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# backup files
*~
.*.swp
# common byproducts
*.pyc
*.pyo
.vscode
.ipynb_checkpoints
__pycache__
# data files
*.root
*.xml
*.class.C
# plots
*.pdf
*.png
# downloads and envs
tuner_env
miniconda.sh

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stages:
- check
check:
stage: check
image: registry.cern.ch/docker.io/library/python:3.10
before_script:
- pip install pre-commit
script:
- pre-commit run --all-files
variables:
PRE_COMMIT_HOME: ${CI_PROJECT_DIR}/.cache/pre-commit
cache:
paths:
- ${PRE_COMMIT_HOME}

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repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.3.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-yaml
- repo: https://github.com/PyCQA/flake8
rev: '5.0.4'
hooks:
- id: flake8
args: ["--ignore=E203,W503,E501,E722"]
- repo: https://github.com/asottile/add-trailing-comma
rev: v2.2.3
hooks:
- id: add-trailing-comma
- repo: https://github.com/psf/black
rev: '22.8.0'
hooks:
- id: black

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<one line to give the program's name and a brief idea of what it does.>
Copyright (C) 2022 Andre Gunther
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) 2022 Andre Gunther
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

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README.md Normal file
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# Parameterisation Tuner
This project provides utils for producing magic parameters used by the pattern recognition algorithms in the Rec project. Typical parameters are coefficients for extrapolation polynomials and weights for TMVA methods.
## Setup
There's a bash script for setting up the necessary (python) environment. Simply do:
```
chmod +x setup.sh
./setup.sh
```
This will install dependencies like ROOT and Jupyter. To enter the environment do:
```
source env/tuner_env/bin/activate
conda activate tuner
```

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electron_main.py Normal file
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# flake8: noqaq
import os
import subprocess
import argparse
from parameterisations.parameterise_magnet_kink import parameterise_magnet_kink
from parameterisations.parameterise_track_model import parameterise_track_model
from parameterisations.parameterise_search_window import parameterise_search_window
from parameterisations.parameterise_field_integral import parameterise_field_integral
from parameterisations.parameterise_hough_histogram import parameterise_hough_histogram
from parameterisations.utils.preselection import preselection
from parameterisations.train_forward_ghost_mlps import (
train_default_forward_ghost_mlp,
train_veloUT_forward_ghost_mlp,
)
from parameterisations.residual_train_matching_ghost_mlps_electron import (
res_train_matching_ghost_mlp,
)
from parameterisations.train_matching_ghost_mlps_electron import (
train_matching_ghost_mlp,
)
from parameterisations.utils.parse_tmva_matrix_to_array_electron import (
parse_tmva_matrix_to_array,
)
parser = argparse.ArgumentParser()
parser.add_argument(
"--field-params",
action="store_true",
help="Enables determination of magnetic field parameterisations.",
)
parser.add_argument(
"--forward-weights",
action="store_true",
help="Enables determination of weights used by neural networks.",
)
parser.add_argument(
"--matching-weights",
action="store_true",
default=True,
help="Enables determination of weights used by neural networks.",
)
parser.add_argument(
"-r",
"--residuals",
action="store_true",
help="Trains neural network with residual tracks.",
)
parser.add_argument(
"-p",
"--prepare",
action="store_true",
default=True,
help="Enables preparation of data for matching.",
)
parser.add_argument(
"--prepare-params-data",
action="store_true",
help="Enables preparation of data for magnetic field parameterisations.",
)
parser.add_argument(
"--prepare-weights-data",
action="store_true",
help="Enables preparation of data for NN weight determination.",
)
args = parser.parse_args()
selected = "nn_electron_training/data/param_data_selected.root"
if args.prepare_params_data:
selection = "chi2_comb < 5 && pt > 10 && p > 1500 && p < 100000 && pid != 11"
print("Run selection cuts =", selection)
selected_md = preselection(
cuts=selection,
input_file="data/param_data_MD.root",
)
selected_mu = preselection(
cuts=selection,
input_file="data/param_data_MU.root",
)
merge_cmd = ["hadd", "-fk", selected, selected_md, selected_mu]
print("Concatenate polarities ...")
subprocess.run(merge_cmd, check=True)
cpp_files = []
if args.field_params:
print("Parameterise magnet kink position ...")
cpp_files.append(parameterise_magnet_kink(input_file=selected))
print("Parameterise track model ...")
cpp_files.append(parameterise_track_model(input_file=selected))
selected_all_p = "nn_electron_training/data/param_data_selected_all_p.root"
if args.prepare_params_data:
selection_all_momenta = "chi2_comb < 5 && pid != 11"
print()
print("Run selection cuts =", selection_all_momenta)
selected_md_all_p = preselection(
cuts=selection_all_momenta,
outfile_postfix="selected_all_p",
input_file="data/param_data_MD.root",
)
selected_mu_all_p = preselection(
cuts=selection_all_momenta,
outfile_postfix="selected_all_p",
input_file="data/param_data_MU.root",
)
merge_cmd = ["hadd", "-fk", selected_all_p, selected_md_all_p, selected_mu_all_p]
print("Concatenate polarities ...")
subprocess.run(merge_cmd, check=True)
if args.field_params:
print("Parameterise search window ...")
cpp_files.append(parameterise_search_window(input_file=selected_all_p))
print("Parameterise magnetic field integral ...")
cpp_files.append(parameterise_field_integral(input_file=selected_all_p))
print("Parameterise Hough histogram binning ...")
cpp_files.append(parameterise_hough_histogram(input_file=selected_all_p))
###>>>
ghost_data = "data/ghost_data.root"
if args.prepare_weights_data:
merge_cmd = [
"hadd",
"-fk",
ghost_data,
"data/ghost_data_B.root",
"data/ghost_data_D.root",
]
print("Concatenate decays for neural network training ...")
subprocess.run(merge_cmd, check=True)
###<<<
if args.forward_weights:
train_default_forward_ghost_mlp(prepare_data=args.prepare_weights_data)
# FIXME: use env variable instead
os.chdir(os.path.dirname(os.path.realpath(__file__)))
train_veloUT_forward_ghost_mlp(prepare_data=args.prepare_weights_data)
# this ensures that the directory is correct
os.chdir(os.path.dirname(os.path.realpath(__file__)))
cpp_files += parse_tmva_matrix_to_array(
[
"nn_electron_training/result/GhostNNDataSet/weights/TMVAClassification_default_forward_ghost_mlp.class.C",
"nn_electron_training/result/GhostNNDataSet/weights/TMVAClassification_veloUT_forward_ghost_mlp.class.C",
],
)
###
###>>>
###
if args.matching_weights and args.residuals:
os.chdir(os.path.dirname(os.path.realpath(__file__)))
res_train_matching_ghost_mlp(
prepare_data=args.prepare,
input_file="data/ghost_data_B.root",
tree_name="PrMatchNN_3e224c41.PrMCDebugMatchToolNN/MVAInputAndOutput", # e6feac0d, B: 3e224c41, B res: 1e13cc7e, D: 8cb154ca
exclude_electrons=False,
only_electrons=True,
residuals="PrMatchNN_1e13cc7e.PrMCDebugMatchToolNN/MVAInputAndOutput",
outdir="nn_electron_training",
n_train_signal=0,
n_train_bkg=20e3,
n_test_signal=1e3,
n_test_bkg=5e3,
)
# this ensures that the directory is correct
os.chdir(os.path.dirname(os.path.realpath(__file__)))
cpp_files += parse_tmva_matrix_to_array(
[
"nn_electron_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C",
],
simd_type=True,
)
if args.matching_weights and not args.residuals:
os.chdir(os.path.dirname(os.path.realpath(__file__)))
train_matching_ghost_mlp(
prepare_data=args.prepare,
input_file="data/ghost_data_B_new_vars_default_weights.root",
tree_name="PrMatchNN_3e224c41.PrMCDebugMatchToolNN/MVAInputAndOutput", # e6feac0d, B: 3e224c41, B res: 1e13cc7e, D: 8cb154ca
exclude_electrons=False,
only_electrons=True,
outdir="nn_electron_training",
n_train_signal=100e3,
n_train_bkg=200e3,
n_test_signal=20e3,
n_test_bkg=40e3,
)
# this ensures that the directory is correct
os.chdir(os.path.dirname(os.path.realpath(__file__)))
cpp_files += parse_tmva_matrix_to_array(
[
"nn_electron_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C",
],
simd_type=True,
)
###
###<<<
###
for file in cpp_files:
subprocess.run(
[
"clang-format",
"-i",
f"{file}",
],
)

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const auto fMin = std::array<simd::float_v, 6>{
{5.23340640939e-05, 1.25644373838e-06, 4.38690185547e-05, 1.90734863281e-06,
4.71249222755e-07, 1.02445483208e-08}};
const auto fMax = std::array<simd::float_v, 6>{{29.9998512268, 0.423314958811,
499.603149414, 499.198486328,
1.36927723885, 0.149392709136}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{0.929669659785881, -9.48043077362455, 10.1715051193127, 2.47667373712576,
-3.58083018257721, 6.45095796912324, 8.45870339869703},
{-0.296042356038334, -9.1896008367615, 5.56711502257143, 17.4486821475108,
-6.40008536792669, -6.6713822283154, 1.07455239812445},
{0.420413986806413, -1.15751488304315, 3.30243747788701, -1.36392382054269,
-0.847138226467055, 4.98479154537921, 4.24441164005755},
{1.5738915069293, -4.98081352303952, 5.8421155864956, -1.57711106103044,
-0.189458896895154, -3.65417561650535, -4.22419444699164},
{-6.66276674820396, 5.45480166931729, -8.03806088012418,
-0.789852234746539, -1.43435711944003, -4.01961155923308,
-14.0834092140066},
{0.817584255737394, 9.67890702465868, -1.76653199291165, -2.6610635109901,
2.51931906192722, -6.76406907184251, 0.968242938156462},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.88483370881801, 0.775741479584514, 0.214825824623319, 1.61128446188167,
1.00658692249476, 0.0826679173714486, -1.12220164589225}}};
const auto fWeightMatrix1to2 = std::array<simd::float_v, 9>{
{-1.73457594569937, -1.67600294506992, 1.88966364345853, 1.18946138791835,
2.47648351789816, -1.24466771533151, -0.315569517202675, 0.530105674163753,
3.05297057699491}};

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const auto fMin = std::array<simd::float_v, 6>{
{5.23340640939e-05, 1.25644373838e-06, 4.38690185547e-05, 1.90734863281e-06,
4.71249222755e-07, 1.02445483208e-08}};
const auto fMax = std::array<simd::float_v, 6>{{29.9998512268, 0.423314958811,
499.603149414, 499.198486328,
1.36927723885, 0.149392709136}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 12>{
{{1.10989365682333, -0.400262341428031, 0.703648655520529,
-10.0488008412327, 2.24766437644792, -1.51561555364132,
-8.19659986380238},
{0.168629698489816, -0.235222573459749, -2.76479490713939,
4.70755796881564, 0.422213317504099, -2.15975111024372,
-0.0413862540873273},
{-1.20008716401126, 5.98181458205593, 1.97530107863487, 0.951399694875291,
3.21037292600378, -1.88398815839093, 6.00348890738369},
{1.59529309197505, 1.03059763992094, -1.28481350389235, 1.77750648317864,
1.66698562433363, 0.560549629043751, -0.646784291824832},
{-10.5582477166915, 1.83421764351223, -4.28308784555713, 2.73941897264262,
-1.09755306824252, -2.76940523423182, -13.1324718956297},
{-1.37726196850241, 1.6684137449588, 0.234563275112263, 0.889405325109031,
1.24137671714337, -0.240977390196439, -2.00650503697469},
{-0.0917280130282914, -6.60741151288151, 4.280141752342, 15.8869539382336,
-4.40078451860264, -11.63552941888, -2.23848664347195},
{1.72810153197739, 1.81133984072885, 1.53310134343984, 1.53430340675608,
-0.880657747996044, -1.01002428097867, 0.327772484279249},
{0.450749853210101, -10.427522498238, 10.1106981167422, 2.50275117049706,
-3.96268925724634, 7.80062171624392, 8.13617432588314},
{-0.899044020226273, 4.04967555584356, -0.184515937391125,
0.605936074234893, 2.11314319461295, 1.08529920345605, 5.198893026323},
{-4.62555398916988, 2.56629651777862, -5.19280819069721, 0.979353155613104,
0.362510005701342, -0.387373325452426, -4.51347844411621},
{0.43181068852013, -1.12870359395317, -5.59123177894442, -2.78683035529746,
-0.119944490657953, -4.22887938179223, -12.1803091805475}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 13>, 6>{
{{-1.67244648790854, 2.58776560386115, 1.05350530586878, 1.12701723441192,
0.309436118156294, -1.41275414644542, -2.14966008423622,
-0.311448006165657, 0.485736777594352, -2.55661662619223,
0.96538530983999, -3.26795296807062, 0.988977174263089},
{-0.381498099253961, -0.549200770730038, -0.893363207717135,
0.119028293459292, 5.13224785809454, 1.77747846865563, -1.7072596641081,
0.0171890434060519, -0.612613204335275, 1.49948177816202,
0.230169849172349, -0.177176079772119, 3.44507835207359},
{-1.20063578327457, 1.63342807940049, -2.53476436290309, -1.5305832886762,
-3.05946450928802, 0.360300407115462, 0.625027143539907,
-1.77680947527138, -0.585041547463601, -2.08759735767147,
0.925138221824412, -1.24854533226616, 2.0502994330023},
{-1.36610982082625, -1.68603095079278, 1.93369535731656, 2.38299921699452,
0.133785811268423, -0.941203171967918, 2.97186174778511, 1.15122509873234,
0.135596009829977, -0.62708569660126, -0.024554433907907,
-0.555962579400608, 0.581541394004209},
{0.349027399089585, 0.0804040832557828, -0.454499280002817,
-1.17318303808809, 0.292596492448844, 0.801032353759436,
0.760037949875418, 0.22815167017283, 0.315794043406641,
-0.969493545848479, -1.03825660899029, 1.94713626859943,
-2.1389717446658},
{-1.88715819596171, 0.277545438410592, -1.68976255449697,
-1.02675310905861, 0.226775035076775, -1.07682401936394,
-0.52218117899507, -1.8253408434363, -1.94344181953331,
-0.444301427484403, -0.343612121595328, -0.177028285618245,
-0.648349320508864}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.844891680754208, 0.967426474103726, 0.960945561425279,
-0.80019723500702, -0.545585546409515, 0.3310030293198,
-2.29115821922715}};

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const auto fMin = std::array<simd::float_v, 6>{
{5.23340640939e-05, 1.25644373838e-06, 4.38690185547e-05, 1.90734863281e-06,
4.71249222755e-07, 1.02445483208e-08}};
const auto fMax = std::array<simd::float_v, 6>{{29.9998512268, 0.423314958811,
499.603149414, 499.198486328,
1.36927723885, 0.149392709136}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{2.57491955820114, -0.0666351688682796, -3.61648923844569,
-8.75747553035328, 0.639385257730277, 10.5129455348455, 1.22080436670381},
{-0.839608322851951, -11.7978768103865, 7.24641653369801, 6.63011679030303,
-5.38496571510758, 8.30851076817151, 6.69929919816849},
{-0.17630967374162, -3.75940580347415, 2.79889282638498, 16.7594894489781,
-3.10824840396248, -8.80345141844331, 3.9130937162361},
{0.19092161829683, -3.60963385297311, 11.9569759734039, 2.10283509156704,
-2.39101707207304, -1.89478969715624, 7.1165950679585},
{-3.02014034882022, 2.24487587036827, -8.90770349935038, -4.05040202238185,
-1.36813505779681, 9.14630004607903, -3.34618937758505},
{0.459674275912345, 8.12262886393506, 0.45018729587823, -1.11787227534737,
2.8096254085019, -0.481877520480143, 7.78611195142966},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.8337752666274, 0.841018614520629, 0.272259015077869, 1.63031107650108,
0.987469718084883, 0.0999586200250234, -1.13752770875358}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-0.0482883134570299, 0.46403980819019, 2.73665245864103,
0.245936163361116, -0.472281505442891, 0.307317690224363,
1.63438201788813, -1.44341215808597, -0.706584289774802},
{-3.91297125261727, 0.681495111998297, -3.37155822025346,
-0.966831590652637, 2.65933759421044, -0.661174079209186,
-1.61531096417457, 0.0991696473049824, -4.51523108840722},
{0.273186686950073, 1.14087516410812, 0.653137998266985,
-0.158819017566112, 0.692268877136322, -8.04912219449925,
-0.825543426908553, -1.92132463640843, -2.47870678055356},
{0.180394111293318, -0.414717927339332, -1.44129610325848,
-1.86532392228702, -0.806791495297171, -1.73521704274739,
1.61348068527877, -1.66550797875971, -0.927403017991324},
{-0.790929106392951, -0.0886126272927867, 0.035682993929273,
-0.602424006939674, 0.334723143379322, 2.22416454606917,
-0.848898627795279, 0.743857937018801, -0.291005217785123},
{-0.681492967014666, -0.368602644948209, 1.52403393057559,
-1.06212309361209, 0.881062654352226, 0.690165878288055,
-1.52203810494393, 1.63217238068739, 2.76628946224152}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.692725959420674, 1.18375950895893, -1.13672009847538, 0.407788542121486,
-0.606866044733726, 0.927912329413981, -0.887231003739174}};

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const auto fMin = std::array<simd::float_v, 6>{
{5.23340640939e-05, 1.25644373838e-06, 4.38690185547e-05, 1.90734863281e-06,
4.71249222755e-07, 1.02445483208e-08}};
const auto fMax = std::array<simd::float_v, 6>{{29.9998512268, 0.423314958811,
499.603149414, 499.198486328,
1.36927723885, 0.149392709136}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{1.10509804069795, -1.06659111536073, -1.23304417235305, -9.91292225685574,
1.00704133279785, 9.53100159268659, -0.793174916006915},
{-0.776689382841375, -10.4158785961964, 8.69653776953056, 6.84445159227452,
-6.97657257253127, 6.63766574651487, 7.11596889066532},
{-0.381785768656306, -5.11642852466812, 3.59950933567307, 16.792587073888,
-3.83635033235741, -7.72761443893271, 3.58572441569503},
{1.04413334688141, -3.78312149763691, 9.83287128246016, 1.4778662654192,
-2.0766161850877, 1.08288357164774, 8.02887163423859},
{-3.94899781448378, 1.94391204753919, -8.65991195739853, -2.00834934461626,
-3.50457026010403, 4.99589301163709, -6.89137092011374},
{-1.29549202700169, 7.66739081183929, 0.281901363288286, -1.19821907042793,
2.92107740687058, 1.14948481762706, 7.31015879384667},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.76053743491788, 0.909858152169371, 0.323489900540112, 1.61941068281945,
0.92342774005317, 0.0522421825019989, -1.23071245493981}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-0.12420793824361, 0.0833900748795987, 3.10092151009265,
0.320087923870887, -0.582151496774453, 0.029470772407136,
1.63438201788813, -1.43480874379498, 1.55528937765131},
{-2.8947400885087, 0.336449303963403, -2.30774902952597, -2.03456375507124,
2.29485822558142, 0.145499754959071, -1.61531096417457,
0.0991522125616504, -6.41616204842718},
{1.75720706890165, 1.241283421626, 0.607968086927335, -0.816112122281315,
0.0294391273974063, -7.94349478092389, -0.825543426908553,
-1.917979348207, -2.03720925778068},
{0.276286323447054, -0.393087539092168, -1.44329478350452,
-1.86277301712902, -0.807222527397035, -1.7239133524486, 1.61348068527877,
-1.66550797875971, -0.966703432130041},
{-1.20952640410995, -1.34067444254507, 0.079870798547199,
-0.0280804827435552, 0.15103668191983, 2.28974121850533,
-0.848898627795279, 0.747603604163112, 0.000485431747120298},
{-0.806478361586365, -0.902043622848205, 2.75668355569402,
-0.636341321727925, 0.189229471295501, 1.41597159860703,
-1.52203810494393, 1.62460924160209, 1.94946724799691}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-1.05665248808677, 1.16854173340171, -0.924262662063758, 0.441514927697916,
-0.908730180794495, 1.02616776486021, 1.26618724664255}};

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electron_training/result_B_old/matching.hpp Normal file
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const auto fMin = std::array<simd::float_v, 6>{
{2.32376150961e-05, 1.07555320028e-06, 1.33514404297e-05, 3.0517578125e-05,
3.99723649025e-06, 4.65661287308e-09}};
const auto fMax = std::array<simd::float_v, 6>{{29.9999885559, 0.509573578835,
498.591552734, 499.918823242,
1.35891008377, 0.149692088366}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{1.09689919364338, -2.36337032185014, -3.02921316084911, -8.60965194111848,
1.07308849187722, 11.2080534568785, -0.962205787111116},
{0.742505004354826, -11.5991419169641, 4.4706991652213, 12.0524034815861,
-7.39781510361567, -0.213355059289303, 2.43301548168847},
{-0.725034235213988, -4.7645569874331, 3.41021029475148, 18.2505819659489,
-2.28931892322383, -6.70009514891697, 7.19788851418639},
{-0.43322070581816, -6.76514244197456, 13.847487618501, 5.02461005105822,
-3.37683447138325, 0.858009838318498, 10.273453699814},
{-5.70448188875026, 5.26491831063117, -11.5555643412233, 3.1883356042284,
-2.133677285889, -2.24006224305986, -8.63987163868301},
{-1.06391634270892, 9.01667090199045, -1.28516566899228, -3.82841187857546,
3.18471029451158, 3.67902076971672, 7.29632098310751},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.68628281779244, 0.945006908224918, 0.536427352104393, 1.40667951887796,
0.832049300778026, -0.1089543073399, -1.43675125780786}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{1.21826975700184, 0.363147471400374, 2.05773388885566, 0.540313557549193,
0.420913357653504, -2.44884689863901, 1.63438201788813, -1.40944934303207,
-2.32871515871553},
{-2.90439321892223, 0.719436509209912, -3.93523150198893,
-1.07342541319164, 2.07689989754924, -2.39788444100381, -1.61531096417457,
0.0991887634515266, -8.04764734753152},
{-2.98923948872248, 2.26253234310036, -0.220642963100105,
-0.279316661053141, 0.0331794243552215, -5.88142829451649,
-0.825543426908553, -1.92002983781799, -8.21361341703474},
{0.662904361912077, -0.885584946591213, -1.45517778095535,
-1.89901295762029, -0.806428733926438, -1.81021900435817,
1.61348068527877, -1.66550797875971, -1.51013848461449},
{-1.37437469030598, -2.21755157129085, 1.33360411388341,
-0.0320979297776227, 0.290980167206705, 2.38901360605064,
-0.848898627795279, 0.766669058008792, 0.257937241570605},
{-1.09504185612819, -0.458703315043996, 1.03883522785983,
-1.05014637612802, 0.806301762243297, 2.21317466894066, -1.52203810494393,
1.5559212254549, 3.4514658408796}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.533136674890316, 1.09880505630834, -0.802064163473207, 1.60699693080913,
-0.951610170601364, 0.802378806704215, -1.20342886768673}};

