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TODO Summary

Generated: 2026-03-25 18:04 UTC

Found 195 annotations.

TODO

MVA_training/VBF_run2_legacy/dnn_preprocessor.py

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632 add mixup

commands.md

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194

configs/dnn_run3_vbf.yaml

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24 Implement the "stratified" option from scikit

plotter/validation_plotter_unified.py

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713 Add overflow bins to the last bin

quick_tests/quickSMFtest_functions.py

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512 make a dictionary list of starting values optimized for sumexponential for specific data, starting with data_* for ggH
584 make a dictionary list of starting values optimized for sumexponential for specific data, starting with data_* for ggH

quick_tests/quick_DNN_training.ipynb

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229 input year as features and other stuff\n",
515 add mixup\n",

run_stage3_validation.py

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287 add ggH + VBF

scripts/collect_todos.py

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49 , TODO(...):, FIXME - ..., etc. (case-insensitive)
104 Summary\n")

src/copperhead_processor.py

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626 make more elegant distinction between Run2 and Run3
994 find out why we don't filter out bad events right now via
2152 STXS VBF cross-section uncertainty
3017 add btag systematics by adding seperate wgts

src/corrections/rochester.py

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126 find out why pt_scale isn't used for ptcorr for MC, bc for data pt_scale is used

src/lib/fit_functions.py

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653 make a dictionary list of starting values optimized for sumexponential for specific data, starting with data_* for ggH
725 make a dictionary list of starting values optimized for sumexponential for specific data, starting with data_* for ggH

stage3/make_templates.py

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642 decorrelate LHE, QGL, PDF uncertainties

FIXME

MVA_training/VBF_run2_legacy/run2_legacyModel/dnn_preprocessor.py

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465 sig and bkg processes defined at line 1976 of AN-19-124. IDK why ggH is not included here
705 Temporay fix to fetch both 2024 and others - replace "v12" with "v*" to accomodate v12 and v15 both. So, that I can combine 2024 with other years' data.

configs/datasets/dataset_nanoAODv12_run2_old.yaml

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59

configs/datasets/dataset_nanoAODv15_run2.yaml

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641 From here onwards below is the automated version of chatGPT from codimd doc (https://codimd.web.cern.ch/aK_Yxu91QHqpsjeWaa4aIw#Parts-missing). So there's a good chance that there's errors

configs/datasets/dataset_nanoAODv9.yaml

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20 Instead of "dummy" provide the /eos or /store path. And update code that when it sees that sample name starts from /store read it from EOS.

configs/datasets/sync_dataset_nanoAODv12.yaml

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19 replaced data_B -> data_D. So, that output file shoudl be same for 2017 and 2022preEE. Easier for the CI/CD sync names.

configs/parameters/SF_filelist.yaml

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29 Update SF
30 Update SF
31 Update SF
32 Update SF
33 Update SF
41
42
43
70 input binning error

configs/parameters/cross_sections.yaml

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81
86 source: AN-19-124 table 2.8
87 source: AN-19-124 table 2.8
134

configs/parameters/misc.yaml

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356 Do we still need this?

configs/parameters/switches.yaml

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294 Not validated yet for Run-3

plotter/plot_DNN_score.py

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117
263
266

plotter/validation_plotter_unified.py

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339 Later try to get the lumi from the copperhead_processor get_sample_info function
341 hardcoded filename
453 add the filter and selection while loading the parquet file
543 Is it mandatory to use all regions and channels name below? Or I can just replace it with args.regions and args.category?
675 this is hardcoded value, should be replaced with lumi value from config file
716 Why weights with data?

run_prestage.py

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418 force turn off xcache for data. This is temporary solution, until hadded for customnanod is fixed for the data files
555 Temporary fix. Remove this patch

run_stage1.py

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526 update this for /store usage

run_stage2.py

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230 . overwrite for testing
270 if there's any missing up and down variations, just plug it with the nominal value:
428 , temp hard code to add dummy prefiring wgts for 2018
861

run_stage2_vbf.py

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410 For debugging purpose.
508 This introduces some issue and number of entries does not remain same.

