
 
 
diff --git a/tests/test_tau_id_es.py b/tests/test_tau_id_es.py
index 5c94cec..40ea60b 100644
--- a/tests/test_tau_id_es.py
+++ b/tests/test_tau_id_es.py
@@ -50,7 +50,7 @@ def _analysis(shifts=(-2, 2)):
                   variations=tuple(ColumnVariation(grid_name(s), derived={"pt_2": f"pt_2 * {factor(s)}"}, applies_to=("embedding",)) for s in shifts),
                   estimators=(DataMinus("QCD", "same_sign", ("EMB", "ZL")),))
     mm = _channel("mm", [Category("control_region", "m_vis > 70", M_VIS)], [data, Process("MUEMB", "MUEMB", "background", "MUEMB")])
-    return Analysis("tau_id", "2018", 1.0, "EMB", {"mt": mt, "mm": mm})
+    return Analysis("tau_id", "2018", 1.0, None, {"mt": mt, "mm": mm})
 
 
 def _h(value):

From 6c29e1a0337bfee221c1e9029a14abbaa43f3670 Mon Sep 17 00:00:00 2001
From: Simon Daigler <simon@daigler.de>
Date: Wed, 30 Sep 2026 12:37:51 +0200
Subject: [PATCH 33/33] style(cmssw): the kept environment variables with the
 other module constants

---
 src/shapesmith/cmssw.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/shapesmith/cmssw.py b/src/shapesmith/cmssw.py
index 63e8ebb..ca0d0d6 100644
--- a/src/shapesmith/cmssw.py
+++ b/src/shapesmith/cmssw.py
@@ -16,6 +16,7 @@
 from shapesmith.config import CombineConfig
 
 logger = logging.getLogger(__name__)
+KEPT_VARIABLES = ("HOME", "USER", "LOGNAME", "TMPDIR", "X509_USER_PROXY")  # the environment a CMSSW subshell starts with, besides PATH
 
 
 def script(combine: CombineConfig, cwd: Path, commands: Sequence[str]) -> str:
@@ -33,9 +34,6 @@ def script(combine: CombineConfig, cwd: Path, commands: Sequence[str]) -> str:
     )
 
 
-KEPT_VARIABLES = ("HOME", "USER", "LOGNAME", "TMPDIR", "X509_USER_PROXY")
-
-
 def clean_environment() -> dict[str, str]:
     return {"PATH": "/usr/bin:/bin", **{name: os.environ[name] for name in KEPT_VARIABLES if name in os.environ}}