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electron_training/result_B_original_weights_residuals/matching.hpp Normal file
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const auto fMin = std::array<simd::float_v, 6>{
{0.000315562472679, 1.14277577268e-06, 0.000274658203125, 0.000102996826172,
1.25970691442e-05, 4.93600964546e-08}};
const auto fMax =
std::array<simd::float_v, 6>{{29.9998435974, 0.431377887726, 490.802429199,
497.135681152, 1.3582059145, 0.147097736597}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{0.925466511495192, -3.04696651693941, 1.48703910101059, -2.74358886415853,
-1.54108875912546, 5.95181992279351, 1.09520524639045},
{0.225076264550494, -5.30106552386163, 6.29832156309508, 7.0574368868963,
-3.96436697758889, -0.110687606346842, 4.65556823990769},
{0.859449093477803, -1.42364618189426, 1.52973494084549, 1.63204418679045,
0.402800627021359, 2.02973355392681, 1.61963813362595},
{0.797017653476011, 1.80207629996926, 1.98407671947614, -4.84738045778757,
-0.237330392456841, 0.555272132234374, -0.334695720441674},
{-0.0089249002524941, -0.0721593078491391, -4.03962401066098,
-0.741196499782838, -0.520561836165389, -2.43469377130746,
-5.05370729239864},
{-0.324849815552061, 0.571642144152413, -2.26163157259376,
-3.96363877139044, 3.80954499156217, 0.812071601189534,
-0.388923872459538},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.90248604788051, 0.75183501464588, 0.163545686727045, 1.62950884794052,
1.04315466999792, 0.0204618414445436, -1.06300958722802}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-3.75265433194304, -4.03077590921609, 0.560952725012946, 1.59903822739795,
2.24144673906438, 0.377410282165578, 1.63438201788813, -1.47007146768882,
-2.58605490949786},
{-2.60068359938433, -0.0309587124576335, -0.841839408810281,
-0.377592041175285, 0.266402105492061, -1.93675266037507,
-1.61531096417457, 0.0988426038328682, 1.51287715061537},
{-0.257103489627825, 2.38563057831866, -2.06682010253696,
-1.50490219717468, 0.990281758525445, -2.89728072212192,
-0.825543426908553, -1.91692155046286, -0.469424293810405},
{0.680066470317009, -0.353277604226862, -1.4315209802379,
-1.86345277642716, -0.806051898385157, -1.70690619012381,
1.61348068527877, -1.66550797875971, -0.804637203024864},
{0.292121929684041, 1.67922513643505, -0.2207750830665, -1.85432148737195,
-0.761761120528791, 0.148603794427115, -0.848898627795279,
0.776926680814688, 0.515413675465116},
{0.436091761386387, -1.32454758986161, 1.0014013582506, -0.251981066947133,
-0.176482975086784, -0.862489698728272, -1.52203810494393,
1.59929932442845, 0.257302379473767}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-3.33863999066044, 1.86100137130551, -0.825426355935299, 0.455646208323886,
-0.730374866334297, 1.65923402820956, 1.60305190554767}};

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electron_training/result_B_original_weights_residuals/og_weights.txt Normal file
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signal: only electrons that are true match but mlp response "no match"
background: all ghost tracks

47
electron_training/result_D/matching.hpp Normal file
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const auto fMin = std::array<simd::float_v, 6>{
{1.4334048501e-05, 1.21005723486e-06, 0.0001220703125, 4.57763671875e-05,
6.51180744171e-06, 5.58793544769e-09}};
const auto fMax = std::array<simd::float_v, 6>{{29.9999580383, 0.388462841511,
497.0078125, 499.509338379,
1.34583222866, 0.148980647326}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{1.80376907529412, -3.94522006510378, 0.731592325680377, -11.2008566094574,
0.679894249961926, 3.84839657473663, -8.07142111719402},
{-2.07030049796095, 3.26515474867444, 0.320697671285593,
-0.564334829758113, 3.06552235842096, 2.6605948885273, 4.6955026167446},
{0.896838903613344, -1.89786210758941, 6.19791014227951, 13.9534201571522,
-9.67617750026742, -5.13735275462149, 5.25604022070252},
{0.350723542258884, -5.46236108010248, 11.3961275449655, 6.86860356458193,
-4.10979098519269, 7.40355727094559, 17.4195097954786},
{-4.98089321802183, -3.06924425120168, -4.16533013325382,
-1.76525268144874, -0.574266009689755, 1.38792795938214,
-11.9738574538811},
{-1.3792553381734, 6.6360108241851, 1.58470490969407, 1.2116201192747,
3.35950082512036, -2.69400720014141, 5.78773380456927},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.89618444435453, 0.756762886971226, 0.224607628956107, 1.58224418687104,
1.01198188838621, 0.0609287072816972, -1.10149714422957}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-1.95030391768864, -2.57241200745428, 2.22513961422549, 1.09636924630915,
-1.41670442059213, 0.268826912887813, 1.63438201788813, -1.41788409846508,
-0.529591299831198},
{-1.7671292981577, -0.123369349217712, -0.866977288876212,
-0.90631173560288, 2.47901931013162, -2.21695976800688, -1.61531096417457,
0.0991885303988918, 1.28955047096799},
{-0.534089470091784, 1.26461913447419, -0.403013723511741,
-0.758910423086654, -0.92644473334079, -1.52818746990179,
-0.825543426908553, -1.92721239362346, 3.26105888866326},
{0.198023173442802, -0.564145037694825, -1.43289188975828,
-1.86352960767302, -0.808447979295006, -1.71330811211152,
1.61348068527877, -1.66550797875971, -0.910801619527772},
{-1.8227621068219, -0.698025453596766, 0.233245541019916,
-0.387306932182454, -1.05004029412037, -0.333220104163044,
-0.848898627795279, 0.782822716993409, -0.262552968051654},
{-0.314822731076892, -1.50428335429844, 0.179689344301775,
-0.325249075131384, -0.635962383213103, -0.491817587388958,
-1.52203810494393, 1.59283237353393, -0.153430533462156}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.572477318545036, 1.12222369845671, 0.962011482756679, 0.427739156517669,
-1.31864386562843, 1.61500835143017, -1.96827876292426}};

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electron_training/result_D_old/matching.hpp Normal file
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const auto fMin = std::array<simd::float_v, 6>{
{1.4334048501e-05, 1.20995343877e-06, 0.000255584716797, 7.62939453125e-05,
1.95447355509e-05, 9.31322574615e-09}};
const auto fMax = std::array<simd::float_v, 6>{{29.9999771118, 0.480875372887,
497.208251953, 499.789672852,
1.33854484558, 0.149920448661}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{-2.53490305244316, -3.87226566727903, -4.46377668521249, 3.76593347190621,
-0.736147925273483, 9.02585614570327, 3.6237871734026},
{0.549076542016505, -6.76487588278314, 12.7146884747338, 6.35190577761167,
-4.4556955709276, 8.26540577622736, 18.015709088484},
{-0.456124601513526, -3.87828385698417, 6.13829079533624, 20.024096872054,
-10.2437102287476, -13.5453994008865, 1.47951238998312},
{0.81464719007702, -5.60573514166659, 7.32263078411251, -5.0446935011349,
-0.701597395356833, 6.4873077480756, 1.05029191775837},
{-5.20529068292815, -1.29341688678248, -12.9211101623102, 4.06192896781978,
-2.24819687530499, -4.47649653096685, -18.7962996196447},
{-1.92319074705205, 9.13989051160526, 1.4372857889395, 3.71255897752862,
2.12080932540223, 0.775519813919651, 13.1780255071529},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{2.48032981707208, 1.99122468030675, 0.147128688791476, 3.20653226030149,
-1.59262641780577, 1.63473498915926, -0.983318163281607}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-1.23665952123739, 1.12167249551526, 0.882383882593374,
-0.923566479348657, 0.0338835472680554, 1.00667491483647,
1.63438201788813, -4.23394622989637, -0.990743251230407},
{-1.17099975681859, -0.0097715608108182, -0.404796049870115,
0.85424890495973, 3.98024762276004, -0.145761966096377, -1.61531096417457,
0.0969352107124514, -5.51833020463238},
{2.4380083430657, 2.00418225228056, -0.792776990439125, -2.80847623446533,
-0.137631196353825, -7.80633161173606, -0.825543426908553,
-2.37988842437401, -6.27310694945946},
{0.225004410426863, -0.39049833080813, -1.43244348842572,
-1.86390252348813, -0.816126277640337, -1.7092593780967, 1.61348068527877,
-1.66550797875971, -0.984918093686436},
{-0.238610540704029, -0.352025987714205, 1.91071878135911,
1.06903719015662, -0.0879085084653824, -5.01732651510019,
-0.848898627795279, 0.301171033553613, -3.31246000228067},
{1.06098527822605, -1.02241211107063, -0.727572693909408,
-0.164960522101848, 0.834295993060446, -0.816864575663688,
-1.52203810494393, 1.63565593112606, 0.829470327557413}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.547016315428893, 0.794153417512957, -0.534810951888594,
0.411807752872488, -1.06003774378693, 0.679060736847631,
0.119300486730084}};

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electron_training/result_reg_B/matching.hpp Normal file
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const auto fMin = std::array<simd::float_v, 6>{
{5.23340640939e-05, 1.25644373838e-06, 4.38690185547e-05, 1.90734863281e-06,
4.71249222755e-07, 1.02445483208e-08}};
const auto fMax = std::array<simd::float_v, 6>{{29.9998512268, 0.423314958811,
499.603149414, 499.198486328,
1.36927723885, 0.149392709136}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{2.57491955820114, -0.0666351688682796, -3.61648923844569,
-8.75747553035328, 0.639385257730277, 10.5129455348455, 1.22080436670381},
{-0.839608322851951, -11.7978768103865, 7.24641653369801, 6.63011679030303,
-5.38496571510758, 8.30851076817151, 6.69929919816849},
{-0.17630967374162, -3.75940580347415, 2.79889282638498, 16.7594894489781,
-3.10824840396248, -8.80345141844331, 3.9130937162361},
{0.19092161829683, -3.60963385297311, 11.9569759734039, 2.10283509156704,
-2.39101707207304, -1.89478969715624, 7.1165950679585},
{-3.02014034882022, 2.24487587036827, -8.90770349935038, -4.05040202238185,
-1.36813505779681, 9.14630004607903, -3.34618937758505},
{0.459674275912345, 8.12262886393506, 0.45018729587823, -1.11787227534737,
2.8096254085019, -0.481877520480143, 7.78611195142966},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.8337752666274, 0.841018614520629, 0.272259015077869, 1.63031107650108,
0.987469718084883, 0.0999586200250234, -1.13752770875358}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-0.0482883134570299, 0.46403980819019, 2.73665245864103,
0.245936163361116, -0.472281505442891, 0.307317690224363,
1.63438201788813, -1.44341215808597, -0.706584289774802},
{-3.91297125261727, 0.681495111998297, -3.37155822025346,
-0.966831590652637, 2.65933759421044, -0.661174079209186,
-1.61531096417457, 0.0991696473049824, -4.51523108840722},
{0.273186686950073, 1.14087516410812, 0.653137998266985,
-0.158819017566112, 0.692268877136322, -8.04912219449925,
-0.825543426908553, -1.92132463640843, -2.47870678055356},
{0.180394111293318, -0.414717927339332, -1.44129610325848,
-1.86532392228702, -0.806791495297171, -1.73521704274739,
1.61348068527877, -1.66550797875971, -0.927403017991324},
{-0.790929106392951, -0.0886126272927867, 0.035682993929273,
-0.602424006939674, 0.334723143379322, 2.22416454606917,
-0.848898627795279, 0.743857937018801, -0.291005217785123},
{-0.681492967014666, -0.368602644948209, 1.52403393057559,
-1.06212309361209, 0.881062654352226, 0.690165878288055,
-1.52203810494393, 1.63217238068739, 2.76628946224152}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.692725959420674, 1.18375950895893, -1.13672009847538, 0.407788542121486,
-0.606866044733726, 0.927912329413981, -0.887231003739174}};

20
env/environment.yaml vendored Normal file
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name: tuner
channels:
- conda-forge
- defaults
dependencies:
- python=3.10
- root
- pre-commit
- jupyter
- black=22.8.0
- flake8=5.0.4
- clang-format
- matplotlib
- uproot
- awkward
- pandas
- numpy
- scikit-learn
- mplhep
- seaborn

151
main.py Normal file
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# flake8: noqaq
import os
import subprocess
import argparse
from parameterisations.parameterise_magnet_kink import parameterise_magnet_kink
from parameterisations.parameterise_track_model import parameterise_track_model
from parameterisations.parameterise_search_window import parameterise_search_window
from parameterisations.parameterise_field_integral import parameterise_field_integral
from parameterisations.parameterise_hough_histogram import parameterise_hough_histogram
from parameterisations.utils.preselection import preselection
from parameterisations.train_forward_ghost_mlps import (
train_default_forward_ghost_mlp,
train_veloUT_forward_ghost_mlp,
)
from parameterisations.train_matching_ghost_mlps import train_matching_ghost_mlp
from parameterisations.utils.parse_tmva_matrix_to_array import (
parse_tmva_matrix_to_array,
)
parser = argparse.ArgumentParser()
parser.add_argument(
"--field-params",
action="store_true",
help="Enables determination of magnetic field parameterisations.",
)
parser.add_argument(
"--forward-weights",
action="store_true",
help="Enables determination of weights used by neural networks.",
)
parser.add_argument(
"--matching-weights",
action="store_true",
default=True,
help="Enables determination of weights used by neural networks.",
)
parser.add_argument(
"--prepare-params-data",
action="store_true",
help="Enables preparation of data for magnetic field parameterisations.",
)
parser.add_argument(
"--prepare-weights-data",
action="store_true",
help="Enables preparation of data for NN weight determination.",
)
args = parser.parse_args()
selected = "neural_net_training/data/param_data_selected.root"
if args.prepare_params_data:
selection = "chi2_comb < 5 && pt > 10 && p > 1500 && p < 100000 && pid != 11"
print("Run selection cuts =", selection)
selected_md = preselection(
cuts=selection,
input_file="data/param_data_MD.root",
)
selected_mu = preselection(
cuts=selection,
input_file="data/param_data_MU.root",
)
merge_cmd = ["hadd", "-fk", selected, selected_md, selected_mu]
print("Concatenate polarities ...")
subprocess.run(merge_cmd, check=True)
cpp_files = []
if args.field_params:
print("Parameterise magnet kink position ...")
cpp_files.append(parameterise_magnet_kink(input_file=selected))
print("Parameterise track model ...")
cpp_files.append(parameterise_track_model(input_file=selected))
selected_all_p = "neural_net_training/data/param_data_selected_all_p.root"
if args.prepare_params_data:
selection_all_momenta = "chi2_comb < 5 && pid != 11"
print()
print("Run selection cuts =", selection_all_momenta)
selected_md_all_p = preselection(
cuts=selection_all_momenta,
outfile_postfix="selected_all_p",
input_file="data/param_data_MD.root",
)
selected_mu_all_p = preselection(
cuts=selection_all_momenta,
outfile_postfix="selected_all_p",
input_file="data/param_data_MU.root",
)
merge_cmd = ["hadd", "-fk", selected_all_p, selected_md_all_p, selected_mu_all_p]
print("Concatenate polarities ...")
subprocess.run(merge_cmd, check=True)
if args.field_params:
print("Parameterise search window ...")
cpp_files.append(parameterise_search_window(input_file=selected_all_p))
print("Parameterise magnetic field integral ...")
cpp_files.append(parameterise_field_integral(input_file=selected_all_p))
print("Parameterise Hough histogram binning ...")
cpp_files.append(parameterise_hough_histogram(input_file=selected_all_p))
###>>>
ghost_data = "neural_net_training/data/ghost_data.root"
if args.prepare_weights_data:
merge_cmd = [
"hadd",
"-fk",
ghost_data,
"data/ghost_data_MD.root",
"data/ghost_data_MU.root",
]
print("Concatenate polarities for neural network training ...")
subprocess.run(merge_cmd, check=True)
###<<<
if args.forward_weights:
train_default_forward_ghost_mlp(prepare_data=args.prepare_weights_data)
# FIXME: use env variable instead
os.chdir(os.path.dirname(os.path.realpath(__file__)))
train_veloUT_forward_ghost_mlp(prepare_data=args.prepare_weights_data)
# this ensures that the directory is correct
os.chdir(os.path.dirname(os.path.realpath(__file__)))
cpp_files += parse_tmva_matrix_to_array(
[
"neural_net_training/result/GhostNNDataSet/weights/TMVAClassification_default_forward_ghost_mlp.class.C",
"neural_net_training/result/GhostNNDataSet/weights/TMVAClassification_veloUT_forward_ghost_mlp.class.C",
],
)
###>>>
if args.matching_weights:
os.chdir(os.path.dirname(os.path.realpath(__file__)))
train_matching_ghost_mlp(
prepare_data=True, # args.prepare_weights_data,
input_file="data/ghost_data_B.root",
tree_name="PrMatchNN_e6feac0d.PrMCDebugMatchToolNN/MVAInputAndOutput",
outdir="neural_net_training",
)
# this ensures that the directory is correct
os.chdir(os.path.dirname(os.path.realpath(__file__)))
cpp_files += parse_tmva_matrix_to_array(
[
"neural_net_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C",
],
simd_type=True,
)
###<<<
for file in cpp_files:
subprocess.run(
[
"clang-format",
"-i",
f"{file}",
],
)

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# flake8: noqaq
import os
import subprocess
import argparse
from parameterisations.parameterise_magnet_kink import parameterise_magnet_kink
from parameterisations.parameterise_track_model import parameterise_track_model
from parameterisations.parameterise_search_window import parameterise_search_window
from parameterisations.parameterise_field_integral import parameterise_field_integral
from parameterisations.parameterise_hough_histogram import parameterise_hough_histogram
from parameterisations.utils.preselection import preselection
from parameterisations.train_forward_ghost_mlps import (
train_default_forward_ghost_mlp,
train_veloUT_forward_ghost_mlp,
)
from parameterisations.losses_train_matching_ghost_mlps import (
train_matching_ghost_mlp,
)
from parameterisations.utils.parse_tmva_matrix_to_array_TrLo import (
parse_tmva_matrix_to_array,
)
parser = argparse.ArgumentParser()
parser.add_argument(
"--field-params",
action="store_true",
help="Enables determination of magnetic field parameterisations.",
)
parser.add_argument(
"--forward-weights",
action="store_true",
help="Enables determination of weights used by neural networks.",
)
parser.add_argument(
"--matching-weights",
action="store_true",
default=True,
help="Enables determination of weights used by neural networks.",
)
parser.add_argument(
"-p",
"--prepare",
action="store_true",
# default=True,
help="Enables preparation of data for matching.",
)
parser.add_argument(
"--prepare-params-data",
action="store_true",
help="Enables preparation of data for magnetic field parameterisations.",
)
parser.add_argument(
"--prepare-weights-data",
action="store_true",
help="Enables preparation of data for NN weight determination.",
)
args = parser.parse_args()
selected = "nn_electron_training/data/param_data_selected.root"
if args.prepare_params_data:
selection = "chi2_comb < 5 && pt > 10 && p > 1500 && p < 100000 && pid != 11"
print("Run selection cuts =", selection)
selected_md = preselection(
cuts=selection,
input_file="data/param_data_MD.root",
)
selected_mu = preselection(
cuts=selection,
input_file="data/param_data_MU.root",
)
merge_cmd = ["hadd", "-fk", selected, selected_md, selected_mu]
print("Concatenate polarities ...")
subprocess.run(merge_cmd, check=True)
cpp_files = []
if args.field_params:
print("Parameterise magnet kink position ...")
cpp_files.append(parameterise_magnet_kink(input_file=selected))
print("Parameterise track model ...")
cpp_files.append(parameterise_track_model(input_file=selected))
selected_all_p = "nn_electron_training/data/param_data_selected_all_p.root"
if args.prepare_params_data:
selection_all_momenta = "chi2_comb < 5 && pid != 11"
print()
print("Run selection cuts =", selection_all_momenta)
selected_md_all_p = preselection(
cuts=selection_all_momenta,
outfile_postfix="selected_all_p",
input_file="data/param_data_MD.root",
)
selected_mu_all_p = preselection(
cuts=selection_all_momenta,
outfile_postfix="selected_all_p",
input_file="data/param_data_MU.root",
)
merge_cmd = ["hadd", "-fk", selected_all_p, selected_md_all_p, selected_mu_all_p]
print("Concatenate polarities ...")
subprocess.run(merge_cmd, check=True)
if args.field_params:
print("Parameterise search window ...")
cpp_files.append(parameterise_search_window(input_file=selected_all_p))
print("Parameterise magnetic field integral ...")
cpp_files.append(parameterise_field_integral(input_file=selected_all_p))
print("Parameterise Hough histogram binning ...")
cpp_files.append(parameterise_hough_histogram(input_file=selected_all_p))
###>>>
ghost_data = "data/ghost_data.root"
if args.prepare_weights_data:
merge_cmd = [
"hadd",
"-fk",
ghost_data,
"data/ghost_data_B.root",
"data/ghost_data_D.root",
]
print("Concatenate decays for neural network training ...")
subprocess.run(merge_cmd, check=True)
###<<<
if args.forward_weights:
train_default_forward_ghost_mlp(prepare_data=args.prepare_weights_data)
# FIXME: use env variable instead
os.chdir(os.path.dirname(os.path.realpath(__file__)))
train_veloUT_forward_ghost_mlp(prepare_data=args.prepare_weights_data)
# this ensures that the directory is correct
os.chdir(os.path.dirname(os.path.realpath(__file__)))
cpp_files += parse_tmva_matrix_to_array(
[
"nn_trackinglosses_training/result/GhostNNDataSet/weights/TMVAClassification_default_forward_ghost_mlp.class.C",
"nn_trackinglosses_training/result/GhostNNDataSet/weights/TMVAClassification_veloUT_forward_ghost_mlp.class.C",
],
)
###
###>>>
###
if args.matching_weights:
os.chdir(os.path.dirname(os.path.realpath(__file__)))
train_matching_ghost_mlp(
prepare_data=args.prepare,
input_file="data/tracking_losses_ntuple_B.root",
tree_name="PrDebugTrackingLosses.PrDebugTrackingTool/Tuple", # e6feac0d, B: 3e224c41, B res: 1e13cc7e, D: 8cb154ca
b_input_file="data/ghost_data_B.root",
b_tree_name="PrMatchNN_3e224c41.PrMCDebugMatchToolNN/MVAInputAndOutput",
only_electrons=True,
outdir="nn_trackinglosses_training",
n_train_signal=20e3,
n_train_bkg=40e3,
n_test_signal=5e3,
n_test_bkg=10e3,
)
# this ensures that the directory is correct
os.chdir(os.path.dirname(os.path.realpath(__file__)))
cpp_files += parse_tmva_matrix_to_array(
[
"nn_trackinglosses_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C",
],
simd_type=True,
)
###
###<<<
###
for file in cpp_files:
subprocess.run(
[
"clang-format",
"-i",
f"{file}",
],
)