run_stage3.py

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686 replace sumexp with BWZxBern
699 replace sumexp with Powerlaw
721 replace sumexp with landauBern
735 replace sumexp with bwzGamma

run_stage3_validation.py

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734

src/copperhead_processor.py

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505 currently, dy_M-50 is hardcoded
641 Why pt_raw
1255 Remove this if condition later when we update the yaml file for run2 too.
1256 hardcoded filename
1300 For data (is is_mc == False) I should not add this variations.
1306 JER variation part is not running.
1888 year is temporarily here.
1987 weights and weightsmodifier are availalbe starting coffea: 2025.3.0
2051 weights and weightsmodifier are availalbe starting coffea: 2025.3.0
2054 extra info for (unweighted + weighted + efficiencies)
2166 Not used
2167 Not used
2267 hardcoded puID
2362
2364 we should get the weight for each jet and multiply them together.
2585 NanoAODv12 and NanoAODv15 have qgl as a field as AK4 jets are CHS for run-2, but not for run-3
2933
3056 Currently the working point is used what was defined for DeepB, should be updated for DeepFlavB

src/corrections/custom_jec.py

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34 jec_parameters is not defined
49 Hard code for now

src/corrections/jet.py

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546 Do we really this this? As HEM veto is there only for 2018 UL

src/lib/ebeMassResCalibration/basic_class_for_calibration.py

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319 for some reason ddf2.compute() is not running on gateway.
973 Added this condition for 2018 data, because the fit was not converging

stage1_loop_Improved.sh

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230 This is a temporary fix, will try to sync the naming convention in the stage2 python script.
368 Hardcoded DY sample name: aMCatNLO or MiNNLO or amcatnloFXFX or powheg or INCamcatnloFXFX

stage2/ggH_datacard/generate_datacard.py

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311
312

stage2_sh.sh

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152 make do_jecUnc optional
170 remove "all" from years list if exists

stage3/make_datacards.py

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39 update the values
47 update the values
55 update the values
63 update the values
71 update the values
112 WE should send this at a centralized YAML file
205 Temporarily commenting out the DYJ01 rate param since we are not using it in the datacard for now
213 Temporarily commenting out the DYJ01 rate param since we are not using it in the datacard for now
327 Temporarily commenting out the rate systematics since we are not using them in the datacard for now

validation/ggH/bias_test/run_bias_test.py

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236
1029 temporarily remove 2024 vbf parquet bc it's corrupt

validation/ggH/categorization/getTable_6_2And6_12.py

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105

validation/ggH/categorization/plot_6_13.py

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195 Update this from central place.

validation/ggH/categorization/plot_6_19.py

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209

validation/ggH/categorization/plot_6_7.py

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273 copy the edges from 2024 BDT edges yaml file

BUG

plotter/validation_plotter_unified.py

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658 PURPOSES

scripts/mergeNanoAODRootFiles.py

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27 )

stage1_loop_Improved.sh

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398 "
408 "

NOTE

DaskGatewaySLURM.ipynb

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88 The file .conda_env_name.txt will be generated by the setup_env.sh script.\n",

configs/parameters/switches.yaml

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304 for nanoAODv15 Run2, it should be false for now
314 for nanoAODv15 Run2, it should be false for now

docs/demo/blindedChi2NDF/chi2Ndf.py

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76 "the chi2 is calculated between a plot of the original distribution and the data. It therefore has more rounding errors " from https://root.cern.ch/doc/master/classRooPlot.html#ae2bbe62ce38dbf09bc894acbb117e68d
120 the difference chi2ndf values between method1 and method2 is about 3%,
262 we verify that chi2/ndf from method2 matches with method3. However, method4 doesn't work when it comes to combining two regions. This is an implementation issue, but I couldn't find a workaround. Thus, we abandon the use of method4.
297 for method4, we observe that chi2 obtained from chi2_ndf over the blinded region

modules/fit_functions.py

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574 the plots are sensitve to rebins

modules/selection.py

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105 fatjet and MET veto for VH: nfatJets_drmuon == 0 and MET_pt < 150 GeV
112 btag cut for VH and ttH categories

modules/selection_debug.py

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243 fatjet and MET veto for VH: nfatJets_drmuon == 0 and MET_pt < 150 GeV
250 btag cut for VH and ttH categories

quick_tests/quick_sort_test.ipynb

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214 this decorator assumes that the operation is exposed under ak.\u001b[39;00m\n\u001b[0;32m---> 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OperationErrorContext(name, args, kwargs):\n\u001b[1;32m 39\u001b[0m gen_or_result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m\u001b[39margs, \u001b[38;5;241m\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isgenerator(gen_or_result):\n",