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moore_options/Bak_get_ghost_data.py Normal file
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from Moore import options, run_reconstruction
from Moore.config import Reconstruction
from PRConfig.TestFileDB import test_file_db
from PyConf.Algorithms import (
PrForwardTrackingVelo,
PrForwardTracking,
PrTrackAssociator,
PrMatchNN,
)
from PyConf.application import make_data_with_FetchDataFromFile
from PyConf.Tools import PrMCDebugForwardTool, PrMCDebugMatchToolNN
from RecoConf.data_from_file import mc_unpackers
from RecoConf.hlt1_tracking import make_hlt1_tracks, make_PrStoreSciFiHits_hits
from RecoConf.hlt2_tracking import get_global_ut_hits_tool, make_PrHybridSeeding_tracks
from RecoConf.mc_checking import make_links_lhcbids_mcparticles_tracking_system
options.evt_max = -1
n_files_per_cat = 1
polarity = "MU"
options.ntuple_file = f"data/ghost_data_{polarity}.root"
input_files = (
(
test_file_db["upgrade_DC19_01_Bs2JPsiPhi_MD"].filenames[:n_files_per_cat]
if polarity == "MD"
else test_file_db["upgrade_DC19_01_Bs2JpsiPhiMU"].filenames[:n_files_per_cat]
)
+ test_file_db[f"upgrade_DC19_01_Bs2PhiPhi{polarity}"].filenames[:n_files_per_cat]
+ test_file_db[f"upgrade_DC19_01_Z2mumu{polarity}"].filenames[:n_files_per_cat]
+ test_file_db[f"upgrade_DC19_01_Bd2Dstmumu{polarity}"].filenames[:n_files_per_cat]
+ test_file_db[f"upgrade_DC19_01_Dst2D0pi{polarity}"].filenames[:n_files_per_cat]
+ test_file_db[f"upgrade_DC19_01_Bd2Kstee{polarity}"].filenames[:n_files_per_cat]
+ test_file_db[f"upgrade_DC19_01_Dp2KSPip_{polarity}"].filenames[:n_files_per_cat]
)
options.input_files = input_files
options.input_type = "ROOT"
options.set_conds_from_testfiledb(f"upgrade_DC19_01_Dst2D0pi{polarity}")
def run_tracking_debug():
links_to_hits = make_links_lhcbids_mcparticles_tracking_system()
hlt1_tracks = make_hlt1_tracks()
seed_tracks = make_PrHybridSeeding_tracks()
# add MCLinking to the (fitted) V1 tracks
links_to_velo_tracks = PrTrackAssociator(
SingleContainer=hlt1_tracks["Velo"]["v1"],
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
links_to_upstream_tracks = PrTrackAssociator(
SingleContainer=hlt1_tracks["Upstream"]["v1"],
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
links_to_seed_tracks = PrTrackAssociator(
SingleContainer=seed_tracks["v1"],
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
# be more robust against imperfect data
loose_forward_params = dict(
MaxChi2PerDoF=16,
MaxChi2XProjection=30,
MaxChi2PerDoFFinal=8,
MaxChi2Stereo=8,
MaxChi2StereoAdd=8,
)
forward_debug = PrForwardTrackingVelo(
InputTracks=hlt1_tracks["Velo"]["Pr"],
SciFiHits=make_PrStoreSciFiHits_hits(),
AddUTHitsToolName=get_global_ut_hits_tool(enable=True),
DebugTool=PrMCDebugForwardTool(
InputTracks=hlt1_tracks["Velo"]["v1"],
InputTrackLinks=links_to_velo_tracks,
MCParticles=mc_unpackers()["MCParticles"],
SciFiHitLinks=links_to_hits,
SciFiHits=make_PrStoreSciFiHits_hits(),
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
),
**loose_forward_params,
)
forward_ut_debug = PrForwardTracking(
SciFiHits=make_PrStoreSciFiHits_hits(),
InputTracks=hlt1_tracks["Upstream"]["Pr"],
AddUTHitsToolName=get_global_ut_hits_tool(enable=True),
DebugTool=PrMCDebugForwardTool(
InputTracks=hlt1_tracks["Upstream"]["v1"],
InputTrackLinks=links_to_upstream_tracks,
MCParticles=mc_unpackers()["MCParticles"],
SciFiHitLinks=links_to_hits,
SciFiHits=make_PrStoreSciFiHits_hits(),
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
),
**loose_forward_params,
)
loose_matching_params = dict(MaxMatchChi2=30.0, MaxDistX=500, MaxDistY=500)
match_debug = PrMatchNN(
VeloInput=hlt1_tracks["Velo"]["Pr"],
SeedInput=seed_tracks["Pr"],
MatchDebugToolName=PrMCDebugMatchToolNN(
VeloTracks=hlt1_tracks["Velo"]["v1"],
SeedTracks=seed_tracks["v1"],
VeloTrackLinks=links_to_velo_tracks,
SeedTrackLinks=links_to_seed_tracks,
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
MCParticles=mc_unpackers()["MCParticles"],
),
AddUTHitsToolName=get_global_ut_hits_tool(enable=True),
**loose_matching_params,
).MatchOutput
data = [forward_debug, forward_ut_debug, match_debug]
return Reconstruction("run_tracking_debug", data)
run_reconstruction(options, run_tracking_debug)

44
moore_options/Bak_get_parameterisation_data.py Normal file
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from Moore import options, run_reconstruction
from Moore.config import Reconstruction
from PRConfig.TestFileDB import test_file_db
from PyConf.Algorithms import PrParameterisationData
from RecoConf.data_from_file import mc_unpackers
from PyConf.application import make_data_with_FetchDataFromFile
options.evt_max = -1
n_files_per_cat = 1
polarity = "MU"
options.ntuple_file = f"data/param_data_{polarity}.root"
input_files = (
(
test_file_db["upgrade_DC19_01_Bs2JPsiPhi_MD"].filenames[:n_files_per_cat]
if polarity == "MD"
else test_file_db["upgrade_DC19_01_Bs2JpsiPhiMU"].filenames[:n_files_per_cat]
)
+ test_file_db[f"upgrade_DC19_01_Bs2PhiPhi{polarity}"].filenames[:n_files_per_cat]
+ test_file_db[f"upgrade_DC19_01_Z2mumu{polarity}"].filenames[:n_files_per_cat]
+ test_file_db[f"upgrade_DC19_01_Bd2Dstmumu{polarity}"].filenames[:n_files_per_cat]
+ test_file_db[f"upgrade_DC19_01_Dst2D0pi{polarity}"].filenames[:n_files_per_cat]
+ test_file_db[f"upgrade_DC19_01_Bd2Kstee{polarity}"].filenames[:n_files_per_cat]
+ test_file_db[f"upgrade_DC19_01_Dp2KSPip_{polarity}"].filenames[:n_files_per_cat]
)
options.input_files = input_files
options.input_type = "ROOT"
options.set_conds_from_testfiledb(f"upgrade_DC19_01_Dst2D0pi{polarity}")
def run_tracking_param_debug():
param_data = PrParameterisationData(
MCParticles=mc_unpackers()["MCParticles"],
MCVPHits=mc_unpackers()["MCVPHits"],
MCFTHits=mc_unpackers()["MCFTHits"],
zRef=8520.0,
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
)
data = [param_data]
return Reconstruction("run_tracking_debug", data)
run_reconstruction(options, run_tracking_param_debug)

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moore_options/Bak_get_resolution_and_eff_data.py Normal file
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"""
This set of options is used for reconstruction development purposes,
and assumes that the input contains MCHits (i.e. is of `Exended`
DST/digi type).
"""
# flake8: noqaq
from Moore import options, run_reconstruction
from Moore.config import Reconstruction
from PyConf.Algorithms import PrKalmanFilter
from PyConf.Tools import TrackMasterExtrapolator
from RecoConf.mc_checking import (
check_track_resolution,
check_tracking_efficiency,
get_mc_categories,
get_hit_type_mask,
make_links_lhcbids_mcparticles_tracking_system,
make_links_tracks_mcparticles,
)
from RecoConf.core_algorithms import make_unique_id_generator
from RecoConf.hlt2_tracking import make_hlt2_tracks
from RecoConf.hlt1_tracking import (
make_VeloClusterTrackingSIMD_hits,
make_PrStorePrUTHits_hits,
make_PrStoreSciFiHits_hits,
get_global_materiallocator,
)
# sample = "Bd2KstEE_MD"
# sample = "Bd2KstEE_MU"
# sample = "Bs2JpsiPhi_MD"
# sample = "Bs2JpsiPhi_MU"
sample = "Bs2PhiPhi_MD"
# sample = "Bs2PhiPhi_MU"
stack = ""
options.evt_max = 5000
options.first_evt = 0 if stack else 5000
options.ntuple_file = f"data/resolutions_and_effs_{sample}{stack}.root"
options.input_type = "ROOT"
options.set_input_and_conds_from_testfiledb(f"upgrade_sim10_Up08_Digi15_{sample}")
def run_tracking_resolution():
tracks = make_hlt2_tracks(light_reco=True, fast_reco=False, use_pr_kf=True)
fitted_forward_tracks = PrKalmanFilter(
Input=tracks["Forward"]["Pr"],
MaxChi2=2.8,
MaxChi2PreOutlierRemoval=20,
HitsVP=make_VeloClusterTrackingSIMD_hits(),
HitsUT=make_PrStorePrUTHits_hits(),
HitsFT=make_PrStoreSciFiHits_hits(),
ReferenceExtrapolator=TrackMasterExtrapolator(
MaterialLocator=get_global_materiallocator(),
),
InputUniqueIDGenerator=make_unique_id_generator(),
).OutputTracks
links_to_lhcbids = make_links_lhcbids_mcparticles_tracking_system()
links_to_forward = make_links_tracks_mcparticles(
InputTracks=tracks["Forward"],
LinksToLHCbIDs=links_to_lhcbids,
)
links_to_match = make_links_tracks_mcparticles(
InputTracks=tracks["Match"],
LinksToLHCbIDs=links_to_lhcbids,
)
links_to_best = make_links_tracks_mcparticles(
InputTracks=tracks["BestLong"],
LinksToLHCbIDs=links_to_lhcbids,
)
res_checker_forward = check_track_resolution(tracks["Forward"], suffix="Forward")
res_checker_best_long = check_track_resolution(
tracks["BestLong"],
suffix="BestLong",
)
res_checker_best_forward = check_track_resolution(
dict(v1=fitted_forward_tracks),
suffix="BestForward",
)
eff_checker_forward = check_tracking_efficiency(
"Forward",
tracks["Forward"],
links_to_forward,
links_to_lhcbids,
get_mc_categories("Forward"),
get_hit_type_mask("Forward"),
)
eff_checker_match = check_tracking_efficiency(
"Match",
tracks["Match"],
links_to_match,
links_to_lhcbids,
get_mc_categories("Match"),
get_hit_type_mask("Match"),
)
eff_checker_best_long = check_tracking_efficiency(
"BestLong",
tracks["BestLong"],
links_to_best,
links_to_lhcbids,
get_mc_categories("BestLong"),
get_hit_type_mask("BestLong"),
)
data = [
res_checker_forward,
res_checker_best_long,
res_checker_best_forward,
eff_checker_forward,
eff_checker_match,
eff_checker_best_long,
]
return Reconstruction("run_tracking_debug", data)
run_reconstruction(options, run_tracking_resolution)

122
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###############################################################################
# (c) Copyright 2023 CERN for the benefit of the LHCb Collaboration #
# #
# This software is distributed under the terms of the GNU General Public #
# Licence version 3 (GPL Version 3), copied verbatim in the file "COPYING". #
# #
# In applying this licence, CERN does not waive the privileges and immunities #
# granted to it by virtue of its status as an Intergovernmental Organization #
# or submit itself to any jurisdiction. #
###############################################################################
# flake8: noqa
from Moore import options, run_reconstruction
from Moore.config import Reconstruction
from RecoConf.data_from_file import mc_unpackers
from RecoConf.hlt1_tracking import make_VeloClusterTrackingSIMD_hits
from RecoConf.hlt2_tracking import (
make_hlt2_tracks,
make_PrKalmanFilter_tracks,
make_PrStorePrUTHits_hits,
make_PrStoreSciFiHits_hits,
)
from RecoConf.mc_checking import (
make_links_lhcbids_mcparticles_tracking_system,
make_links_tracks_mcparticles,
make_default_IdealStateCreator,
)
from PyConf.Algorithms import PrTrackAssociator, PrDebugTrackingLosses
from PyConf.application import make_data_with_FetchDataFromFile
import glob
decay = "B"
if decay == "B":
options.input_files = glob.glob("/auto/data/guenther/Bd_Kstee/*.xdigi")
elif decay == "D":
options.input_files = glob.glob("/auto/data/guenther/Dst_D0ee/*.xdigi")
elif decay == "test":
options.input_files = ["/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi"]
options.conddb_tag = "sim-20210617-vc-md100"
options.dddb_tag = "dddb-20210617"
options.simulation = True
options.input_type = "ROOT"
options.ntuple_file = f"data/tracking_losses_ntuple_{decay}.root"
options.evt_max = -1
# run with
# ./Moore/run gaudirun.py Moore/Hlt/RecoConf/options/tracking_developments/run_pr_tracking_losses.py
# tested by mc_matching_example.py
def run_tracking_losses():
links_to_hits = make_links_lhcbids_mcparticles_tracking_system()
hlt2_tracks = make_hlt2_tracks(light_reco=True, fast_reco=False, use_pr_kf=True)
vp_hits = make_VeloClusterTrackingSIMD_hits()
ut_hits = make_PrStorePrUTHits_hits()
ft_hits = make_PrStoreSciFiHits_hits()
fitted_match_tracks = make_PrKalmanFilter_tracks( # fitted_forward_tracks
input_tracks=hlt2_tracks["Match"]["Pr"], # Forward
hits_vp=vp_hits,
hits_ut=ut_hits,
hits_ft=ft_hits,
)
# add MCLinking to the (fitted) V1 tracks
links_to_velo_tracks = PrTrackAssociator(
SingleContainer=hlt2_tracks["Velo"]["v1"],
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
links_to_long_tracks = PrTrackAssociator(
SingleContainer=hlt2_tracks["Match"]["v1"], # Forward
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
with PrTrackAssociator.bind(FractionOK=0.5):
loose_links_to_long_tracks = PrTrackAssociator(
SingleContainer=hlt2_tracks["Match"]["v1"], # Forward
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
links_to_fitted_tracks = PrTrackAssociator(
SingleContainer=fitted_match_tracks, # fitted_forward_tracks
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
tracking_losses = PrDebugTrackingLosses(
name="PrDebugTrackingLosses",
TrackType="Long",
StudyTracks=hlt2_tracks["Match"]["v1"], # Forward
VeloTracks=hlt2_tracks["Velo"]["v1"],
MCParticles=mc_unpackers()["MCParticles"],
MCVPHits=mc_unpackers()["MCVPHits"],
MCUTHits=mc_unpackers()["MCUTHits"],
MCFTHits=mc_unpackers()["MCFTHits"],
VeloTrackLinks=links_to_velo_tracks,
TrackLinks=links_to_long_tracks,
LooseTrackLinks=loose_links_to_long_tracks,
FittedTrackLinks=links_to_fitted_tracks,
# LHCbIDLinks=links_to_hits,
IdealStateCreator=make_default_IdealStateCreator(),
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
)
data = [tracking_losses]
return Reconstruction("run_tracking_losses", data)
run_reconstruction(options, run_tracking_losses)

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from Moore import options, run_reconstruction
from Moore.config import Reconstruction
from PyConf.Algorithms import PrParameterisationData
from RecoConf.data_from_file import mc_unpackers
from PyConf.application import make_data_with_FetchDataFromFile
import glob
options.evt_max = -1
n_files_per_cat = 1
decay = "B"
options.ntuple_file = f"data/param_data_{decay}.root"
options.input_type = "ROOT"
if decay == "B":
options.input_files = glob.glob("/auto/data/guenther/Bd_Kstee/*.xdigi")
elif decay == "D":
options.input_files = glob.glob("/auto/data/guenther/Dst_D0ee/*.xdigi")
options.dddb_tag = "dddb-20210617"
options.conddb_tag = "sim-20210617-vc-md100"
options.simulation = True
def run_tracking_param_debug():
param_data = PrParameterisationData(
MCParticles=mc_unpackers()["MCParticles"],
MCVPHits=mc_unpackers()["MCVPHits"],
MCFTHits=mc_unpackers()["MCFTHits"],
zRef=8520.0,
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
)
data = [param_data]
return Reconstruction("run_tracking_debug", data)
run_reconstruction(options, run_tracking_param_debug)

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# flake8: noqa
"""
Moore/run gaudirun.py /work/cetin/LHCb/reco_tuner/moore_options/get_ghost_data.py
"""
from Moore import options, run_reconstruction
from Moore.config import Reconstruction
from PyConf.Algorithms import (
PrForwardTrackingVelo,
PrForwardTracking,
PrTrackAssociator,
PrMatchNN,
PrResidualVeloTracks,
PrResidualSeedingLong,
fromPrMatchTracksV1Tracks,
fromPrVeloTracksV1Tracks,
fromPrSeedingTracksV1Tracks,
)
from PyConf.application import make_data_with_FetchDataFromFile
from PyConf.Tools import PrMCDebugForwardTool, PrMCDebugMatchToolNN
from RecoConf.data_from_file import mc_unpackers
from RecoConf.hlt1_tracking import make_hlt1_tracks, make_PrStoreSciFiHits_hits
from RecoConf.hlt2_tracking import (
get_global_ut_hits_tool,
make_PrHybridSeeding_tracks,
make_PrMatchNN_tracks,
get_fast_hlt2_tracks,
)
from RecoConf.mc_checking import make_links_lhcbids_mcparticles_tracking_system
import glob
options.evt_max = -1
decay = "D" # D, B
options.ntuple_file = f"data/ghost_data_{decay}_electron_weights.root"
options.input_type = "ROOT"
if decay == "B":
options.input_files = glob.glob("/auto/data/guenther/Bd_Kstee/*.xdigi")
elif decay == "D":
options.input_files = glob.glob("/auto/data/guenther/Dst_D0ee/*.xdigi")
elif decay == "test":
options.input_files = ["/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi"]
elif decay == "both":
options.input_files = glob.glob("/auto/data/guenther/Bd_Kstee/*.xdigi") + glob.glob(
"/auto/data/guenther/Dst_D0ee/*.xdigi"
)
options.dddb_tag = "dddb-20210617"
options.conddb_tag = "sim-20210617-vc-md100"
# options.geometry_version = "run3/trunk" #"run3/before-rich1-geom-update-26052022"
# options.conditions_version = "master"
options.simulation = True
def run_tracking_debug():
links_to_hits = make_links_lhcbids_mcparticles_tracking_system()
hlt1_tracks = make_hlt1_tracks()
seed_tracks = make_PrHybridSeeding_tracks()
# add MCLinking to the (fitted) V1 tracks
links_to_velo_tracks = PrTrackAssociator(
SingleContainer=hlt1_tracks["Velo"]["v1"],
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
links_to_upstream_tracks = PrTrackAssociator(
SingleContainer=hlt1_tracks["Upstream"]["v1"],
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
links_to_seed_tracks = PrTrackAssociator(
SingleContainer=seed_tracks["v1"],
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
# be more robust against imperfect data
loose_forward_params = dict(
MaxChi2PerDoF=16,
MaxChi2XProjection=30,
MaxChi2PerDoFFinal=8,
MaxChi2Stereo=8,
MaxChi2StereoAdd=8,
)
forward_debug = PrForwardTrackingVelo(
InputTracks=hlt1_tracks["Velo"]["Pr"],
SciFiHits=make_PrStoreSciFiHits_hits(),
AddUTHitsToolName=get_global_ut_hits_tool(enable=True),
DebugTool=PrMCDebugForwardTool(
InputTracks=hlt1_tracks["Velo"]["v1"],
InputTrackLinks=links_to_velo_tracks,
MCParticles=mc_unpackers()["MCParticles"],
SciFiHitLinks=links_to_hits,
SciFiHits=make_PrStoreSciFiHits_hits(),
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
),
**loose_forward_params,
)
forward_ut_debug = PrForwardTracking(
SciFiHits=make_PrStoreSciFiHits_hits(),
InputTracks=hlt1_tracks["Upstream"]["Pr"],
AddUTHitsToolName=get_global_ut_hits_tool(enable=True),
DebugTool=PrMCDebugForwardTool(
InputTracks=hlt1_tracks["Upstream"]["v1"],
InputTrackLinks=links_to_upstream_tracks,
MCParticles=mc_unpackers()["MCParticles"],
SciFiHitLinks=links_to_hits,
SciFiHits=make_PrStoreSciFiHits_hits(),
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
),
**loose_forward_params,
)
loose_matching_params = dict(
MaxMatchChi2=30.0, # 30.0,
MaxDistX=500, # 500,
MaxDistY=500, # 500,
MaxDSlope=1.5,
MinMatchNN=0.215, # NN response cut value
)
match_debug = PrMatchNN(
VeloInput=hlt1_tracks["Velo"]["Pr"],
SeedInput=seed_tracks["Pr"],
MatchDebugToolName=PrMCDebugMatchToolNN(
VeloTracks=hlt1_tracks["Velo"]["v1"],
SeedTracks=seed_tracks["v1"],
VeloTrackLinks=links_to_velo_tracks,
SeedTrackLinks=links_to_seed_tracks,
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
MCParticles=mc_unpackers()["MCParticles"],
),
AddUTHitsToolName=get_global_ut_hits_tool(enable=True),
**loose_matching_params,
).MatchOutput
"""
v1_match_tracks = fromPrMatchTracksV1Tracks(
InputTracksLocation=match_debug,
VeloTracksLocation=hlt1_tracks["Velo"]["v1"],
SeedTracksLocation=seed_tracks["v1"],
).OutputTracksLocation
# run Matching on residual velo and seed track segments
pr_velo_residual = PrResidualVeloTracks(
TracksLocation=match_debug,
VeloTrackLocation=hlt1_tracks["Velo"]["Pr"],
).VeloTrackOutput
v1_velo_residual = fromPrVeloTracksV1Tracks(
InputTracksLocation=pr_velo_residual
).OutputTracksLocation
pr_seed_residual = PrResidualSeedingLong(
MatchTracksLocation=match_debug,
SeedTracksLocation=seed_tracks["Pr"],
).SeedTracksOutput
v1_seed_residual = fromPrSeedingTracksV1Tracks(
InputTracksLocation=pr_seed_residual
).OutputTracksLocation
# add MCLinking to the (fitted) residual V1 tracks
links_to_res_velo_tracks = PrTrackAssociator(
SingleContainer=v1_velo_residual,
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
links_to_res_seed_tracks = PrTrackAssociator(
SingleContainer=v1_seed_residual,
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
loose_res_matching_params = dict(
MaxMatchChi2=30.0, # 30.0,
MaxDistX=500, # 500,
MaxDistY=500, # 500,
MaxDSlope=1.5,
MinMatchNN=0.5, # NN response cut value
FastYTol=2500.0,
)
match_residual = PrMatchNN(
VeloInput=pr_velo_residual,
SeedInput=pr_seed_residual,
MatchDebugToolName=PrMCDebugMatchToolNN(
VeloTracks=v1_velo_residual,
SeedTracks=v1_seed_residual,
VeloTrackLinks=links_to_res_velo_tracks,
SeedTrackLinks=links_to_res_seed_tracks,
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
MCParticles=mc_unpackers()["MCParticles"],
),
AddUTHitsToolName=get_global_ut_hits_tool(enable=True),
**loose_res_matching_params,
).MatchOutput
"""
data = [forward_debug, forward_ut_debug, match_debug] # match_residual]
return Reconstruction("run_tracking_debug", data)
run_reconstruction(options, run_tracking_debug)

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# flake8: noqa
"""
This set of options is used for reconstruction development purposes,
and assumes that the input contains MCHits (i.e. is of `Exended`
DST/digi type).
author: Furkan Cetin
date: 10/2023
Moore/run gaudirun.py /work/cetin/LHCb/reco_tuner/moore_options/get_resolution_and_eff_data.py
"""
from Moore import options, run_reconstruction
from Moore.config import Reconstruction
from PyConf.Algorithms import PrKalmanFilter
from PyConf.Tools import TrackMasterExtrapolator
import glob
from RecoConf.mc_checking import (
check_track_resolution,
check_tracking_efficiency,
get_mc_categories,
get_hit_type_mask,
make_links_lhcbids_mcparticles_tracking_system,
make_links_tracks_mcparticles,
)
from RecoConf.core_algorithms import make_unique_id_generator
from RecoConf.hlt2_tracking import make_hlt2_tracks
from RecoConf.hlt1_tracking import (
make_VeloClusterTrackingSIMD_hits,
make_PrStorePrUTHits_hits,
make_PrStoreSciFiHits_hits,
get_global_materiallocator,
)
decay = "D"
options.evt_max = -1
options.ntuple_file = f"data/resolutions_and_effs_{decay}_electron_weights.root"
options.input_type = "ROOT"
if decay == "B":
options.input_files = glob.glob("/auto/data/guenther/Bd_Kstee/*.xdigi")
elif decay == "D":
options.input_files = glob.glob("/auto/data/guenther/Dst_D0ee/*.xdigi")
elif decay == "test":
options.input_files = ["/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi"]
options.dddb_tag = "dddb-20210617"
options.conddb_tag = "sim-20210617-vc-md100"
options.simulation = True
def run_tracking_resolution():
tracks = make_hlt2_tracks(light_reco=True, fast_reco=False, use_pr_kf=True)
fitted_forward_tracks = PrKalmanFilter(
Input=tracks["Forward"]["Pr"],
MaxChi2=2.8,
MaxChi2PreOutlierRemoval=20,
HitsVP=make_VeloClusterTrackingSIMD_hits(),
HitsUT=make_PrStorePrUTHits_hits(),
HitsFT=make_PrStoreSciFiHits_hits(),
ReferenceExtrapolator=TrackMasterExtrapolator(
MaterialLocator=get_global_materiallocator(),
),
InputUniqueIDGenerator=make_unique_id_generator(),
).OutputTracks
links_to_lhcbids = make_links_lhcbids_mcparticles_tracking_system()
links_to_forward = make_links_tracks_mcparticles(
InputTracks=tracks["Forward"],
LinksToLHCbIDs=links_to_lhcbids,
)
links_to_match = make_links_tracks_mcparticles(
InputTracks=tracks["Match"],
LinksToLHCbIDs=links_to_lhcbids,
)
links_to_best = make_links_tracks_mcparticles(
InputTracks=tracks["BestLong"],
LinksToLHCbIDs=links_to_lhcbids,
)
links_to_seed = make_links_tracks_mcparticles(
InputTracks=tracks["Seed"],
LinksToLHCbIDs=links_to_lhcbids,
)
res_checker_forward = check_track_resolution(tracks["Forward"], suffix="Forward")
res_checker_best_long = check_track_resolution(
tracks["BestLong"],
suffix="BestLong",
)
res_checker_best_forward = check_track_resolution(
dict(v1=fitted_forward_tracks),
suffix="BestForward",
)
res_checker_seed = check_track_resolution(
tracks["Seed"],
suffix="Seed",
)
eff_checker_forward = check_tracking_efficiency(
"Forward",
tracks["Forward"],
links_to_forward,
links_to_lhcbids,
get_mc_categories("Forward"),
get_hit_type_mask("Forward"),
)
eff_checker_match = check_tracking_efficiency(
"Match",
tracks["Match"],
links_to_match,
links_to_lhcbids,
get_mc_categories("Match"),
get_hit_type_mask("Match"),
)
eff_checker_best_long = check_tracking_efficiency(
"BestLong",
tracks["BestLong"],
links_to_best,
links_to_lhcbids,
get_mc_categories("BestLong"),
get_hit_type_mask("BestLong"),
)
eff_checker_seed = check_tracking_efficiency(
"Seed",
tracks["Seed"],
links_to_seed,
links_to_lhcbids,
get_mc_categories("Seed"),
get_hit_type_mask("Seed"),
)
data = [
res_checker_forward,
res_checker_best_long,
res_checker_best_forward,
res_checker_seed,
eff_checker_forward,
eff_checker_match,
eff_checker_best_long,
eff_checker_seed,
]
return Reconstruction("run_tracking_debug", data)
run_reconstruction(options, run_tracking_resolution)

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###############################################################################
# (c) Copyright 2023 CERN for the benefit of the LHCb Collaboration #
# #
# This software is distributed under the terms of the GNU General Public #
# Licence version 3 (GPL Version 3), copied verbatim in the file "COPYING". #
# #
# In applying this licence, CERN does not waive the privileges and immunities #
# granted to it by virtue of its status as an Intergovernmental Organization #
# or submit itself to any jurisdiction. #
###############################################################################
# flake8: noqa
from Moore import options, run_reconstruction
from Moore.config import Reconstruction
from RecoConf.data_from_file import mc_unpackers
from RecoConf.hlt1_tracking import make_VeloClusterTrackingSIMD_hits
from RecoConf.hlt2_tracking import (
make_hlt2_tracks,
make_PrKalmanFilter_tracks,
make_PrStorePrUTHits_hits,
make_PrStoreSciFiHits_hits,
)
from RecoConf.mc_checking import (
make_links_lhcbids_mcparticles_tracking_system,
make_links_tracks_mcparticles,
make_default_IdealStateCreator,
)
from PyConf.Algorithms import PrTrackAssociator, PrDebugTrackingLosses
from PyConf.application import make_data_with_FetchDataFromFile
import glob
"""
run with
Moore/run gaudirun.py /work/cetin/LHCb/reco_tuner/moore_options/get_tracking_losses.py
tested by mc_matching_example.py
"""
decay = "D"
if decay == "B":
options.input_files = glob.glob("/auto/data/guenther/Bd_Kstee/*.xdigi")
elif decay == "D":
options.input_files = glob.glob("/auto/data/guenther/Dst_D0ee/*.xdigi")
elif decay == "test":
options.input_files = ["/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi"]
options.conddb_tag = "sim-20210617-vc-md100"
options.dddb_tag = "dddb-20210617"
options.simulation = True
options.input_type = "ROOT"
options.ntuple_file = f"data/tracking_losses_ntuple_{decay}_match_electron_weights.root"
options.evt_max = -1
def run_tracking_losses():
links_to_hits = make_links_lhcbids_mcparticles_tracking_system()
hlt2_tracks = make_hlt2_tracks(light_reco=True, fast_reco=False, use_pr_kf=True)
vp_hits = make_VeloClusterTrackingSIMD_hits()
ut_hits = make_PrStorePrUTHits_hits()
ft_hits = make_PrStoreSciFiHits_hits()
fitted_match_tracks = make_PrKalmanFilter_tracks( # fitted_forward_tracks
input_tracks=hlt2_tracks["Match"]["Pr"], # Forward
hits_vp=vp_hits,
hits_ut=ut_hits,
hits_ft=ft_hits,
)
# add MCLinking to the (fitted) V1 tracks
links_to_velo_tracks = PrTrackAssociator(
SingleContainer=hlt2_tracks["Velo"]["v1"],
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
links_to_long_tracks = PrTrackAssociator(
SingleContainer=hlt2_tracks["Match"]["v1"], # Forward
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
with PrTrackAssociator.bind(FractionOK=0.5):
loose_links_to_long_tracks = PrTrackAssociator(
SingleContainer=hlt2_tracks["Match"]["v1"], # Forward
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
links_to_fitted_tracks = PrTrackAssociator(
SingleContainer=fitted_match_tracks, # fitted_forward_tracks
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
tracking_losses = PrDebugTrackingLosses(
name="PrDebugTrackingLosses",
TrackType="Long",
StudyTracks=hlt2_tracks["Match"]["v1"], # Forward
VeloTracks=hlt2_tracks["Velo"]["v1"],
MCParticles=mc_unpackers()["MCParticles"],
MCVPHits=mc_unpackers()["MCVPHits"],
MCUTHits=mc_unpackers()["MCUTHits"],
MCFTHits=mc_unpackers()["MCFTHits"],
VeloTrackLinks=links_to_velo_tracks,
TrackLinks=links_to_long_tracks,
LooseTrackLinks=loose_links_to_long_tracks,
FittedTrackLinks=links_to_fitted_tracks,
# LHCbIDLinks=links_to_hits,
IdealStateCreator=make_default_IdealStateCreator(),
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
)
data = [tracking_losses]
return Reconstruction("run_tracking_losses", data)
run_reconstruction(options, run_tracking_losses)

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# flake8: noqaq
"""
NOT IMPLEMENTED YET
Moore/run gaudirun.py /work/cetin/LHCb/reco_tuner/moore_options/Recent_get_ghost_data.py
"""
from Moore import options, run_reconstruction
from Moore.config import Reconstruction
from PyConf.Algorithms import (
PrForwardTrackingVelo,
PrForwardTracking,
PrTrackAssociator,
PrMatchNN,
)
from PyConf.application import make_data_with_FetchDataFromFile
from PyConf.Tools import PrMCDebugForwardTool, PrMCDebugMatchToolNN
from RecoConf.data_from_file import mc_unpackers
from RecoConf.hlt1_tracking import make_hlt1_tracks, make_PrStoreSciFiHits_hits
from RecoConf.hlt2_tracking import get_global_ut_hits_tool, make_PrHybridSeeding_tracks
from RecoConf.mc_checking import make_links_lhcbids_mcparticles_tracking_system
import glob
options.evt_max = -1
decay = "test" # D, B
options.ntuple_file = f"data/ghost_data_{decay}.root"
options.input_type = "ROOT"
if decay == "B":
options.input_files = glob.glob("/auto/data/guenther/Bd_Kstee/*.xdigi")
elif decay == "D":
options.input_files = glob.glob("/auto/data/guenther/Dst_D0ee/*.xdigi")
elif decay == "test":
options.input_files = ["/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi"]
elif decay == "both":
options.input_files = glob.glob("/auto/data/guenther/Bd_Kstee/*.xdigi") + glob.glob(
"/auto/data/guenther/Dst_D0ee/*.xdigi"
)
options.dddb_tag = "dddb-20210617"
options.conddb_tag = "sim-20210617-vc-md100"
options.simulation = True
def run_tracking_debug():
links_to_hits = make_links_lhcbids_mcparticles_tracking_system()
hlt1_tracks = make_hlt1_tracks()
seed_tracks = make_PrHybridSeeding_tracks()
# add MCLinking to the (fitted) V1 tracks
links_to_velo_tracks = PrTrackAssociator(
SingleContainer=hlt1_tracks["Velo"]["v1"],
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
links_to_upstream_tracks = PrTrackAssociator(
SingleContainer=hlt1_tracks["Upstream"]["v1"],
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
links_to_seed_tracks = PrTrackAssociator(
SingleContainer=seed_tracks["v1"],
LinkerLocationID=links_to_hits,
MCParticleLocation=mc_unpackers()["MCParticles"],
MCVerticesInput=mc_unpackers()["MCVertices"],
).OutputLocation
# be more robust against imperfect data
loose_forward_params = dict(
MaxChi2PerDoF=16,
MaxChi2XProjection=30,
MaxChi2PerDoFFinal=8,
MaxChi2Stereo=8,
MaxChi2StereoAdd=8,
)
forward_debug = PrForwardTrackingVelo(
InputTracks=hlt1_tracks["Velo"]["Pr"],
SciFiHits=make_PrStoreSciFiHits_hits(),
AddUTHitsToolName=get_global_ut_hits_tool(enable=True),
DebugTool=PrMCDebugForwardTool(
InputTracks=hlt1_tracks["Velo"]["v1"],
InputTrackLinks=links_to_velo_tracks,
MCParticles=mc_unpackers()["MCParticles"],
SciFiHitLinks=links_to_hits,
SciFiHits=make_PrStoreSciFiHits_hits(),
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
),
**loose_forward_params,
)
forward_ut_debug = PrForwardTracking(
SciFiHits=make_PrStoreSciFiHits_hits(),
InputTracks=hlt1_tracks["Upstream"]["Pr"],
AddUTHitsToolName=get_global_ut_hits_tool(enable=True),
DebugTool=PrMCDebugForwardTool(
InputTracks=hlt1_tracks["Upstream"]["v1"],
InputTrackLinks=links_to_upstream_tracks,
MCParticles=mc_unpackers()["MCParticles"],
SciFiHitLinks=links_to_hits,
SciFiHits=make_PrStoreSciFiHits_hits(),
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
),
**loose_forward_params,
)
loose_matching_params = dict(
MaxMatchChi2=30.0,
MaxDistX=500,
MaxDistY=500,
MaxDSlope=1.5,
)
match_debug = PrMatchNN(
VeloInput=hlt1_tracks["Velo"]["Pr"],
SeedInput=seed_tracks["Pr"],
MatchDebugToolName=PrMCDebugMatchToolNN(
VeloTracks=hlt1_tracks["Velo"]["v1"],
SeedTracks=seed_tracks["v1"],
VeloTrackLinks=links_to_velo_tracks,
SeedTrackLinks=links_to_seed_tracks,
TrackInfo=make_data_with_FetchDataFromFile("/Event/MC/TrackInfo"),
MCParticles=mc_unpackers()["MCParticles"],
),
AddUTHitsToolName=get_global_ut_hits_tool(enable=True),
**loose_matching_params,
).MatchOutput
data = [forward_debug, forward_ut_debug, match_debug]
return Reconstruction("run_tracking_debug", data)
run_reconstruction(options, run_tracking_debug)

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# flake8: noqa
"""
This set of options is used for reconstruction development purposes,
and assumes that the input contains MCHits (i.e. is of `Exended`
DST/digi type).
author: Furkan Cetin
date: 10/2023
Moore/run gaudirun.py /work/cetin/LHCb/reco_tuner/moore_options/residual_get_resolution_and_eff_data.py
"""
from Moore import options, run_reconstruction
from Moore.config import Reconstruction
from PyConf.Algorithms import PrKalmanFilter
from PyConf.Tools import TrackMasterExtrapolator
import glob
from RecoConf.mc_checking import (
check_track_resolution,
check_tracking_efficiency,
get_mc_categories,
get_hit_type_mask,
make_links_lhcbids_mcparticles_tracking_system,
make_links_tracks_mcparticles,
)
from RecoConf.core_algorithms import make_unique_id_generator
from RecoConf.hlt2_tracking import make_res_hlt2_tracks
from RecoConf.hlt1_tracking import (
make_VeloClusterTrackingSIMD_hits,
make_PrStorePrUTHits_hits,
make_PrStoreSciFiHits_hits,
get_global_materiallocator,
)
# sample = "DstD0EE_MD"
# sample = "Bd2KstEE_MD"
# sample = "Bd2KstEE_MU"
# sample = "Bs2JpsiPhi_MD"
# sample = "Bs2JpsiPhi_MU"
# sample = "Bs2PhiPhi_MD"
# sample = "Bs2PhiPhi_MU"
decay = "D"
options.evt_max = -1
options.ntuple_file = (
f"data/resolutions_and_effs_{decay}_with_electron_weights_as_residual.root"
)
options.input_type = "ROOT"
if decay == "B":
options.input_files = glob.glob("/auto/data/guenther/Bd_Kstee/*.xdigi")
elif decay == "D":
options.input_files = glob.glob("/auto/data/guenther/Dst_D0ee/*.xdigi")
elif decay == "test":
options.input_files = ["/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi"]
options.dddb_tag = "dddb-20210617"
options.conddb_tag = "sim-20210617-vc-md100"
options.simulation = True
def run_tracking_resolution():
tracks = make_res_hlt2_tracks(light_reco=True, fast_reco=False, use_pr_kf=True)
fitted_forward_tracks = PrKalmanFilter(
Input=tracks["Forward"]["Pr"],
MaxChi2=2.8,
MaxChi2PreOutlierRemoval=20,
HitsVP=make_VeloClusterTrackingSIMD_hits(),
HitsUT=make_PrStorePrUTHits_hits(),
HitsFT=make_PrStoreSciFiHits_hits(),
ReferenceExtrapolator=TrackMasterExtrapolator(
MaterialLocator=get_global_materiallocator(),
),
InputUniqueIDGenerator=make_unique_id_generator(),
).OutputTracks
links_to_lhcbids = make_links_lhcbids_mcparticles_tracking_system()
links_to_forward = make_links_tracks_mcparticles(
InputTracks=tracks["Forward"],
LinksToLHCbIDs=links_to_lhcbids,
)
links_to_match = make_links_tracks_mcparticles(
InputTracks=tracks["Match"],
LinksToLHCbIDs=links_to_lhcbids,
)
links_to_best = make_links_tracks_mcparticles(
InputTracks=tracks["BestLong"],
LinksToLHCbIDs=links_to_lhcbids,
)
links_to_seed = make_links_tracks_mcparticles(
InputTracks=tracks["Seed"],
LinksToLHCbIDs=links_to_lhcbids,
)
res_checker_forward = check_track_resolution(
tracks["Forward"],
suffix="Forward",
)
res_checker_best_long = check_track_resolution(
tracks["BestLong"],
suffix="BestLong",
)
res_checker_best_forward = check_track_resolution(
dict(v1=fitted_forward_tracks),
suffix="BestForward",
)
res_checker_seed = check_track_resolution(
tracks["Seed"],
suffix="Seed",
)
eff_checker_forward = check_tracking_efficiency(
"Forward",
tracks["Forward"],
links_to_forward,
links_to_lhcbids,
get_mc_categories("Forward"),
get_hit_type_mask("Forward"),
)
eff_checker_match = check_tracking_efficiency(
"Match",
tracks["Match"],
links_to_match,
links_to_lhcbids,
get_mc_categories("Match"),
get_hit_type_mask("Match"),
)
eff_checker_best_long = check_tracking_efficiency(
"BestLong",
tracks["BestLong"],
links_to_best,
links_to_lhcbids,
get_mc_categories("BestLong"),
get_hit_type_mask("BestLong"),
)
eff_checker_seed = check_tracking_efficiency(
"Seed",
tracks["Seed"],
links_to_seed,
links_to_lhcbids,
get_mc_categories("Seed"),
get_hit_type_mask("Seed"),
)
data = [
res_checker_forward,
res_checker_best_long,
res_checker_best_forward,
res_checker_seed,
eff_checker_forward,
eff_checker_match,
eff_checker_best_long,
eff_checker_seed,
]
return Reconstruction("run_tracking_debug", data)
run_reconstruction(options, run_tracking_resolution)

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const auto fMin = std::array<simd::float_v, 6>{
{1.32643926918e-05, 1.20999777664e-06, 3.81469726562e-06, 1.52587890625e-05,
2.20164656639e-06, 1.86264514923e-09}};
const auto fMax = std::array<simd::float_v, 6>{{14.9999952316, 0.436187297106,
249.999572754, 399.485595703,
1.30260443687, 0.148344695568}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{2.32568146034949, -3.97864517484141, -0.976136452226726, 1.84234344676559,
-3.10046463102268, 4.13961872392198, 1.32395215581256},
{-0.246260592363558, -16.6289365646957, 15.8745926520597, 5.54227150397204,
-3.52013322130382, 3.54800430147538, 4.65963029843042},
{-0.0480865527472585, -0.629210074395733, 6.00348546361291,
2.9051880336304, -0.14352194426084, 1.69533803008533, 8.43612131346998},
{0.586453583994425, -2.56124202576808, 2.59227690708752,
0.0874243316906918, -2.97381765628525, 5.49796401976845,
3.23192359468339},
{0.429663439996412, -22.1383805768484, -0.392774946210208,
-3.3393241414433, -0.0183236766918373, 1.7443084621404,
-23.1241106528584},
{1.51561003857451, -0.252437187813493, 3.4382652179148, 1.64873635165153,
1.3257641118939, -1.3769915299618, 6.284788658685},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.26283446391613, 1.060406101318, 0.30016156694275, 0.868137090713936,
0.620452727287864, 0.654572151525178, -1.93868171775984}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-0.756398914721592, 1.43176897679079, -1.9761225512629,
-0.252826703054453, 5.76338466721064, 0.853447490406625, 1.63438201788813,
-1.30124222851611, -1.16516476663684},
{1.33354118308893, 2.2779204457711, -2.4183940976708, -1.41409141050929,
-3.03014280476042, -0.105294409656274, -1.61531096417457,
0.0713464687805576, -4.46730787742624},
{1.69117951310622, 0.478803367417533, -0.0952992998738417,
-1.42291620159966, -5.3475695755735, -0.851706256912453,
-0.825543426908553, -1.84634786630319, 1.10300947885605},
{1.62294844942986, -1.4305887420849, 1.34690035656602, -1.75196364787073,
-1.34911857298729, -1.19784919878849, 1.61348068527877, -1.6413641883722,
-1.80987544922642},
{-0.885340378859963, -1.27010625003553, 1.64729145944323,
-1.93179670311711, -2.00487598846412, 0.858689001379895,
-0.848898627795279, 0.783837335125351, -1.50563595386066},
{-0.643070342091735, -1.362074820856, 3.23003893144526, -1.8069989021131,
-1.52168986931666, -2.92720177768097, -1.52203810494393, 1.54153084775635,
4.02998353429178}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-1.03488783417574, 0.540010884713827, -1.17870273673375, 1.01943381348885,
-0.679190259548567, 1.25798110915057, 2.3197360649145}};

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const auto fMin = std::array<simd::float_v, 6>{
{1.37092808927e-06, 1.07555365503e-06, 0, 1.90734863281e-06,
1.73929147422e-05, 1.86264514923e-09}};
const auto fMax = std::array<simd::float_v, 6>{{14.9999952316, 0.456682443619,
249.999572754, 399.509643555,
1.33115208149, 0.149789437652}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{-1.3734781925797, 13.4202721220084, -5.84482699847354, 0.208720202271194,
3.52940201568696, -5.35007508017961, 6.10232623582908},
{0.269463828190076, 12.2029002280153, 6.20803317501961, -9.43442815316897,
2.5338939027162, 5.99544654330182, 16.266514230858},
{-0.165852817298963, -12.5570036498389, 19.5108101030614, 10.1445756810778,
-4.70591905221782, -9.82613113151628, 2.66946232799658},
{0.280264112609391, -40.4573608414915, 4.50829859766595, -9.38270110978156,
2.13898954875748, 4.73797410702965, -38.2552994749474},
{-15.3913555770922, 1.18454625888548, 1.03308239102009, 2.80096921737441,
-1.86435943580432, -5.12259817922783, -14.7182721956392},
{-0.473433045504226, 14.9901069695702, -0.236384720797966,
-2.83841297397374, 4.98474416815065, -6.59501221410077, 6.97717117093051},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{0.142197307909266, 4.84602282950846, -9.65725300640334, 5.68314089024306,
0.631054662487241, 0.766483060165155, 2.3260315163825}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{0.647996552227704, -3.612673407752, 0.218049700051821, 4.89119034256858,
-0.00710530398728626, -0.739119819896367, 1.63438201788813,
0.7192739388343, -4.39806909742125},
{-0.719597437431301, -3.27873531826254, -2.03233412174408,
-3.60079441122056, 0.0930923625129556, -2.47473692076248,
-1.61531096417457, -1.73667807655155, 3.65065717704823},
{2.15115292231327, 0.537266754158749, -0.529575619029901,
-0.840914255611436, 1.02786405393109, -2.2383981589872,
-0.825543426908553, -0.685116658617715, -1.95672133400954},
{0.164139216021613, -0.378666175423714, -1.43567813416239,
-1.86509513117207, -0.825083002191541, -1.70460785835385,
1.61348068527877, -1.66550797875971, -0.956253568725315},
{-1.87493924816154, -0.453672605669931, 0.283493943583684,
0.878365550455799, 0.284631862858431, 0.933935190438462,
-0.848898627795279, 0.121615867119966, 2.40557433526087},
{0.853517633026983, -0.322377109742158, 0.30359642229039,
-2.70050427549895, 0.434398564771274, -1.07531792256432,
-1.52203810494393, 0.471135339353818, -7.51274733403613}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.773202850704438, 0.952227138510482, 0.74769506152075, 0.306824902699197,
-0.557424643818581, 1.36609661342348, -1.24818793392955}};