quick_tests/quick_trigger_matching.ipynb

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290 this decorator assumes that the operation is exposed under ak.\u001b[39;00m\n\u001b[0;32m---> 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OperationErrorContext(name, args, kwargs):\n\u001b[1;32m 39\u001b[0m gen_or_result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m\u001b[39margs, \u001b[38;5;241m\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isgenerator(gen_or_result):\n",

scripts/get_significance.sh

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11 combine cards command is different for EACH category
14 Generate asimov with pdf_index that's most representative
20 combine cards command is different for EACH category
23 Generate asimov with pdf_index that's most representative
29 combine cards command is different for EACH category
32 Generate asimov with pdf_index that's most representative
38 combine cards command is different for EACH category
41 Generate asimov with pdf_index that's most representative
47 combine cards command is different for EACH category
50 Generate asimov with pdf_index that's most representative
56 combine cards command is different for EACH category
59 Generate asimov with pdf_index that's most representative
69 combine cards command is different for EACH category
72 Generate asimov with pdf_index that's most representative
78 combine cards command is different for EACH category
81 Generate asimov with pdf_index that's most representative
87 combine cards command is different for EACH category
90 Generate asimov with pdf_index that's most representative
96 combine cards command is different for EACH category
99 Generate asimov with pdf_index that's most representative
105 combine cards command is different for EACH category
108 Generate asimov with pdf_index that's most representative
115 combine cards command is different for EACH category
118 Generate asimov with pdf_index that's most representative
134 combine cards command is different for EACH category
137 Generate asimov with pdf_index that's most representative
142 combine cards command is different for EACH category
145 Generate asimov with pdf_index that's most representative
150 combine cards command is different for EACH category
153 Generate asimov with pdf_index that's most representative
158 combine cards command is different for EACH category
161 Generate asimov with pdf_index that's most representative
166 combine cards command is different for EACH category
169 Generate asimov with pdf_index that's most representative
175 combine cards command is different for EACH category
178 Generate asimov with pdf_index that's most representative
191 combine cards command is different for EACH category
194 Generate asimov with pdf_index that's most representative
202 combine cards command is different for EACH category
205 Generate asimov with pdf_index that's most representative
212 combine cards command is different for EACH category
215 Generate asimov with pdf_index that's most representative
223 combine cards command is different for EACH category
226 Generate asimov with pdf_index that's most representative
233 combine cards command is different for EACH category
236 Generate asimov with pdf_index that's most representative
243 combine cards command is different for EACH category
246 Generate asimov with pdf_index that's most representative

scripts/jetPUId_yieldTable.py

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107 fatjet and MET veto for VH: nfatJets_drmuon == 0 and MET_pt < 150 GeV
114 btag cut for VH and ttH categories

scripts/mergeNanoAODRootFiles.py

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306 the redirector
311 different redirector for the output path and creation of directory in above command.

src/copperhead/zpt_rewgt/derive/bin_definitions.py

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104 if last bin width is smaller than the previous one, merge the last two bins

src/copperhead/zpt_rewgt/derive/save_SF_rootFiles.py

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241 Set min/max or title should go after ratio_plot.Draw()

src/copperhead_processor.py

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292 its recommended to not apply the Jet_jetId or any other correction
427 Don't reset PuppiMET.phi otherwise we will see a peak at zero in PuppiMET.phi distribution
544 this would probably be superfluous if you already do trigger matching
608 Hardcoded chi2 cut for beam constraint
924 if you want to keep this method, (which I don't btw since the original
2934 SoftJET variables are all from Nominal variation despite variation names

src/corrections/geofit.py

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48 pt_corr == pt_roch - pt_gen

src/corrections/jet.py

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382 PUID for 2017 is tight in horn region but not for other eras in Run2
714 we assume that "nominal" is the first element list
846 if I name this "pt_jer_nominal", sorting jets by jet.pt breaks
1001 if you use pt, eta, phi, or t variables to initialize, it doesn't work. It's quite finnicky in that way.

src/lib/ebeMassResCalibration/basic_class_for_calibration.py

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778 Remember to provide "Name" argument to plotOn so that legend and chi2 can find the correct objects
791 compute chi2 after all plotOn calls to ensure correct components are drawn
996 The lower bound on sigma was intentionally loosened from 0.1 to 0.001
1198 Remember to provide "Name" argument to plotOn so that legend and chi2 can find the correct objects
1211 compute chi2 after all plotOn calls to ensure correct components are drawn