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const auto fMin = std::array<simd::float_v, 6>{
{1.37092808927e-06, 1.07555365503e-06, 0, 1.90734863281e-06,
1.73929147422e-05, 1.86264514923e-09}};
const auto fMax = std::array<simd::float_v, 6>{{14.9999952316, 0.456682443619,
249.999572754, 399.509643555,
1.33115208149, 0.149789437652}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{0.55218535628556, -9.3289553119363, -3.16480805777192, 9.21929582222451,
-5.84675321729571, 4.37995011218691, -2.12651852927708},
{2.19402229437066, -36.4572143799157, 4.72612050852174, 0.871774263011679,
0.308249736812244, 5.59902946146285, -21.3121523564936},
{0.326882064023056, 2.35866196875568, 9.48783066071353, 2.75913715527822,
-3.60778259684168, 2.80447887380193, 12.22677213297},
{0.555959841347612, -11.3379921223552, 24.99514413087, 4.38044026679039,
-4.79766508655656, -5.51874542469878, 8.39926399588362},
{-0.474573814356478, -45.048645069346, -1.91571008337192,
-2.97043145049536, -0.791922976045819, 2.80933052961339,
-45.2686657256446},
{1.02111090620048, 0.942295739720341, 4.23884295504771, 3.69611210680021,
3.06108184531354, -5.59083664638509, 5.48212218750871},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.25219270646431, 0.549228434890616, 0.470255515433846, 0.916142200504342,
1.60846971174291, 0.516066034145183, -1.99907858325808}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-2.16740050633671, 1.64201098704318, -1.81457731661729, 0.276267162453127,
4.41723045721244, 0.116946763347361, 1.63438201788813, -1.34454525041306,
-11.6363132267585},
{-0.975733315897721, -0.74456197080548, 1.37299729852781,
-0.935058973429512, 0.0844226992748141, -0.132452262552727,
-1.61531096417457, -0.186263378023113, 5.02662780750337},
{1.04696354000933, 0.278924511733321, -1.35925413801625, 0.938772342837744,
-0.549530917541879, -0.520171806146222, -0.825543426908553,
-2.06608637235381, -0.791984902945839},
{-1.2045961477844, -0.991003979261367, 1.09783625990238,
-0.421872249827208, -0.889785288418292, 2.04952712400642,
1.61348068527877, -1.7061481912452, -4.6379237728574},
{-1.36108475234833, -0.998277929718627, 1.44485269371602,
-0.712692589749601, 2.24954768341439, 2.14013866962467,
-0.848898627795279, 0.868380765164237, -2.78040856790563},
{-0.388348743847599, -3.23828818784509, -3.09515929145523,
-1.60979064312646, 2.55518501696684, -2.40442392560053, -1.52203810494393,
1.61704406536505, 1.28981466057697}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.662286199846436, 0.602757344813461, -0.498657128878293,
0.682053959836921, -0.846606195204036, 0.885206167679193,
-0.091536072257332}};

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const auto fMin = std::array<simd::float_v, 6>{
{8.165359759e-06, 1.20664617498e-06, 3.0517578125e-05, 0, 4.7143548727e-06,
5.58793544769e-09}};
const auto fMax =
std::array<simd::float_v, 6>{{14.9999341965, 0.441820472479, 249.991241455,
399.657226562, 1.31253051758, 0.1461160779}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{-2.69517659211572, 11.8302794023495, -4.18951579686807, -3.98494892798567,
2.81897548445767, 0.59383239448013, 8.23409922740496},
{0.211366021230384, -17.963369064596, 15.9757126175754, 7.06797978526591,
-4.70452883659984, -6.9670945574808, -6.09966951812501},
{-0.671572194549583, 11.3044506689324, 0.41567016443692, -1.37717944379749,
4.32454960210643, -2.81417446537734, 9.27800394526066},
{-0.0170007006326477, -29.3978844207289, 1.21375106319138,
-4.08361109078602, 1.26964946956945, 2.36059581879151, -28.6616649803861},
{-11.5040478504233, 0.787126057627091, -1.9688816880041, 3.80563620582515,
-1.24505398457039, -4.63206817893295, -13.6204407803068},
{-0.338909805576579, 5.40829054574145, -5.80255047095045,
-4.01690019633219, 1.01720190260241, -8.00726918670078,
-9.13220942993612},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{-0.0200186919403349, 1.41949954504535, 1.49019129872922,
0.288411192617344, -1.04637027529446, 0.461207091311545,
2.34712624673865}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-0.742932789484951, 1.098742538125, -0.406409364576387, 3.47055509094897,
0.0962780863393642, 1.41748292133237, 1.63438201788813, -1.44301381179313,
-0.572613401802679},
{-0.38589120983735, 1.59861062444015, -0.0248567208616739,
0.671741015980856, -0.708380620370054, -1.03895600322296,
-1.61531096417457, -0.148523097987218, -4.64632456422582},
{0.79166633002489, -1.08475628425482, -4.28859285488566, 1.52323344063281,
0.841577416846386, -2.87987947235168, -0.825543426908553,
-1.68433960913801, 3.44474663480542},
{0.0775004589408732, -0.262461293729405, -1.52083397977799,
-1.8717755745741, -0.836405509817299, -1.7218693116007, 1.61348068527877,
-1.66550797875971, -0.970612266783855},
{-0.173976577204694, 0.622518962366594, 1.06846030554012,
-1.98774771637332, 0.519455930696643, 0.29715629978414,
-0.848898627795279, -0.571811756436865, -0.634485828880002},
{1.01806297385566, -2.23322855713652, -0.6087066354355, -2.48675705217909,
3.17812971554116, 0.101672334443862, -1.52203810494393, 2.31992216900119,
-1.25181073559493}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.916964821952665, 0.719312774569769, -0.639131582384414,
0.543723763328418, -0.519810071051254, 0.818949275577508,
-0.217502220186121}};

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const auto fMin = std::array<simd::float_v, 6>{
{3.08839298668e-06, 1.0285064036e-06, 1.52587890625e-05, 0,
5.87664544582e-07, 1.16415321827e-10}};
const auto fMax =
std::array<simd::float_v, 6>{{14.9999723434, 0.448565632105, 249.991241455,
399.657226562, 1.32571601868, 0.1461160779}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{-13.6018653076529, 11.5780217700141, -7.92762809494091, -2.3767990231665,
2.10509041357149, 8.93423542038951, 0.697736541430846},
{1.39148569147387, -18.5749654585149, 16.332262515645, 8.93683318362009,
-5.31296543840869, -5.3403427435078, -2.19396356951465},
{-1.01323411158617, 13.2753123794943, 0.728991860392637, -2.42297786296918,
5.31377513515812, -3.50060317341991, 10.417424252956},
{-0.248243535822069, 4.62216903283789, 7.02215266119243, 1.16722623835237,
-4.02343144066426, 0.795833957766165, 8.68951250524976},
{-0.238717750484162, 6.4095254209171, -7.18004762765776, -5.26488261250603,
0.399079753011244, -13.2043917021304, -15.6484370000787},
{0.28927080766293, -43.0775712799999, 1.66954473021466, -9.33896425089968,
2.33665742943925, 3.79800824384931, -44.3378970188981},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.40243557561751, 0.527362898119982, 0.45726589950568, 1.14682278333905,
1.07970493015474, -0.120090795589863, -1.93859670804163}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-0.799170659507791, 0.78794128149515, -0.763826599227941,
-2.3771947370175, 1.02090569194105, 2.93661596670106, 1.63438201788813,
-1.4315640726598, -1.65256239855233},
{-0.0840828763430264, 1.63030483445294, 0.480480602063334,
-2.6196066367932, -1.07206902633681, 1.70077768270329, -1.61531096417457,
0.0827459973313509, -6.82577663153282},
{0.549379141222342, -1.30994855822444, -3.47047538273556,
0.416631880451092, -2.01641324755852, 0.534999953845232,
-0.825543426908553, -1.89592023892521, 5.51877157805828},
{0.0804714249535426, -0.5308079142129, -1.48689873935011,
-1.86763554052357, -0.869089360209786, -1.67763600182079,
1.61348068527877, -1.66550797875971, -0.925481963732789},
{-0.686375033428724, 1.09398610198181, 0.699349709460149,
-1.04209787556848, 0.0477294646540392, -0.311194459626976,
-0.848898627795279, 1.21798575421877, -1.20136465619996},
{0.65672978185887, -2.41522086895727, -0.906588505776888, 1.17488116346046,
0.348225140957002, -1.76790548692959, -1.52203810494393, 1.20010038210504,
2.16681827421459}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.711664725241253, 0.506164178116774, -0.741743336419543,
0.501270635463003, -0.672368683770616, 0.747306441658917,
0.789949973283111}};

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const auto fMin = std::array<simd::float_v, 7>{
{2.32376150961e-05, 1.20999845876e-06, 3.0517578125e-05, 0.000152587890625,
5.18634915352e-05, 3.16649675369e-08, 4964.515625}};
const auto fMax = std::array<simd::float_v, 7>{
{29.999835968, 0.448848098516, 490.75402832, 499.918823242, 1.29696559906,
0.148829773068, 5764.58056641}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 8>, 9>{
{{-13.8767665400575, 4.05734115522388, -3.01709661856028, 1.12334316344471,
-1.95431900429486, -4.28496976296461, 2.12003203912787,
-16.3247309911133},
{0.212048009453922, -15.1738107548058, 16.7279720978323, 7.86809247963017,
-2.44754013164889, 7.7765844954342, -7.1858320802125, 14.8502047053221},
{1.03697617644536, -7.74330829725443, 6.56587047894099, 17.8488797860709,
-6.58256061835055, -14.3326703613101, -4.21591741028686,
-3.48521822531376},
{1.07161857075862, -6.02457375820184, -2.95388380942296, -1.32423877366328,
4.40729929976243, 4.47413261680277, -9.1510537721088, -3.00961301024585},
{-0.483652311202822, 1.61937809966064, 3.0445519571216, 0.815891204469984,
0.474869080905695, 3.43775266744451, -1.25098304071557, 7.12769003125851},
{-8.4010714790805, 8.31810836442086, -3.26991947652379, 1.31844760189238,
-0.316007929405036, -0.703746325371237, 4.74898967505285,
-1.11739245753407},
{-0.592761413330552, 4.04188612003611, -0.218806073885883,
3.90563951642846, 7.09174466959683, -6.3569150742699, -5.14953269394216,
2.75424697228316},
{0.547164481580195, 1.70249203967427, 1.94714702524239, -13.7351709164445,
1.80504850488469, -2.90102696607898, 0.572900917600169, -10.365898528612},
{-1.41297642979771, 1.7421562904492, 1.51246974803507, -0.277205719612539,
-0.746303261257708, 1.31841345876455, -0.315569517202675,
-1.43151946831495}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 10>, 7>{
{{-2.70914120357355, 0.519189852188428, 1.64293953499867, -1.42908155115225,
-0.911252443482285, -3.62723599571144, -3.12039388485614,
-2.24012508264097, -1.80018616467714, -0.387269363887802},
{0.825289573993859, 0.977559873140871, -1.19932065232476,
0.448270358180695, -1.01118687034592, -0.12068624133809, 1.92125679147867,
-1.22870635454816, 1.06194042880088, -1.67985680933482},
{0.117628014226149, -0.666150093594241, -1.96462719830508,
-1.34621345717382, 2.69897179096947, 1.45683981784585, -0.280779666268364,
-1.09056907866035, 0.143585634417832, -0.853077107436903},
{0.343557768966074, -1.36884597467765, -0.978489408664556,
1.04108942352196, 2.38422271469634, -1.42280162989848, -1.24692906453324,
1.16005819097626, -1.81861709989607, 0.792826064358476},
{-2.43543923840386, -0.790741678609659, -0.86057585327147,
-0.560696061368329, -0.546486276970939, -1.10828693920102,
-0.390844170382116, -0.191292459405275, 0.655178595334291,
3.62562636803186},
{-1.85600205994161, -0.851713021005162, -2.36960755021907,
-2.65847940214873, 4.19992558926354, 0.482968294979867,
-0.674617611858262, 0.537074281854966, -1.44013551902026,
0.12897906197469},
{3.05467659680961, -0.835919265923888, -1.97139370203255,
-0.833191777667285, 3.1259995582494, 1.3049178372323, -0.601501165563516,
-0.476449568704171, 0.0595564302057028, 1.86826919022162}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 8>{
{-0.742315179835233, -0.384238828861699, -0.639019653069106,
-0.469522590533314, 0.812934812918375, -0.548705434492968,
1.10784727825793, -1.47828921845706}};

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const auto fMin = std::array<simd::float_v, 6>{
{2.32376150961e-05, 1.51249741975e-06, 3.0517578125e-05, 4.57763671875e-05,
1.30217522383e-05, 9.31322574615e-10}};
const auto fMax =
std::array<simd::float_v, 6>{{29.9999866486, 0.402866601944, 497.675262451,
499.88583374, 1.35172855854, 0.1488314569}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{-0.716890254960393, 5.8069257184991, -1.74563699770656, -1.69375462311209,
0.292600378995007, 4.27627333971203, 5.05829948252536},
{1.39109753193721, -6.17525389654849, 7.57671398067678, -5.43048780303785,
-1.09791116843721, 1.86130825538439, -3.82867359027486},
{-0.463070234910456, -4.56547441068759, 5.40748303002796, 24.3147882327414,
-6.31462696612228, -15.7641466083901, 3.16004633819498},
{0.153443312046544, -13.7240931193717, 12.4658109156892, 3.93975979118258,
-6.11948248810469, 12.0087465863604, 11.8434487900601},
{-5.38333972443605, 7.08960513470396, -14.0225023836695, 1.62191385618879,
-3.70995234249952, -6.21018449120275, -16.3820927289576},
{1.28910616897801, 11.7392825108682, -0.745172957676181, -2.71535399916244,
2.69193347520725, -7.76807154851574, 3.33706974699574},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.69376603852368, 0.713685235953229, 0.537330926797311, 1.24885881426728,
0.849445456302149, 0.0549823762550653, -1.60838065333664}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-3.49743269512971, -1.59190099226759, 2.68952831238107, 1.47409713154181,
-0.358823304868459, 1.51035818148923, 1.63438201788813, -1.37184378061365,
-4.8236951156242},
{-1.62443558899203, 0.637337506470021, -1.81394608796523,
-0.39782822266736, 2.98247880411195, -3.00550692859844, -1.61531096417457,
0.0991975320503116, -7.79260298177481},
{2.63673645224951, 0.769840121669036, -1.81866900675112, -1.22134862739373,
0.671174013434412, -1.47933584039013, -0.825543426908553,
-1.92253219419135, 3.8017813083906},
{0.205195965291138, -0.35698019904733, -1.43178372298118,
-1.86979559465315, -0.819043768918633, -1.72129504552091,
1.61348068527877, -1.66550797875971, -0.957274797031432},
{3.39235161127949, 0.557496083138389, 0.358810791879255, -1.30084105984251,
-0.542916984939091, -0.0267147558240502, -0.848898627795279,
0.771556793635358, 0.0697782536980876},
{0.481340186388348, 0.112198736662793, 2.17905577117167,
-0.602783430688711, -0.0915323075405589, 0.497824854127751,
-1.52203810494393, 1.50364257368639, -0.374485200843083}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.768478620967589, 0.945551538481868, 0.96174226855089, 0.370062157422418,
-0.78327662856066, 0.822576347537717, -0.718860728264376}};

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const auto fMin = std::array<simd::float_v, 6>{
{0.354097932577, 5.52064511794e-06, 0.000244140625, 8.39233398438e-05,
6.46021217108e-05, 3.98140400648e-08}};
const auto fMax = std::array<simd::float_v, 6>{{29.9984798431, 0.343307316303,
487.684082031, 497.415130615,
1.28809189796, 0.148829773068}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{0.528613355828958, -3.98084730501778, -0.592082531501982,
0.138947239841158, -0.778623431382993, -0.581951852087617,
-3.3077751082926},
{0.626191010935061, -6.59632782328807, 9.64286730275841, 9.55716888102903,
-4.21769290858214, -0.877735461418827, 7.66427785912706},
{0.589042763591211, 0.342730710819044, 2.15591537442552, 3.00613486546159,
0.031406906405544, 0.245821626313224, 4.14102878259858},
{0.331983030850774, 0.936730026632873, 5.0246621889186, -8.55182143000926,
-1.36911477615904, -0.62033806094376, -4.12767358459756},
{-1.83087334545531, 1.70659514344126, 1.64680904436349, -3.69383485282499,
-1.60992615163927, -1.33158200933679, -3.68738551321132},
{0.497605072926462, 3.10686068573917, -3.38889852931357, -2.83744183592321,
5.8582848269084, -5.8114650940735, -2.19632367553395},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{2.34124335963039, 0.212194857163335, 0.442598967492635, 1.99142561696414,
0.932043520652152, 0.0950084159057334, -0.343964005014347}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-3.9980933021236, -0.654530522369937, 1.38643691032487, 0.846962243830957,
0.106764765445591, 0.432714049442539, 1.63438201788813, -1.09500118769163,
-0.477330937420509},
{-1.00177046130397, 0.910392283082755, -1.10524270003512,
-0.863367119958066, 0.356000819965252, -1.36464636332376,
-1.61531096417457, -1.07499530514837, 2.02772049025211},
{1.06312654536343, 1.19247984844137, -2.56993344812772, -1.59660765668362,
-1.43473393145022, -2.45597801241373, -0.825543426908553,
-1.66068434492917, 1.54276462560785},
{1.81681515757912, -1.04949680940877, -1.47464408054066, -2.35655553716087,
-0.81674566968838, -2.03350840389647, 1.61348068527877, -1.66550797875971,
-2.15831244577917},
{-1.03932528137019, 1.40966162144001, -1.28446720148786, -1.3440214301115,
-0.764149070532308, -0.346882028973845, -0.848898627795279,
2.00051119462677, 3.35327375607444},
{-1.86664223320468, -2.77494106516727, 0.280364440162091,
-0.51153329496928, 0.099515543403597, -0.231471190430381,
-1.52203810494393, 1.14272217943492, 0.830204232719646}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.707654910623957, 0.947610371696967, -0.734533082005471,
2.92853232573231, -0.764897377620809, 2.76504552610281, 2.01235259703278}};

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const auto fMin = std::array<simd::float_v, 6>{
{0.257591664791, 1.18096104416e-05, 0.000593185424805, 0.00165557861328,
0.00012809690088, 4.9639493227e-07}};
const auto fMax = std::array<simd::float_v, 6>{{29.9983310699, 0.346089184284,
494.445037842, 497.105712891,
1.28034591675, 0.146788269281}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{0.238406879601667, -5.59592601269328, -1.48529518782053,
-1.21815009023291, 1.34269102160607, 1.34969291565497, -4.51875687730105},
{0.396886922398879, -1.55290356333354, 9.68785078213303, 8.92661791228501,
-4.14921556686506, -4.79373464075343, 8.51558304693096},
{-0.605978331887513, -2.01049013335995, 2.42576702923552, 1.52363979902223,
-0.98764665307072, 5.47124537232274, 6.44617285846946},
{0.194697743583909, 1.28944295625644, 7.01265960466827, -8.8098678043251,
-1.29787641608371, -1.01125992648077, -2.62580313202802},
{-0.149097384185005, 0.601644139549549, -3.20384472073729,
-1.11764357962076, 0.661266078420317, -2.99007258105897,
-4.75089443675904},
{-0.0637125691675382, -0.031901246578545, -5.86825160360429,
-6.08669255423129, 6.57894839440667, 1.56562582414305, -2.45567329718821},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{2.16691834156097, 1.28877310398459, -0.0182036429219536, 1.64574682748412,
-1.776462177169, 1.02789865613476, -1.86072790490082}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-4.86220133984369, 0.118835729154668, -0.219969977992415,
0.391324848601455, -1.52700917088122, 1.34069581551041, 1.63438201788813,
-1.46686286855675, 0.828587551619351},
{-2.34704356917816, 1.32098559104381, -0.35321222806336, -2.37018474851075,
-0.428177327276122, -0.598193543229222, -1.61531096417457,
0.788200423431897, 1.42375444061969},
{-0.520599794082693, 1.88897717167843, -0.983200551417999,
-2.10145861332195, 2.58359759649054, -1.9520611743449, -0.825543426908553,
-2.21273436389439, 1.68368588984848},
{0.687372876118682, -0.350871511760717, -1.43005506081713,
-1.86332872620019, -0.805133918174304, -1.70605683547268,
1.61348068527877, -1.66550797875971, -0.80539832878319},
{0.641334100110318, 0.829686404507413, 1.12377545166463, -1.2786548533532,
-2.2652307380297, -0.577326144935801, -0.848898627795279,
-0.112416063323718, 3.09322414387249},
{-2.10459256659739, -2.04968111694632, 0.989486352894292,
-1.53078668929007, -0.90726448865931, 0.837532331802425,
-1.52203810494393, 2.96223264118436, -2.25826102849139}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.632200441072234, 1.49561211302111, -1.13710464066982, 0.45277221100554,
-0.690200710879259, 0.878498633554998, 2.07286062799155}};

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const auto fMin = std::array<simd::float_v, 6>{
{2.32376150961e-05, 1.20999845876e-06, 3.0517578125e-05, 0.000152587890625,
5.18634915352e-05, 3.16649675369e-08}};
const auto fMax = std::array<simd::float_v, 6>{{29.999835968, 0.448848098516,
490.75402832, 499.918823242,
1.29696559906, 0.148829773068}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{0.972643778287334, 0.945437530240695, -1.40069143935294,
-15.6034120045671, 1.14493675557278, 6.76331107008671, -6.58864627844693},
{1.99177578845469, -13.3678019612632, 8.38118795560118, 1.73988710441318,
-4.61454323644065, 5.29554800958296, 1.796743670204},
{0.154471209290507, -6.25196675947653, 5.03239643950246, 17.3659761341648,
-6.54695139344376, -13.0321058473978, -2.79459536100855},
{-1.91255962568079, -8.6500289238652, 11.3312847667967, 13.5402314908838,
-2.61341614761575, 6.63476937311634, 18.5047027165893},
{-13.4902851128642, 5.03927112314943, -7.35289370328568,
0.0572131890099181, -1.6142848069816, -3.07255458814266,
-18.9635216594601},
{1.88222476973218, 6.53087839421258, 2.08080853139342, 0.816872513930955,
1.76981234909237, -8.6501994076645, 3.81699174241397},
{-0.79066972900182, -0.617757099680603, 0.740878002718091,
0.681870030239224, -1.20759406685829, 0.769290467724204,
-1.8437808630988},
{1.96787188749046, 0.680940366397391, 0.050263650384077, 1.68306844400001,
1.12938262301514, 0.122157098634831, -0.887283402159991}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-2.73702380879827, 1.22468365009789, 2.40149928694528, 0.276654711632341,
-0.947460759127638, -0.94795299724562, 1.63438201788813,
-1.41515589667229, -0.708508928627869},
{-0.408168817589508, -0.542699435360695, -0.336829708223667,
-0.507220427829013, 0.533181686353704, -0.0512849135791123,
-1.61531096417457, 0.0991539876010671, 4.00684418941464},
{0.401110123287066, -0.82501422982477, -0.82214087163611,
-2.13310745114762, 0.656608219190029, -1.54611499475089,
-0.825543426908553, -1.92246825444023, -2.49920928064247},
{0.743417630960188, -2.54297207137451, 0.868639896626588, 1.21759484724959,
-0.432278512319556, -0.682439011110067, 1.61348068527877,
-1.70813842427554, 0.191141321065651},
{0.601790057732671, -2.70865568575877, -0.949516903771233,
1.41807664967738, 0.0135866328882364, 1.63463920593405,
-0.848898627795279, 0.794266404867267, -4.68030461730642},
{-0.894524549453373, -0.413420422791491, -1.27841462173856,
-0.921761527738667, 1.7613032977725, -1.20901458126865, -1.52203810494393,
1.63899587513312, 3.18360564985773}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 7>{
{-0.468166794846483, 0.905418443044577, 0.345720533590786,
0.626519340549303, -0.564753919345451, 0.871170117133406,
-2.29725166588317}};

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const auto fMin = std::array<simd::float_v, 7>{
{2.32376150961e-05, 1.20999845876e-06, 3.0517578125e-05, 0.000152587890625,
5.18634915352e-05, 3.16649675369e-08, 1.63267832249e-05}};
const auto fMax = std::array<simd::float_v, 7>{
{29.999835968, 0.448848098516, 490.75402832, 499.918823242, 1.29696559906,
0.148829773068, 1.406919837}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 8>, 9>{
{{-15.5425486721894, 4.46064219760936, -2.34623364306547, 0.673061906567754,
-0.869156572627564, -3.91456514808376, -0.568696256770434,
-16.6632172224501},
{0.447767212299748, -12.0732988541946, 13.5397418382974, 6.88739679435815,
-6.24126921111681, 10.2657097797903, 2.43233624582838, 16.3044055554715},
{0.711332752822416, -7.17479141259481, 6.60735241080743, 17.1002661287198,
-5.66447497808782, -13.5847364290022, -3.2812531600052,
-4.16110866444881},
{0.632252449853337, -0.994201889160893, 0.163028638247136,
0.771845371822938, 1.96713990468425, 3.63340983309008, -1.20631209983256,
-0.448420201049805},
{-0.841164977118048, 9.93038462960693, 2.29748287289709,
-0.0626255430240932, 3.26040532046237, -3.3032557034584,
0.549324748173291, 8.63089145494412},
{-4.64294924610689, -1.03961735354666, -5.94838304383518,
-5.14494916413428, 0.865768755325211, 3.17305862226336, -0.17689672644592,
-11.1702998443119},
{-0.75257412651179, 7.45653016330318, 1.53531423087191, -0.944661904110734,
2.27175825244693, 0.625586633690943, 0.556680865915938, 8.70515377733531},
{1.48517605340595, -1.10139488332919, -1.20437312666678, -15.7567359489487,
0.564551471160599, 0.343355103916556, 0.956188296533458,
-14.4810699542064},
{-1.41297642979771, 1.7421562904492, 1.51246974803507, -0.277205719612539,
-0.746303261257708, 1.31841345876455, -0.315569517202675,
-1.43151946831495}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 10>, 7>{
{{0.249095596049212, 0.43896816611743, 2.51443611518656, -1.99550475508056,
-3.01891555380374, -1.5384309247739, -1.10809432820241, -2.23884147411375,
-1.80018616467714, 0.0926501061367807},
{-0.79810107527313, -0.128565504120936, -1.47898746860618,
-1.98749268865462, -4.1729473774923, -0.319376625137038, 2.68241976233123,
-1.2438721745196, 1.06194042880088, -1.11115934197209},
{-0.541616972751047, -0.883639706603654, -1.21647636736428,
2.00429851976991, -0.333604676335978, -1.30666235698471,
0.300409853048531, 1.71280717271126, 0.143585634417832,
0.862440249952535},
{-0.0738827412712401, 0.710660017309775, -1.81469923323104,
-2.0032894120881, 0.0757757984176355, 0.946471866500602,
-0.862679340246423, -0.336329345694109, -1.81861709989607,
-1.65647777258377},
{-2.46837296738587, -0.892461394707053, -0.164670653708065,
-1.40986988591441, -1.29634197190675, -0.103818171050218,
1.62473520412615, -0.10368342877725, 0.655178595334291, 3.10987357888943},
{-3.51942943078094, 0.05403637176598, -0.112974678381018,
-0.992599640919349, 2.32462754890465, 0.0152632384089371,
-1.55107042088954, -2.78524739346744, -1.44013551902026,
-0.069348300182213},
{3.50273770909445, -0.563785026359985, -0.682273837786807,
0.00116206143253937, 0.0443816144597161, 0.571844608360393,
-1.17322063876001, -1.09420727621842, 0.0595564302057028,
0.887055205865514}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 8>{
{-0.527095695381938, 0.873978759522188, -0.505869602713493,
-0.458736757125275, 1.00063852384923, -0.651233083081496, 1.09109419846381,
-1.55585886153223}};

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const auto fMin = std::array<simd::float_v, 7>{
{2.32376150961e-05, 1.20999845876e-06, 3.0517578125e-05, 0.000152587890625,
5.18634915352e-05, 3.16649675369e-08, 2.91038304567e-11}};
const auto fMax = std::array<simd::float_v, 7>{
{29.999835968, 0.448848098516, 490.75402832, 499.918823242, 1.29696559906,
0.148829773068, 0.000133186404128}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 8>, 9>{
{{-1.92788180969447, 4.16064785412784, -2.11335551271703, 7.9294095607534,
2.18170560740568, -8.44761548627774, -21.5047552584798,
-16.0650884865238},
{-1.61376550856811, -18.3767723062232, 6.29188806075221, 17.1629698975724,
-4.06178649035417, 8.91994724771869, 0.945309347327087, 10.2364214261801},
{0.260725207756956, -7.39063316113963, 5.85798680146154, 20.895198655668,
-5.37769824548582, -14.2293948664243, 0.995342070597369,
1.20800372506884},
{0.953241806012774, 2.47323765132763, 2.08443843097691, -1.43196935568204,
4.74700613459522, 0.189179081361804, -16.9045615453658,
-6.43026395704888},
{-1.92981663032188, -0.37230565268653, 0.814369803792726, 1.73699189907859,
1.11733301402944, 1.46887116928329, 1.49186452419427, -1.29918641902703},
{-16.8338849958765, 6.56643273179019, -3.48428147705122, 0.326903745037315,
-2.06105265356339, -4.41540617406857, 5.02090403276349,
-13.5579467656888},
{-2.06856046873756, 1.37857017711159, -10.4255727807086, 5.40802232507597,
6.86445294409404, 5.35440411482745, -6.85993978444102,
-0.729076014469736},
{-0.526653874830334, 8.98715315712336, -2.34742788084526,
-1.27417058696474, 5.55759129208842, -3.2700796957674, -0.831113531084397,
2.18499951551135},
{-1.41297642979771, 1.7421562904492, 1.51246974803507, -0.277205719612539,
-0.746303261257708, 1.31841345876455, -0.315569517202675,
-1.43151946831495}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 10>, 7>{
{{0.0324469931195793, -0.288230539372084, 1.64983047434275,
-1.24756371282518, -1.94639586807131, -0.310928305245747,
-4.99162520915551, 0.264942892832968, -1.80018616467714,
2.77914512003005},
{-0.0148602437129058, -1.2132748075938, -0.218359722842231,
-0.633592266259126, -1.66464499515867, -2.55247320011507,
0.942074824320476, -1.41987137293765, 1.06194042880088, 3.89059634854256},
{-0.0974156676281787, 1.4515472939941, 1.12169407748122, 0.1569833587188,
0.715433387778868, -2.40068948213013, -1.20271162851859, 1.58722622760245,
0.143585634417832, -0.958611632301647},
{-0.535241107505903, -0.222101479961216, -1.72874348280829,
-1.09357655226657, 1.67832177468419, -1.85229898078416,
-0.879756942942339, 0.0297380421842839, -1.81861709989607,
-0.271711324852575},
{-2.12445796868783, -0.913233265968283, -0.338898758417067,
-1.65257155394075, -1.15348755568266, -0.571688294860023,
-0.590397833605982, -0.152323738308279, 0.655178595334291,
-6.84207556062884e-06},
{-1.95868900053493, -0.605205894790946, -1.36009261632635,
-2.34452772551367, 1.60461574133745, -0.00209217938454121,
0.145219515490194, -3.24026630749251, -1.44013551902026,
10.2107763198695},
{0.384756246095988, -0.392456215033468, -2.59979095776574,
-1.14968086393069, -0.936541749845882, 4.08852696879947,
-0.0319867516820682, -1.98678786024887, 0.0595564302057028,
3.2850148235822}}};
const auto fWeightMatrix2to3 = std::array<simd::float_v, 8>{
{-0.975457561894625, 0.722739660815715, -0.35623550622024, 1.13391106903613,
0.663374242757088, -0.893283186205502, 0.795604576331046,
-1.33372154704332}};

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const auto ResfMin = std::array<simd::float_v, 6>{
{1.20620707094e-05, 2.0063980628e-06, 8.45295653562e-05, 0.000119162104966,
4.75468114018e-05, 4.38088898491e-09}};
const auto ResfMax =
std::array<simd::float_v, 6>{{29.999212265, 0.29415422678, 486.515930176,
499.948669434, 1.293815732, 0.145083397627}};
const auto ResfWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{nan, nan, nan, nan, nan, nan, nan},
{nan, nan, nan, nan, nan, nan, nan},
{nan, nan, nan, nan, nan, nan, nan},
{nan, nan, nan, nan, nan, nan, nan},
{nan, nan, nan, nan, nan, nan, nan},
{nan, nan, nan, nan, nan, nan, nan},
{nan, nan, nan, nan, nan, nan, nan},
{nan, nan, nan, nan, nan, nan, nan}}};
const auto ResfWeightMatrix1to2 = std::array<simd::float_v, 9>{
{-nan, -nan, -nan, -nan, -nan, -nan, -nan, -nan, -nan}};

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outputs_nn/output_B.txt Normal file
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: Parsing option string:
: ... "V:!Silent:Color:DrawProgressBar:AnalysisType=Classification"
: The following options are set:
: - By User:
: V: "True" [Verbose flag]
: Color: "True" [Flag for coloured screen output (default: True, if in batch mode: False)]
: Silent: "False" [Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)]
: DrawProgressBar: "True" [Draw progress bar to display training, testing and evaluation schedule (default: True)]
: AnalysisType: "Classification" [Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)]
: - Default:
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Transformations: "I" [List of transformations to test; formatting example: "Transformations=I;D;P;U;G,D", for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations]
: Correlations: "False" [boolean to show correlation in output]
: ROC: "True" [boolean to show ROC in output]
: ModelPersistence: "True" [Option to save the trained model in xml file or using serialization]
DataSetInfo : [MatchNNDataSet] : Added class "Signal"
: Add Tree Signal of type Signal with 187767 events
DataSetInfo : [MatchNNDataSet] : Added class "Background"
: Add Tree Bkg of type Background with 14040318 events
: Dataset[MatchNNDataSet] : Class index : 0 name : Signal
: Dataset[MatchNNDataSet] : Class index : 1 name : Background
Factory : Booking method: matching_mlp
:
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: NCycles: "700" [Number of training cycles]
: HiddenLayers: "N+2,N" [Specification of hidden layer architecture]
: NeuronType: "ReLU" [Neuron activation function type]
: EstimatorType: "CE" [MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood]
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "Norm" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: TrainingMethod: "BP" [Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)]
: LearningRate: "2.000000e-02" [ANN learning rate parameter]
: DecayRate: "1.000000e-02" [Decay rate for learning parameter]
: TestRate: "50" [Test for overtraining performed at each #th epochs]
: Sampling: "1.000000e+00" [Only 'Sampling' (randomly selected) events are trained each epoch]
: SamplingImportance: "1.000000e+00" [ The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided.]
: UseRegulator: "False" [Use regulator to avoid over-training]
: - Default:
: RandomSeed: "1" [Random seed for initial synapse weights (0 means unique seed for each run; default value '1')]
: NeuronInputType: "sum" [Neuron input function type]
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: EpochMonitoring: "False" [Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)]
: SamplingEpoch: "1.000000e+00" [Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training]
: SamplingTraining: "True" [The training sample is sampled]
: SamplingTesting: "False" [The testing sample is sampled]
: ResetStep: "50" [How often BFGS should reset history]
: Tau: "3.000000e+00" [LineSearch "size step"]
: BPMode: "sequential" [Back-propagation learning mode: sequential or batch]
: BatchSize: "-1" [Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events]
: ConvergenceImprove: "1.000000e-30" [Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)]
: ConvergenceTests: "-1" [Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)]
: UpdateLimit: "10000" [Maximum times of regulator update]
: CalculateErrors: "False" [Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value]
: WeightRange: "1.000000e+00" [Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range]
matching_mlp : [MatchNNDataSet] : Create Transformation "Norm" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
matching_mlp : Building Network.
: Initializing weights
Factory : Train all methods
: Rebuilding Dataset MatchNNDataSet
: Parsing option string:
: ... "SplitMode=random:V:nTrain_Signal=100000.0:nTrain_Background=200000.0:nTest_Signal=10000.0:nTest_Background=20000.0"
: The following options are set:
: - By User:
: SplitMode: "Random" [Method of picking training and testing events (default: random)]
: nTrain_Signal: "100000" [Number of training events of class Signal (default: 0 = all)]
: nTest_Signal: "10000" [Number of test events of class Signal (default: 0 = all)]
: nTrain_Background: "200000" [Number of training events of class Background (default: 0 = all)]
: nTest_Background: "20000" [Number of test events of class Background (default: 0 = all)]
: V: "True" [Verbosity (default: true)]
: - Default:
: MixMode: "SameAsSplitMode" [Method of mixing events of different classes into one dataset (default: SameAsSplitMode)]
: SplitSeed: "100" [Seed for random event shuffling]
: NormMode: "EqualNumEvents" [Overall renormalisation of event-by-event weights used in the training (NumEvents: average weight of 1 per event, independently for signal and background; EqualNumEvents: average weight of 1 per event for signal, and sum of weights for background equal to sum of weights for signal)]
: ScaleWithPreselEff: "False" [Scale the number of requested events by the eff. of the preselection cuts (or not)]
: TrainTestSplit_Signal: "0.000000e+00" [Number of test events of class Signal (default: 0 = all)]
: TrainTestSplit_Background: "0.000000e+00" [Number of test events of class Background (default: 0 = all)]
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Correlations: "True" [Boolean to show correlation output (Default: true)]
: CalcCorrelations: "True" [Compute correlations and also some variable statistics, e.g. min/max (Default: true )]
: Building event vectors for type 2 Signal
: Dataset[MatchNNDataSet] : create input formulas for tree Signal
: Building event vectors for type 2 Background
: Dataset[MatchNNDataSet] : create input formulas for tree Bkg
DataSetFactory : [MatchNNDataSet] : Number of events in input trees
:
:
: Dataset[MatchNNDataSet] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ...
: Dataset[MatchNNDataSet] : such that the effective (weighted) number of events in each class is the same
: Dataset[MatchNNDataSet] : (and equals the number of events (entries) given for class=0 )
: Dataset[MatchNNDataSet] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ...
: Dataset[MatchNNDataSet] : ... (note that N_j is the sum of TRAINING events
: Dataset[MatchNNDataSet] : ..... Testing events are not renormalised nor included in the renormalisation factor!)
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 100000
: Signal -- testing events : 10000
: Signal -- training and testing events: 110000
: Background -- training events : 200000
: Background -- testing events : 20000
: Background -- training and testing events: 220000
:
DataSetInfo : Correlation matrix (Signal):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.094 +0.508 +0.558 +0.393 +0.145
: teta2: -0.094 +1.000 -0.010 +0.345 -0.010 +0.388
: distX: +0.508 -0.010 +1.000 +0.202 +0.501 +0.230
: distY: +0.558 +0.345 +0.202 +1.000 +0.507 +0.472
: dSlope: +0.393 -0.010 +0.501 +0.507 +1.000 +0.497
: dSlopeY: +0.145 +0.388 +0.230 +0.472 +0.497 +1.000
: --------------------------------------------------------
DataSetInfo : Correlation matrix (Background):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 +0.008 +0.363 +0.312 -0.001 +0.102
: teta2: +0.008 +1.000 +0.217 +0.626 +0.297 +0.493
: distX: +0.363 +0.217 +1.000 +0.062 +0.631 +0.203
: distY: +0.312 +0.626 +0.062 +1.000 +0.250 +0.543
: dSlope: -0.001 +0.297 +0.631 +0.250 +1.000 +0.361
: dSlopeY: +0.102 +0.493 +0.203 +0.543 +0.361 +1.000
: --------------------------------------------------------
DataSetFactory : [MatchNNDataSet] :
:
Factory : [MatchNNDataSet] : Create Transformation "I" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 8.4293 9.2426 [ 2.3238e-05 30.000 ]
: teta2: 0.0057581 0.014094 [ 1.5125e-06 0.40287 ]
: distX: 40.107 55.141 [ 3.0518e-05 497.68 ]
: distY: 26.294 37.024 [ 4.5776e-05 499.89 ]
: dSlope: 0.33133 0.23520 [ 1.3022e-05 1.3517 ]
: dSlopeY: 0.0054522 0.0092106 [ 9.3132e-10 0.14883 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
IdTransformation : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Separation
: --------------------------------
: 1 : chi2 : 5.690e-01
: 2 : distX : 3.736e-01
: 3 : distY : 2.091e-01
: 4 : dSlopeY : 8.232e-02
: 5 : dSlope : 8.601e-03
: 6 : teta2 : 3.474e-03
: --------------------------------
Factory : Train method: matching_mlp for Classification
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.43805 0.61618 [ -1.0000 1.0000 ]
: teta2: -0.97142 0.069969 [ -1.0000 1.0000 ]
: distX: -0.83882 0.22159 [ -1.0000 1.0000 ]
: distY: -0.89480 0.14813 [ -1.0000 1.0000 ]
: dSlope: -0.50978 0.34801 [ -1.0000 1.0000 ]
: dSlopeY: -0.92673 0.12377 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Training Network
:
: Elapsed time for training with 300000 events: 853 sec
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (300000 events)
: Elapsed time for evaluation of 300000 events: 0.495 sec
: Creating xml weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
: Creating standalone class: MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C
: Write special histos to file: matching_ghost_mlp_training.root:/MatchNNDataSet/Method_MLP/matching_mlp
Factory : Training finished
:
: Ranking input variables (method specific)...
matching_mlp : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Importance
: --------------------------------
: 1 : distY : 5.213e+02
: 2 : teta2 : 4.435e+02
: 3 : dSlopeY : 4.414e+02
: 4 : distX : 3.118e+02
: 5 : dSlope : 2.646e+01
: 6 : chi2 : 7.066e+00
: --------------------------------
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
matching_mlp : Building Network.
: Initializing weights
Factory : Test all methods
Factory : Test method: matching_mlp for Classification performance
:
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on testing sample (30000 events)
: Elapsed time for evaluation of 30000 events: 0.0597 sec
Factory : Evaluate all methods
Factory : Evaluate classifier: matching_mlp
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.29449 0.63524 [ -0.99999 0.99994 ]
: teta2: -0.97212 0.070073 [ -1.0000 0.32779 ]
: distX: -0.80346 0.24082 [ -1.0000 0.97553 ]
: distY: -0.87751 0.16136 [ -1.0000 0.95680 ]
: dSlope: -0.50293 0.35312 [ -0.99999 0.86422 ]
: dSlopeY: -0.91903 0.13042 [ -1.0000 0.95486 ]
: -----------------------------------------------------------
matching_mlp : [MatchNNDataSet] : Loop over test events and fill histograms with classifier response...
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.29449 0.63524 [ -0.99999 0.99994 ]
: teta2: -0.97212 0.070073 [ -1.0000 0.32779 ]
: distX: -0.80346 0.24082 [ -1.0000 0.97553 ]
: distY: -0.87751 0.16136 [ -1.0000 0.95680 ]
: dSlope: -0.50293 0.35312 [ -0.99999 0.86422 ]
: dSlopeY: -0.91903 0.13042 [ -1.0000 0.95486 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: MatchNNDataSet matching_mlp : 0.958
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: MatchNNDataSet matching_mlp : 0.450 (0.414) 0.886 (0.882) 0.980 (0.979)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:MatchNNDataSet : Created tree 'TestTree' with 30000 events
:
Dataset:MatchNNDataSet : Created tree 'TrainTree' with 300000 events
:
Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
Transforming nn_electron_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C ...
Found minimum and maximum values for 6 variables.
Found 3 matrices:
1. fWeightMatrix0to1 with 7 columns and 8 rows
2. fWeightMatrix1to2 with 9 columns and 6 rows
3. fWeightMatrix2to3 with 7 columns and 1 rows

268
outputs_nn/output_B_res.txt Normal file
View File

@ -0,0 +1,268 @@
: Parsing option string:
: ... "V:!Silent:Color:DrawProgressBar:AnalysisType=Classification"
: The following options are set:
: - By User:
: V: "True" [Verbose flag]
: Color: "True" [Flag for coloured screen output (default: True, if in batch mode: False)]
: Silent: "False" [Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)]
: DrawProgressBar: "True" [Draw progress bar to display training, testing and evaluation schedule (default: True)]
: AnalysisType: "Classification" [Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)]
: - Default:
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Transformations: "I" [List of transformations to test; formatting example: "Transformations=I;D;P;U;G,D", for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations]
: Correlations: "False" [boolean to show correlation in output]
: ROC: "True" [boolean to show ROC in output]
: ModelPersistence: "True" [Option to save the trained model in xml file or using serialization]
DataSetInfo : [MatchNNDataSet] : Added class "Signal"
: Add Tree Signal of type Signal with 7718 events
DataSetInfo : [MatchNNDataSet] : Added class "Background"
: Add Tree Bkg of type Background with 11895204 events
: Dataset[MatchNNDataSet] : Class index : 0 name : Signal
: Dataset[MatchNNDataSet] : Class index : 1 name : Background
Factory : Booking method: matching_mlp
:
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: NCycles: "700" [Number of training cycles]
: HiddenLayers: "N+2,N" [Specification of hidden layer architecture]
: NeuronType: "ReLU" [Neuron activation function type]
: EstimatorType: "CE" [MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood]
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "Norm" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: TrainingMethod: "BP" [Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)]
: LearningRate: "2.000000e-02" [ANN learning rate parameter]
: DecayRate: "1.000000e-02" [Decay rate for learning parameter]
: TestRate: "50" [Test for overtraining performed at each #th epochs]
: Sampling: "1.000000e+00" [Only 'Sampling' (randomly selected) events are trained each epoch]
: SamplingImportance: "1.000000e+00" [ The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided.]
: UseRegulator: "False" [Use regulator to avoid over-training]
: - Default:
: RandomSeed: "1" [Random seed for initial synapse weights (0 means unique seed for each run; default value '1')]
: NeuronInputType: "sum" [Neuron input function type]
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: EpochMonitoring: "False" [Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)]
: SamplingEpoch: "1.000000e+00" [Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training]
: SamplingTraining: "True" [The training sample is sampled]
: SamplingTesting: "False" [The testing sample is sampled]
: ResetStep: "50" [How often BFGS should reset history]
: Tau: "3.000000e+00" [LineSearch "size step"]
: BPMode: "sequential" [Back-propagation learning mode: sequential or batch]
: BatchSize: "-1" [Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events]
: ConvergenceImprove: "1.000000e-30" [Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)]
: ConvergenceTests: "-1" [Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)]
: UpdateLimit: "10000" [Maximum times of regulator update]
: CalculateErrors: "False" [Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value]
: WeightRange: "1.000000e+00" [Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range]
matching_mlp : [MatchNNDataSet] : Create Transformation "Norm" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
matching_mlp : Building Network.
: Initializing weights
Factory : Train all methods
: Rebuilding Dataset MatchNNDataSet
: Parsing option string:
: ... "SplitMode=random:V:nTrain_Signal=0:nTrain_Background=20000.0:nTest_Signal=1000.0:nTest_Background=5000.0"
: The following options are set:
: - By User:
: SplitMode: "Random" [Method of picking training and testing events (default: random)]
: nTrain_Signal: "0" [Number of training events of class Signal (default: 0 = all)]
: nTest_Signal: "1000" [Number of test events of class Signal (default: 0 = all)]
: nTrain_Background: "20000" [Number of training events of class Background (default: 0 = all)]
: nTest_Background: "5000" [Number of test events of class Background (default: 0 = all)]
: V: "True" [Verbosity (default: true)]
: - Default:
: MixMode: "SameAsSplitMode" [Method of mixing events of different classes into one dataset (default: SameAsSplitMode)]
: SplitSeed: "100" [Seed for random event shuffling]
: NormMode: "EqualNumEvents" [Overall renormalisation of event-by-event weights used in the training (NumEvents: average weight of 1 per event, independently for signal and background; EqualNumEvents: average weight of 1 per event for signal, and sum of weights for background equal to sum of weights for signal)]
: ScaleWithPreselEff: "False" [Scale the number of requested events by the eff. of the preselection cuts (or not)]
: TrainTestSplit_Signal: "0.000000e+00" [Number of test events of class Signal (default: 0 = all)]
: TrainTestSplit_Background: "0.000000e+00" [Number of test events of class Background (default: 0 = all)]
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Correlations: "True" [Boolean to show correlation output (Default: true)]
: CalcCorrelations: "True" [Compute correlations and also some variable statistics, e.g. min/max (Default: true )]
: Building event vectors for type 2 Signal
: Dataset[MatchNNDataSet] : create input formulas for tree Signal
: Building event vectors for type 2 Background
: Dataset[MatchNNDataSet] : create input formulas for tree Bkg
DataSetFactory : [MatchNNDataSet] : Number of events in input trees
:
:
: Dataset[MatchNNDataSet] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ...
: Dataset[MatchNNDataSet] : such that the effective (weighted) number of events in each class is the same
: Dataset[MatchNNDataSet] : (and equals the number of events (entries) given for class=0 )
: Dataset[MatchNNDataSet] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ...
: Dataset[MatchNNDataSet] : ... (note that N_j is the sum of TRAINING events
: Dataset[MatchNNDataSet] : ..... Testing events are not renormalised nor included in the renormalisation factor!)
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 6718
: Signal -- testing events : 1000
: Signal -- training and testing events: 7718
: Background -- training events : 20000
: Background -- testing events : 5000
: Background -- training and testing events: 25000
:
DataSetInfo : Correlation matrix (Signal):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.083 +0.248 +0.242 +0.206 +0.042
: teta2: -0.083 +1.000 +0.038 +0.508 +0.191 +0.637
: distX: +0.248 +0.038 +1.000 -0.175 +0.681 +0.107
: distY: +0.242 +0.508 -0.175 +1.000 +0.349 +0.484
: dSlope: +0.206 +0.191 +0.681 +0.349 +1.000 +0.349
: dSlopeY: +0.042 +0.637 +0.107 +0.484 +0.349 +1.000
: --------------------------------------------------------
DataSetInfo : Correlation matrix (Background):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.024 +0.242 +0.209 +0.046 +0.055
: teta2: -0.024 +1.000 +0.245 +0.652 +0.371 +0.483
: distX: +0.242 +0.245 +1.000 +0.017 +0.776 +0.198
: distY: +0.209 +0.652 +0.017 +1.000 +0.312 +0.554
: dSlope: +0.046 +0.371 +0.776 +0.312 +1.000 +0.392
: dSlopeY: +0.055 +0.483 +0.198 +0.554 +0.392 +1.000
: --------------------------------------------------------
DataSetFactory : [MatchNNDataSet] :
:
Factory : [MatchNNDataSet] : Create Transformation "I" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 14.879 7.6783 [ 0.35410 29.998 ]
: teta2: 0.0053594 0.015677 [ 5.5206e-06 0.34331 ]
: distX: 74.975 63.347 [ 0.00024414 487.68 ]
: distY: 35.490 43.750 [ 8.3923e-05 497.42 ]
: dSlope: 0.35788 0.24459 [ 6.4602e-05 1.2881 ]
: dSlopeY: 0.0073112 0.012369 [ 3.9814e-08 0.14883 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
IdTransformation : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Separation
: --------------------------------
: 1 : chi2 : 9.921e-02
: 2 : distY : 8.773e-02
: 3 : dSlopeY : 2.784e-02
: 4 : teta2 : 2.748e-02
: 5 : dSlope : 2.662e-02
: 6 : distX : 1.420e-02
: --------------------------------
Factory : Train method: matching_mlp for Classification
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.020078 0.51803 [ -1.0000 1.0000 ]
: teta2: -0.96881 0.091329 [ -1.0000 1.0000 ]
: distX: -0.69253 0.25979 [ -1.0000 1.0000 ]
: distY: -0.85730 0.17591 [ -1.0000 1.0000 ]
: dSlope: -0.44439 0.37979 [ -1.0000 1.0000 ]
: dSlopeY: -0.90175 0.16622 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Training Network
:
: Elapsed time for training with 26718 events: 57.7 sec
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (26718 events)
: Elapsed time for evaluation of 26718 events: 0.0346 sec
: Creating xml weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
: Creating standalone class: MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C
: Write special histos to file: matching_ghost_mlp_training.root:/MatchNNDataSet/Method_MLP/matching_mlp
Factory : Training finished
:
: Ranking input variables (method specific)...
matching_mlp : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Importance
: --------------------------------
: 1 : distY : 1.467e+02
: 2 : teta2 : 6.884e+01
: 3 : distX : 6.627e+01
: 4 : dSlopeY : 3.066e+01
: 5 : dSlope : 1.175e+01
: 6 : chi2 : 2.632e+00
: --------------------------------
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
matching_mlp : Building Network.
: Initializing weights
Factory : Test all methods
Factory : Test method: matching_mlp for Classification performance
:
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on testing sample (6000 events)
: Elapsed time for evaluation of 6000 events: 0.0118 sec
Factory : Evaluate all methods
Factory : Evaluate classifier: matching_mlp
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 0.10881 0.51711 [ -1.0020 0.99902 ]
: teta2: -0.96093 0.10865 [ -0.99988 0.46950 ]
: distX: -0.67673 0.27337 [ -0.99968 0.75285 ]
: distY: -0.82663 0.20236 [ -0.99997 0.83868 ]
: dSlope: -0.46394 0.38477 [ -0.99839 0.97924 ]
: dSlopeY: -0.89235 0.16561 [ -1.0000 0.93883 ]
: -----------------------------------------------------------
matching_mlp : [MatchNNDataSet] : Loop over test events and fill histograms with classifier response...
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 0.10881 0.51711 [ -1.0020 0.99902 ]
: teta2: -0.96093 0.10865 [ -0.99988 0.46950 ]
: distX: -0.67673 0.27337 [ -0.99968 0.75285 ]
: distY: -0.82663 0.20236 [ -0.99997 0.83868 ]
: dSlope: -0.46394 0.38477 [ -0.99839 0.97924 ]
: dSlopeY: -0.89235 0.16561 [ -1.0000 0.93883 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: MatchNNDataSet matching_mlp : 0.842
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: MatchNNDataSet matching_mlp : 0.075 (0.082) 0.476 (0.467) 0.841 (0.828)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:MatchNNDataSet : Created tree 'TestTree' with 6000 events
:
Dataset:MatchNNDataSet : Created tree 'TrainTree' with 26718 events
:
Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
Transforming nn_electron_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C ...
Found minimum and maximum values for 6 variables.
Found 3 matrices:
1. fWeightMatrix0to1 with 7 columns and 8 rows
2. fWeightMatrix1to2 with 9 columns and 6 rows
3. fWeightMatrix2to3 with 7 columns and 1 rows

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: Parsing option string:
: ... "V:!Silent:Color:DrawProgressBar:AnalysisType=Classification"
: The following options are set:
: - By User:
: V: "True" [Verbose flag]
: Color: "True" [Flag for coloured screen output (default: True, if in batch mode: False)]
: Silent: "False" [Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)]
: DrawProgressBar: "True" [Draw progress bar to display training, testing and evaluation schedule (default: True)]
: AnalysisType: "Classification" [Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)]
: - Default:
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Transformations: "I" [List of transformations to test; formatting example: "Transformations=I;D;P;U;G,D", for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations]
: Correlations: "False" [boolean to show correlation in output]
: ROC: "True" [boolean to show ROC in output]
: ModelPersistence: "True" [Option to save the trained model in xml file or using serialization]
DataSetInfo : [MatchNNDataSet] : Added class "Signal"
: Add Tree Signal of type Signal with 8286 events
DataSetInfo : [MatchNNDataSet] : Added class "Background"
: Add Tree Bkg of type Background with 12762964 events
: Dataset[MatchNNDataSet] : Class index : 0 name : Signal
: Dataset[MatchNNDataSet] : Class index : 1 name : Background
Factory : Booking method: matching_mlp
:
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: NCycles: "700" [Number of training cycles]
: HiddenLayers: "N+2,N" [Specification of hidden layer architecture]
: NeuronType: "ReLU" [Neuron activation function type]
: EstimatorType: "CE" [MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood]
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "Norm" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: TrainingMethod: "BP" [Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)]
: LearningRate: "2.000000e-02" [ANN learning rate parameter]
: DecayRate: "1.000000e-02" [Decay rate for learning parameter]
: TestRate: "50" [Test for overtraining performed at each #th epochs]
: Sampling: "1.000000e+00" [Only 'Sampling' (randomly selected) events are trained each epoch]
: SamplingImportance: "1.000000e+00" [ The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided.]
: UseRegulator: "False" [Use regulator to avoid over-training]
: - Default:
: RandomSeed: "1" [Random seed for initial synapse weights (0 means unique seed for each run; default value '1')]
: NeuronInputType: "sum" [Neuron input function type]
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: EpochMonitoring: "False" [Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)]
: SamplingEpoch: "1.000000e+00" [Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training]
: SamplingTraining: "True" [The training sample is sampled]
: SamplingTesting: "False" [The testing sample is sampled]
: ResetStep: "50" [How often BFGS should reset history]
: Tau: "3.000000e+00" [LineSearch "size step"]
: BPMode: "sequential" [Back-propagation learning mode: sequential or batch]
: BatchSize: "-1" [Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events]
: ConvergenceImprove: "1.000000e-30" [Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)]
: ConvergenceTests: "-1" [Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)]
: UpdateLimit: "10000" [Maximum times of regulator update]
: CalculateErrors: "False" [Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value]
: WeightRange: "1.000000e+00" [Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range]
matching_mlp : [MatchNNDataSet] : Create Transformation "Norm" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
matching_mlp : Building Network.
: Initializing weights
Factory : Train all methods
: Rebuilding Dataset MatchNNDataSet
: Parsing option string:
: ... "SplitMode=random:V:nTrain_Signal=0:nTrain_Background=20000.0:nTest_Signal=1000.0:nTest_Background=5000.0"
: The following options are set:
: - By User:
: SplitMode: "Random" [Method of picking training and testing events (default: random)]
: nTrain_Signal: "0" [Number of training events of class Signal (default: 0 = all)]
: nTest_Signal: "1000" [Number of test events of class Signal (default: 0 = all)]
: nTrain_Background: "20000" [Number of training events of class Background (default: 0 = all)]
: nTest_Background: "5000" [Number of test events of class Background (default: 0 = all)]
: V: "True" [Verbosity (default: true)]
: - Default:
: MixMode: "SameAsSplitMode" [Method of mixing events of different classes into one dataset (default: SameAsSplitMode)]
: SplitSeed: "100" [Seed for random event shuffling]
: NormMode: "EqualNumEvents" [Overall renormalisation of event-by-event weights used in the training (NumEvents: average weight of 1 per event, independently for signal and background; EqualNumEvents: average weight of 1 per event for signal, and sum of weights for background equal to sum of weights for signal)]
: ScaleWithPreselEff: "False" [Scale the number of requested events by the eff. of the preselection cuts (or not)]
: TrainTestSplit_Signal: "0.000000e+00" [Number of test events of class Signal (default: 0 = all)]
: TrainTestSplit_Background: "0.000000e+00" [Number of test events of class Background (default: 0 = all)]
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Correlations: "True" [Boolean to show correlation output (Default: true)]
: CalcCorrelations: "True" [Compute correlations and also some variable statistics, e.g. min/max (Default: true )]
: Building event vectors for type 2 Signal
: Dataset[MatchNNDataSet] : create input formulas for tree Signal
: Building event vectors for type 2 Background
: Dataset[MatchNNDataSet] : create input formulas for tree Bkg
DataSetFactory : [MatchNNDataSet] : Number of events in input trees
:
:
: Dataset[MatchNNDataSet] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ...
: Dataset[MatchNNDataSet] : such that the effective (weighted) number of events in each class is the same
: Dataset[MatchNNDataSet] : (and equals the number of events (entries) given for class=0 )
: Dataset[MatchNNDataSet] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ...
: Dataset[MatchNNDataSet] : ... (note that N_j is the sum of TRAINING events
: Dataset[MatchNNDataSet] : ..... Testing events are not renormalised nor included in the renormalisation factor!)
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 7286
: Signal -- testing events : 1000
: Signal -- training and testing events: 8286
: Background -- training events : 20000
: Background -- testing events : 5000
: Background -- training and testing events: 25000
:
DataSetInfo : Correlation matrix (Signal):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.090 +0.190 +0.270 +0.150 +0.032
: teta2: -0.090 +1.000 +0.022 +0.557 +0.231 +0.681
: distX: +0.190 +0.022 +1.000 -0.243 +0.667 +0.066
: distY: +0.270 +0.557 -0.243 +1.000 +0.299 +0.491
: dSlope: +0.150 +0.231 +0.667 +0.299 +1.000 +0.343
: dSlopeY: +0.032 +0.681 +0.066 +0.491 +0.343 +1.000
: --------------------------------------------------------
DataSetInfo : Correlation matrix (Background):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.032 +0.249 +0.208 +0.048 +0.047
: teta2: -0.032 +1.000 +0.256 +0.643 +0.377 +0.464
: distX: +0.249 +0.256 +1.000 +0.027 +0.771 +0.192
: distY: +0.208 +0.643 +0.027 +1.000 +0.323 +0.556
: dSlope: +0.048 +0.377 +0.771 +0.323 +1.000 +0.394
: dSlopeY: +0.047 +0.464 +0.192 +0.556 +0.394 +1.000
: --------------------------------------------------------
DataSetFactory : [MatchNNDataSet] :
:
Factory : [MatchNNDataSet] : Create Transformation "I" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 15.110 7.5957 [ 0.25759 29.998 ]
: teta2: 0.0049007 0.015613 [ 1.1810e-05 0.34609 ]
: distX: 77.540 64.030 [ 0.00059319 494.45 ]
: distY: 35.596 43.128 [ 0.0016556 497.11 ]
: dSlope: 0.37313 0.24282 [ 0.00012810 1.2803 ]
: dSlopeY: 0.0071048 0.011434 [ 4.9639e-07 0.14679 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
IdTransformation : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Separation
: --------------------------------
: 1 : chi2 : 8.701e-02
: 2 : distY : 7.455e-02
: 3 : dSlope : 6.957e-02
: 4 : teta2 : 4.316e-02
: 5 : dSlopeY : 2.562e-02
: 6 : distX : 1.371e-02
: --------------------------------
Factory : Train method: matching_mlp for Classification
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.0011851 0.51079 [ -1.0000 1.0000 ]
: teta2: -0.97175 0.090226 [ -1.0000 1.0000 ]
: distX: -0.68636 0.25900 [ -1.0000 1.0000 ]
: distY: -0.85679 0.17352 [ -1.0000 1.0000 ]
: dSlope: -0.41728 0.37935 [ -1.0000 1.0000 ]
: dSlopeY: -0.90320 0.15579 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Training Network
:
: Elapsed time for training with 27286 events: 59.2 sec
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (27286 events)
: Elapsed time for evaluation of 27286 events: 0.0331 sec
: Creating xml weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
: Creating standalone class: MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C
: Write special histos to file: matching_ghost_mlp_training.root:/MatchNNDataSet/Method_MLP/matching_mlp
Factory : Training finished
:
: Ranking input variables (method specific)...
matching_mlp : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Importance
: --------------------------------
: 1 : distY : 1.487e+02
: 2 : distX : 9.251e+01
: 3 : dSlopeY : 5.612e+01
: 4 : teta2 : 3.951e+01
: 5 : dSlope : 1.219e+01
: 6 : chi2 : 1.428e+00
: --------------------------------
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
matching_mlp : Building Network.
: Initializing weights
Factory : Test all methods
Factory : Test method: matching_mlp for Classification performance
:
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on testing sample (6000 events)
: Elapsed time for evaluation of 6000 events: 0.0113 sec
Factory : Evaluate all methods
Factory : Evaluate classifier: matching_mlp
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 0.10129 0.51080 [ -0.98564 0.99991 ]
: teta2: -0.96473 0.096760 [ -0.99997 0.43123 ]
: distX: -0.68127 0.26859 [ -0.99983 0.92711 ]
: distY: -0.83124 0.20417 [ -0.99994 1.0115 ]
: dSlope: -0.45660 0.39080 [ -0.99695 0.96415 ]
: dSlopeY: -0.89629 0.16201 [ -0.99999 1.0015 ]
: -----------------------------------------------------------
matching_mlp : [MatchNNDataSet] : Loop over test events and fill histograms with classifier response...
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 0.10129 0.51080 [ -0.98564 0.99991 ]
: teta2: -0.96473 0.096760 [ -0.99997 0.43123 ]
: distX: -0.68127 0.26859 [ -0.99983 0.92711 ]
: distY: -0.83124 0.20417 [ -0.99994 1.0115 ]
: dSlope: -0.45660 0.39080 [ -0.99695 0.96415 ]
: dSlopeY: -0.89629 0.16201 [ -0.99999 1.0015 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: MatchNNDataSet matching_mlp : 0.854
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: MatchNNDataSet matching_mlp : 0.091 (0.089) 0.501 (0.494) 0.851 (0.854)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:MatchNNDataSet : Created tree 'TestTree' with 6000 events
:
Dataset:MatchNNDataSet : Created tree 'TrainTree' with 27286 events
:
Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
Transforming nn_electron_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C ...
Found minimum and maximum values for 6 variables.
Found 3 matrices:
1. fWeightMatrix0to1 with 7 columns and 8 rows
2. fWeightMatrix1to2 with 9 columns and 6 rows
3. fWeightMatrix2to3 with 7 columns and 1 rows

268
outputs_nn/output_both.txt Normal file
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@ -0,0 +1,268 @@
: Parsing option string:
: ... "V:!Silent:Color:DrawProgressBar:AnalysisType=Classification"
: The following options are set:
: - By User:
: V: "True" [Verbose flag]
: Color: "True" [Flag for coloured screen output (default: True, if in batch mode: False)]
: Silent: "False" [Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)]
: DrawProgressBar: "True" [Draw progress bar to display training, testing and evaluation schedule (default: True)]
: AnalysisType: "Classification" [Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)]
: - Default:
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Transformations: "I" [List of transformations to test; formatting example: "Transformations=I;D;P;U;G,D", for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations]
: Correlations: "False" [boolean to show correlation in output]
: ROC: "True" [boolean to show ROC in output]
: ModelPersistence: "True" [Option to save the trained model in xml file or using serialization]
DataSetInfo : [MatchNNDataSet] : Added class "Signal"
: Add Tree Signal of type Signal with 13829 events
DataSetInfo : [MatchNNDataSet] : Added class "Background"
: Add Tree Bkg of type Background with 29144752 events
: Dataset[MatchNNDataSet] : Class index : 0 name : Signal
: Dataset[MatchNNDataSet] : Class index : 1 name : Background
Factory : Booking method: matching_mlp
:
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: NCycles: "700" [Number of training cycles]
: HiddenLayers: "N+2,N" [Specification of hidden layer architecture]
: NeuronType: "ReLU" [Neuron activation function type]
: EstimatorType: "CE" [MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood]
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "Norm" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: TrainingMethod: "BP" [Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)]
: LearningRate: "2.000000e-02" [ANN learning rate parameter]
: DecayRate: "1.000000e-02" [Decay rate for learning parameter]
: TestRate: "50" [Test for overtraining performed at each #th epochs]
: Sampling: "1.000000e+00" [Only 'Sampling' (randomly selected) events are trained each epoch]
: SamplingImportance: "1.000000e+00" [ The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided.]
: UseRegulator: "False" [Use regulator to avoid over-training]
: - Default:
: RandomSeed: "1" [Random seed for initial synapse weights (0 means unique seed for each run; default value '1')]
: NeuronInputType: "sum" [Neuron input function type]
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: EpochMonitoring: "False" [Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)]
: SamplingEpoch: "1.000000e+00" [Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training]
: SamplingTraining: "True" [The training sample is sampled]
: SamplingTesting: "False" [The testing sample is sampled]
: ResetStep: "50" [How often BFGS should reset history]
: Tau: "3.000000e+00" [LineSearch "size step"]
: BPMode: "sequential" [Back-propagation learning mode: sequential or batch]
: BatchSize: "-1" [Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events]
: ConvergenceImprove: "1.000000e-30" [Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)]
: ConvergenceTests: "-1" [Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)]
: UpdateLimit: "10000" [Maximum times of regulator update]
: CalculateErrors: "False" [Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value]
: WeightRange: "1.000000e+00" [Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range]
matching_mlp : [MatchNNDataSet] : Create Transformation "Norm" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
matching_mlp : Building Network.
: Initializing weights
Factory : Train all methods
: Rebuilding Dataset MatchNNDataSet
: Parsing option string:
: ... "SplitMode=random:V:nTrain_Signal=0:nTrain_Background=20000.0:nTest_Signal=2000.0:nTest_Background=5000.0"
: The following options are set:
: - By User:
: SplitMode: "Random" [Method of picking training and testing events (default: random)]
: nTrain_Signal: "0" [Number of training events of class Signal (default: 0 = all)]
: nTest_Signal: "2000" [Number of test events of class Signal (default: 0 = all)]
: nTrain_Background: "20000" [Number of training events of class Background (default: 0 = all)]
: nTest_Background: "5000" [Number of test events of class Background (default: 0 = all)]
: V: "True" [Verbosity (default: true)]
: - Default:
: MixMode: "SameAsSplitMode" [Method of mixing events of different classes into one dataset (default: SameAsSplitMode)]
: SplitSeed: "100" [Seed for random event shuffling]
: NormMode: "EqualNumEvents" [Overall renormalisation of event-by-event weights used in the training (NumEvents: average weight of 1 per event, independently for signal and background; EqualNumEvents: average weight of 1 per event for signal, and sum of weights for background equal to sum of weights for signal)]
: ScaleWithPreselEff: "False" [Scale the number of requested events by the eff. of the preselection cuts (or not)]
: TrainTestSplit_Signal: "0.000000e+00" [Number of test events of class Signal (default: 0 = all)]
: TrainTestSplit_Background: "0.000000e+00" [Number of test events of class Background (default: 0 = all)]
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Correlations: "True" [Boolean to show correlation output (Default: true)]
: CalcCorrelations: "True" [Compute correlations and also some variable statistics, e.g. min/max (Default: true )]
: Building event vectors for type 2 Signal
: Dataset[MatchNNDataSet] : create input formulas for tree Signal
: Building event vectors for type 2 Background
: Dataset[MatchNNDataSet] : create input formulas for tree Bkg
DataSetFactory : [MatchNNDataSet] : Number of events in input trees
:
:
: Dataset[MatchNNDataSet] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ...
: Dataset[MatchNNDataSet] : such that the effective (weighted) number of events in each class is the same
: Dataset[MatchNNDataSet] : (and equals the number of events (entries) given for class=0 )
: Dataset[MatchNNDataSet] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ...
: Dataset[MatchNNDataSet] : ... (note that N_j is the sum of TRAINING events
: Dataset[MatchNNDataSet] : ..... Testing events are not renormalised nor included in the renormalisation factor!)
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 11829
: Signal -- testing events : 2000
: Signal -- training and testing events: 13829
: Background -- training events : 20000
: Background -- testing events : 5000
: Background -- training and testing events: 25000
:
DataSetInfo : Correlation matrix (Signal):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.082 +0.200 +0.302 +0.182 +0.049
: teta2: -0.082 +1.000 +0.033 +0.461 +0.179 +0.632
: distX: +0.200 +0.033 +1.000 -0.222 +0.685 +0.075
: distY: +0.302 +0.461 -0.222 +1.000 +0.306 +0.463
: dSlope: +0.182 +0.179 +0.685 +0.306 +1.000 +0.319
: dSlopeY: +0.049 +0.632 +0.075 +0.463 +0.319 +1.000
: --------------------------------------------------------
DataSetInfo : Correlation matrix (Background):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.003 +0.368 +0.313 -0.005 +0.094
: teta2: -0.003 +1.000 +0.215 +0.617 +0.302 +0.491
: distX: +0.368 +0.215 +1.000 +0.065 +0.633 +0.203
: distY: +0.313 +0.617 +0.065 +1.000 +0.246 +0.532
: dSlope: -0.005 +0.302 +0.633 +0.246 +1.000 +0.356
: dSlopeY: +0.094 +0.491 +0.203 +0.532 +0.356 +1.000
: --------------------------------------------------------
DataSetFactory : [MatchNNDataSet] :
:
Factory : [MatchNNDataSet] : Create Transformation "I" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 13.817 7.9796 [ 0.0011579 29.997 ]
: teta2: 0.0040130 0.012209 [ 1.9755e-06 0.23492 ]
: distX: 71.018 61.492 [ 0.0031776 478.62 ]
: distY: 31.234 37.327 [ 0.00019073 497.26 ]
: dSlope: 0.37346 0.23976 [ 5.9959e-05 1.2822 ]
: dSlopeY: 0.0063004 0.010258 [ 3.9814e-08 0.14883 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
IdTransformation : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Separation
: --------------------------------
: 1 : chi2 : 9.147e-02
: 2 : distY : 5.407e-02
: 3 : teta2 : 4.044e-02
: 4 : dSlope : 3.233e-02
: 5 : distX : 2.801e-02
: 6 : dSlopeY : 1.699e-02
: --------------------------------
Factory : Train method: matching_mlp for Classification
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.078822 0.53204 [ -1.0000 1.0000 ]
: teta2: -0.96585 0.10395 [ -1.0000 1.0000 ]
: distX: -0.70325 0.25696 [ -1.0000 1.0000 ]
: distY: -0.87438 0.15013 [ -1.0000 1.0000 ]
: dSlope: -0.41755 0.37399 [ -1.0000 1.0000 ]
: dSlopeY: -0.91533 0.13785 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Training Network
:
: Elapsed time for training with 31829 events: 64.5 sec
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (31829 events)
: Elapsed time for evaluation of 31829 events: 0.0391 sec
: Creating xml weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
: Creating standalone class: MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C
: Write special histos to file: matching_ghost_mlp_training.root:/MatchNNDataSet/Method_MLP/matching_mlp
Factory : Training finished
:
: Ranking input variables (method specific)...
matching_mlp : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Importance
: --------------------------------
: 1 : distY : 3.588e+02
: 2 : dSlopeY : 2.134e+02
: 3 : distX : 1.426e+02
: 4 : teta2 : 7.020e+01
: 5 : dSlope : 1.303e+01
: 6 : chi2 : 3.098e+00
: --------------------------------
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
matching_mlp : Building Network.
: Initializing weights
Factory : Test all methods
Factory : Test method: matching_mlp for Classification performance
:
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on testing sample (7000 events)
: Elapsed time for evaluation of 7000 events: 0.0138 sec
Factory : Evaluate all methods
Factory : Evaluate classifier: matching_mlp
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.055433 0.55630 [ -0.99875 1.0001 ]
: teta2: -0.96118 0.10498 [ -0.99999 0.45981 ]
: distX: -0.71039 0.26310 [ -0.99989 0.79697 ]
: distY: -0.86095 0.16028 [ -1.0000 0.89878 ]
: dSlope: -0.43538 0.38054 [ -0.99815 0.98969 ]
: dSlopeY: -0.91076 0.14080 [ -1.0000 0.93883 ]
: -----------------------------------------------------------
matching_mlp : [MatchNNDataSet] : Loop over test events and fill histograms with classifier response...
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.055433 0.55630 [ -0.99875 1.0001 ]
: teta2: -0.96118 0.10498 [ -0.99999 0.45981 ]
: distX: -0.71039 0.26310 [ -0.99989 0.79697 ]
: distY: -0.86095 0.16028 [ -1.0000 0.89878 ]
: dSlope: -0.43538 0.38054 [ -0.99815 0.98969 ]
: dSlopeY: -0.91076 0.14080 [ -1.0000 0.93883 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: MatchNNDataSet matching_mlp : 0.853
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: MatchNNDataSet matching_mlp : 0.000 (0.000) 0.470 (0.511) 0.877 (0.882)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:MatchNNDataSet : Created tree 'TestTree' with 7000 events
:
Dataset:MatchNNDataSet : Created tree 'TrainTree' with 31829 events
:
Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
Transforming nn_electron_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C ...
Found minimum and maximum values for 6 variables.
Found 3 matrices:
1. fWeightMatrix0to1 with 7 columns and 8 rows
2. fWeightMatrix1to2 with 9 columns and 6 rows
3. fWeightMatrix2to3 with 7 columns and 1 rows

268
outputs_nn/output_e_B.txt Normal file
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@ -0,0 +1,268 @@
: Parsing option string:
: ... "V:!Silent:Color:DrawProgressBar:AnalysisType=Classification"
: The following options are set:
: - By User:
: V: "True" [Verbose flag]
: Color: "True" [Flag for coloured screen output (default: True, if in batch mode: False)]
: Silent: "False" [Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)]
: DrawProgressBar: "True" [Draw progress bar to display training, testing and evaluation schedule (default: True)]
: AnalysisType: "Classification" [Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)]
: - Default:
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Transformations: "I" [List of transformations to test; formatting example: "Transformations=I;D;P;U;G,D", for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations]
: Correlations: "False" [boolean to show correlation in output]
: ROC: "True" [boolean to show ROC in output]
: ModelPersistence: "True" [Option to save the trained model in xml file or using serialization]
DataSetInfo : [MatchNNDataSet] : Added class "Signal"
: Add Tree Signal of type Signal with 187767 events
DataSetInfo : [MatchNNDataSet] : Added class "Background"
: Add Tree Bkg of type Background with 14040318 events
: Dataset[MatchNNDataSet] : Class index : 0 name : Signal
: Dataset[MatchNNDataSet] : Class index : 1 name : Background
Factory : Booking method: matching_mlp
:
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: NCycles: "700" [Number of training cycles]
: HiddenLayers: "N+2,N" [Specification of hidden layer architecture]
: NeuronType: "ReLU" [Neuron activation function type]
: EstimatorType: "CE" [MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood]
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "Norm" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: TrainingMethod: "BP" [Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)]
: LearningRate: "2.000000e-02" [ANN learning rate parameter]
: DecayRate: "1.000000e-02" [Decay rate for learning parameter]
: TestRate: "50" [Test for overtraining performed at each #th epochs]
: Sampling: "1.000000e+00" [Only 'Sampling' (randomly selected) events are trained each epoch]
: SamplingImportance: "1.000000e+00" [ The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided.]
: UseRegulator: "False" [Use regulator to avoid over-training]
: - Default:
: RandomSeed: "1" [Random seed for initial synapse weights (0 means unique seed for each run; default value '1')]
: NeuronInputType: "sum" [Neuron input function type]
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: EpochMonitoring: "False" [Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)]
: SamplingEpoch: "1.000000e+00" [Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training]
: SamplingTraining: "True" [The training sample is sampled]
: SamplingTesting: "False" [The testing sample is sampled]
: ResetStep: "50" [How often BFGS should reset history]
: Tau: "3.000000e+00" [LineSearch "size step"]
: BPMode: "sequential" [Back-propagation learning mode: sequential or batch]
: BatchSize: "-1" [Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events]
: ConvergenceImprove: "1.000000e-30" [Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)]
: ConvergenceTests: "-1" [Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)]
: UpdateLimit: "10000" [Maximum times of regulator update]
: CalculateErrors: "False" [Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value]
: WeightRange: "1.000000e+00" [Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range]
matching_mlp : [MatchNNDataSet] : Create Transformation "Norm" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
matching_mlp : Building Network.
: Initializing weights
Factory : Train all methods
: Rebuilding Dataset MatchNNDataSet
: Parsing option string:
: ... "SplitMode=random:V:nTrain_Signal=50000.0:nTrain_Background=500000.0:nTest_Signal=20000.0:nTest_Background=100000.0"
: The following options are set:
: - By User:
: SplitMode: "Random" [Method of picking training and testing events (default: random)]
: nTrain_Signal: "50000" [Number of training events of class Signal (default: 0 = all)]
: nTest_Signal: "20000" [Number of test events of class Signal (default: 0 = all)]
: nTrain_Background: "500000" [Number of training events of class Background (default: 0 = all)]
: nTest_Background: "100000" [Number of test events of class Background (default: 0 = all)]
: V: "True" [Verbosity (default: true)]
: - Default:
: MixMode: "SameAsSplitMode" [Method of mixing events of different classes into one dataset (default: SameAsSplitMode)]
: SplitSeed: "100" [Seed for random event shuffling]
: NormMode: "EqualNumEvents" [Overall renormalisation of event-by-event weights used in the training (NumEvents: average weight of 1 per event, independently for signal and background; EqualNumEvents: average weight of 1 per event for signal, and sum of weights for background equal to sum of weights for signal)]
: ScaleWithPreselEff: "False" [Scale the number of requested events by the eff. of the preselection cuts (or not)]
: TrainTestSplit_Signal: "0.000000e+00" [Number of test events of class Signal (default: 0 = all)]
: TrainTestSplit_Background: "0.000000e+00" [Number of test events of class Background (default: 0 = all)]
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Correlations: "True" [Boolean to show correlation output (Default: true)]
: CalcCorrelations: "True" [Compute correlations and also some variable statistics, e.g. min/max (Default: true )]
: Building event vectors for type 2 Signal
: Dataset[MatchNNDataSet] : create input formulas for tree Signal
: Building event vectors for type 2 Background
: Dataset[MatchNNDataSet] : create input formulas for tree Bkg
DataSetFactory : [MatchNNDataSet] : Number of events in input trees
:
:
: Dataset[MatchNNDataSet] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ...
: Dataset[MatchNNDataSet] : such that the effective (weighted) number of events in each class is the same
: Dataset[MatchNNDataSet] : (and equals the number of events (entries) given for class=0 )
: Dataset[MatchNNDataSet] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ...
: Dataset[MatchNNDataSet] : ... (note that N_j is the sum of TRAINING events
: Dataset[MatchNNDataSet] : ..... Testing events are not renormalised nor included in the renormalisation factor!)
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 50000
: Signal -- testing events : 20000
: Signal -- training and testing events: 70000
: Background -- training events : 500000
: Background -- testing events : 100000
: Background -- training and testing events: 600000
:
DataSetInfo : Correlation matrix (Signal):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.094 +0.511 +0.560 +0.392 +0.141
: teta2: -0.094 +1.000 -0.010 +0.336 -0.009 +0.390
: distX: +0.511 -0.010 +1.000 +0.200 +0.501 +0.229
: distY: +0.560 +0.336 +0.200 +1.000 +0.505 +0.456
: dSlope: +0.392 -0.009 +0.501 +0.505 +1.000 +0.494
: dSlopeY: +0.141 +0.390 +0.229 +0.456 +0.494 +1.000
: --------------------------------------------------------
DataSetInfo : Correlation matrix (Background):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 +0.006 +0.360 +0.312 -0.004 +0.103
: teta2: +0.006 +1.000 +0.218 +0.626 +0.297 +0.487
: distX: +0.360 +0.218 +1.000 +0.065 +0.633 +0.205
: distY: +0.312 +0.626 +0.065 +1.000 +0.250 +0.538
: dSlope: -0.004 +0.297 +0.633 +0.250 +1.000 +0.358
: dSlopeY: +0.103 +0.487 +0.205 +0.538 +0.358 +1.000
: --------------------------------------------------------
DataSetFactory : [MatchNNDataSet] :
:
Factory : [MatchNNDataSet] : Create Transformation "I" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 8.4488 9.2446 [ 5.2334e-05 30.000 ]
: teta2: 0.0057495 0.014113 [ 1.2564e-06 0.42331 ]
: distX: 40.154 55.148 [ 4.3869e-05 499.60 ]
: distY: 26.206 36.751 [ 1.9073e-06 499.20 ]
: dSlope: 0.33045 0.23497 [ 4.7125e-07 1.3693 ]
: dSlopeY: 0.0054210 0.0091700 [ 1.0245e-08 0.14939 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
IdTransformation : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Separation
: --------------------------------
: 1 : chi2 : 5.701e-01
: 2 : distX : 3.731e-01
: 3 : distY : 2.108e-01
: 4 : dSlopeY : 8.367e-02
: 5 : dSlope : 8.157e-03
: 6 : teta2 : 3.280e-03
: --------------------------------
Factory : Train method: matching_mlp for Classification
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.43675 0.61631 [ -1.0000 1.0000 ]
: teta2: -0.97284 0.066677 [ -1.0000 1.0000 ]
: distX: -0.83926 0.22077 [ -1.0000 1.0000 ]
: distY: -0.89501 0.14724 [ -1.0000 1.0000 ]
: dSlope: -0.51734 0.34321 [ -1.0000 1.0000 ]
: dSlopeY: -0.92743 0.12276 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Training Network
:
: Elapsed time for training with 550000 events: 1.28e+03 sec
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (550000 events)
: Elapsed time for evaluation of 550000 events: 0.743 sec
: Creating xml weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
: Creating standalone class: MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C
: Write special histos to file: matching_ghost_mlp_training.root:/MatchNNDataSet/Method_MLP/matching_mlp
Factory : Training finished
:
: Ranking input variables (method specific)...
matching_mlp : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Importance
: --------------------------------
: 1 : distY : 3.418e+02
: 2 : dSlopeY : 2.969e+02
: 3 : teta2 : 2.257e+02
: 4 : distX : 2.089e+02
: 5 : dSlope : 1.525e+01
: 6 : chi2 : 3.953e+00
: --------------------------------
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
matching_mlp : Building Network.
: Initializing weights
Factory : Test all methods
Factory : Test method: matching_mlp for Classification performance
:
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on testing sample (120000 events)
: Elapsed time for evaluation of 120000 events: 0.165 sec
Factory : Evaluate all methods
Factory : Evaluate classifier: matching_mlp
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.15704 0.62353 [ -1.0000 0.99999 ]
: teta2: -0.97379 0.069648 [ -0.99999 0.49021 ]
: distX: -0.76831 0.25194 [ -1.0000 0.99861 ]
: distY: -0.86100 0.17259 [ -1.0000 0.99055 ]
: dSlope: -0.50135 0.35288 [ -1.0000 0.94915 ]
: dSlopeY: -0.91297 0.13474 [ -1.0000 0.99972 ]
: -----------------------------------------------------------
matching_mlp : [MatchNNDataSet] : Loop over test events and fill histograms with classifier response...
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.15704 0.62353 [ -1.0000 0.99999 ]
: teta2: -0.97379 0.069648 [ -0.99999 0.49021 ]
: distX: -0.76831 0.25194 [ -1.0000 0.99861 ]
: distY: -0.86100 0.17259 [ -1.0000 0.99055 ]
: dSlope: -0.50135 0.35288 [ -1.0000 0.94915 ]
: dSlopeY: -0.91297 0.13474 [ -1.0000 0.99972 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: MatchNNDataSet matching_mlp : 0.958
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: MatchNNDataSet matching_mlp : 0.434 (0.435) 0.889 (0.888) 0.982 (0.982)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:MatchNNDataSet : Created tree 'TestTree' with 120000 events
:
Dataset:MatchNNDataSet : Created tree 'TrainTree' with 550000 events
:
Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
Transforming nn_electron_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C ...
Found minimum and maximum values for 6 variables.
Found 3 matrices:
1. fWeightMatrix0to1 with 7 columns and 8 rows
2. fWeightMatrix1to2 with 9 columns and 6 rows
3. fWeightMatrix2to3 with 7 columns and 1 rows

280
outputs_nn/output_n_B.txt Normal file
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@ -0,0 +1,280 @@
: Parsing option string:
: ... "V:!Silent:Color:DrawProgressBar:AnalysisType=Classification"
: The following options are set:
: - By User:
: V: "True" [Verbose flag]
: Color: "True" [Flag for coloured screen output (default: True, if in batch mode: False)]
: Silent: "False" [Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)]
: DrawProgressBar: "True" [Draw progress bar to display training, testing and evaluation schedule (default: True)]
: AnalysisType: "Classification" [Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)]
: - Default:
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Transformations: "I" [List of transformations to test; formatting example: "Transformations=I;D;P;U;G,D", for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations]
: Correlations: "False" [boolean to show correlation in output]
: ROC: "True" [boolean to show ROC in output]
: ModelPersistence: "True" [Option to save the trained model in xml file or using serialization]
DataSetInfo : [MatchNNDataSet] : Added class "Signal"
: Add Tree Signal of type Signal with 2175608 events
DataSetInfo : [MatchNNDataSet] : Added class "Background"
: Add Tree Bkg of type Background with 14040318 events
: Dataset[MatchNNDataSet] : Class index : 0 name : Signal
: Dataset[MatchNNDataSet] : Class index : 1 name : Background
Factory : Booking method: matching_mlp
:
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: NCycles: "700" [Number of training cycles]
: HiddenLayers: "N+2,N" [Specification of hidden layer architecture]
: NeuronType: "ReLU" [Neuron activation function type]
: EstimatorType: "CE" [MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood]
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "Norm" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: TrainingMethod: "BP" [Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)]
: LearningRate: "2.000000e-02" [ANN learning rate parameter]
: DecayRate: "1.000000e-02" [Decay rate for learning parameter]
: TestRate: "50" [Test for overtraining performed at each #th epochs]
: Sampling: "1.000000e+00" [Only 'Sampling' (randomly selected) events are trained each epoch]
: SamplingImportance: "1.000000e+00" [ The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided.]
: UseRegulator: "False" [Use regulator to avoid over-training]
: - Default:
: RandomSeed: "1" [Random seed for initial synapse weights (0 means unique seed for each run; default value '1')]
: NeuronInputType: "sum" [Neuron input function type]
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: EpochMonitoring: "False" [Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)]
: SamplingEpoch: "1.000000e+00" [Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training]
: SamplingTraining: "True" [The training sample is sampled]
: SamplingTesting: "False" [The testing sample is sampled]
: ResetStep: "50" [How often BFGS should reset history]
: Tau: "3.000000e+00" [LineSearch "size step"]
: BPMode: "sequential" [Back-propagation learning mode: sequential or batch]
: BatchSize: "-1" [Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events]
: ConvergenceImprove: "1.000000e-30" [Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)]
: ConvergenceTests: "-1" [Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)]
: UpdateLimit: "10000" [Maximum times of regulator update]
: CalculateErrors: "False" [Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value]
: WeightRange: "1.000000e+00" [Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range]
matching_mlp : [MatchNNDataSet] : Create Transformation "Norm" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
matching_mlp : Building Network.
: Initializing weights
Factory : Train all methods
: Rebuilding Dataset MatchNNDataSet
: Parsing option string:
: ... "SplitMode=random:V:nTrain_Signal=50000.0:nTrain_Background=500000.0:nTest_Signal=20000.0:nTest_Background=100000.0"
: The following options are set:
: - By User:
: SplitMode: "Random" [Method of picking training and testing events (default: random)]
: nTrain_Signal: "50000" [Number of training events of class Signal (default: 0 = all)]
: nTest_Signal: "20000" [Number of test events of class Signal (default: 0 = all)]
: nTrain_Background: "500000" [Number of training events of class Background (default: 0 = all)]
: nTest_Background: "100000" [Number of test events of class Background (default: 0 = all)]
: V: "True" [Verbosity (default: true)]
: - Default:
: MixMode: "SameAsSplitMode" [Method of mixing events of different classes into one dataset (default: SameAsSplitMode)]
: SplitSeed: "100" [Seed for random event shuffling]
: NormMode: "EqualNumEvents" [Overall renormalisation of event-by-event weights used in the training (NumEvents: average weight of 1 per event, independently for signal and background; EqualNumEvents: average weight of 1 per event for signal, and sum of weights for background equal to sum of weights for signal)]
: ScaleWithPreselEff: "False" [Scale the number of requested events by the eff. of the preselection cuts (or not)]
: TrainTestSplit_Signal: "0.000000e+00" [Number of test events of class Signal (default: 0 = all)]
: TrainTestSplit_Background: "0.000000e+00" [Number of test events of class Background (default: 0 = all)]
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Correlations: "True" [Boolean to show correlation output (Default: true)]
: CalcCorrelations: "True" [Compute correlations and also some variable statistics, e.g. min/max (Default: true )]
: Building event vectors for type 2 Signal
: Dataset[MatchNNDataSet] : create input formulas for tree Signal
: Building event vectors for type 2 Background
: Dataset[MatchNNDataSet] : create input formulas for tree Bkg
DataSetFactory : [MatchNNDataSet] : Number of events in input trees
: Dataset[MatchNNDataSet] : Signal requirement: "chi2<15 && distX<250 && distY<400 && dSlope<1.5 && dSlopeY<0.15"
: Dataset[MatchNNDataSet] : Signal -- number of events passed: 2151182 / sum of weights: 2.15118e+06
: Dataset[MatchNNDataSet] : Signal -- efficiency : 0.988773
: Dataset[MatchNNDataSet] : Background requirement: "chi2<15 && distX<250 && distY<400 && dSlope<1.5 && dSlopeY<0.15"
: Dataset[MatchNNDataSet] : Background -- number of events passed: 7175761 / sum of weights: 7.17576e+06
: Dataset[MatchNNDataSet] : Background -- efficiency : 0.511083
: Dataset[MatchNNDataSet] : you have opted for interpreting the requested number of training/testing events
: to be the number of events AFTER your preselection cuts
:
: Dataset[MatchNNDataSet] : you have opted for interpreting the requested number of training/testing events
: to be the number of events AFTER your preselection cuts
:
: Dataset[MatchNNDataSet] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ...
: Dataset[MatchNNDataSet] : such that the effective (weighted) number of events in each class is the same
: Dataset[MatchNNDataSet] : (and equals the number of events (entries) given for class=0 )
: Dataset[MatchNNDataSet] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ...
: Dataset[MatchNNDataSet] : ... (note that N_j is the sum of TRAINING events
: Dataset[MatchNNDataSet] : ..... Testing events are not renormalised nor included in the renormalisation factor!)
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 50000
: Signal -- testing events : 20000
: Signal -- training and testing events: 70000
: Dataset[MatchNNDataSet] : Signal -- due to the preselection a scaling factor has been applied to the numbers of requested events: 0.988773
: Background -- training events : 500000
: Background -- testing events : 100000
: Background -- training and testing events: 600000
: Dataset[MatchNNDataSet] : Background -- due to the preselection a scaling factor has been applied to the numbers of requested events: 0.511083
:
DataSetInfo : Correlation matrix (Signal):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 +0.197 +0.512 +0.603 +0.392 +0.418
: teta2: +0.197 +1.000 +0.456 +0.649 +0.399 +0.581
: distX: +0.512 +0.456 +1.000 +0.445 +0.555 +0.606
: distY: +0.603 +0.649 +0.445 +1.000 +0.529 +0.568
: dSlope: +0.392 +0.399 +0.555 +0.529 +1.000 +0.647
: dSlopeY: +0.418 +0.581 +0.606 +0.568 +0.647 +1.000
: --------------------------------------------------------
DataSetInfo : Correlation matrix (Background):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 +0.001 +0.370 +0.305 +0.002 +0.084
: teta2: +0.001 +1.000 +0.173 +0.650 +0.280 +0.455
: distX: +0.370 +0.173 +1.000 +0.043 +0.627 +0.195
: distY: +0.305 +0.650 +0.043 +1.000 +0.240 +0.458
: dSlope: +0.002 +0.280 +0.627 +0.240 +1.000 +0.362
: dSlopeY: +0.084 +0.455 +0.195 +0.458 +0.362 +1.000
: --------------------------------------------------------
DataSetFactory : [MatchNNDataSet] :
:
Factory : [MatchNNDataSet] : Create Transformation "I" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 4.1539 4.6460 [ 1.3264e-05 15.000 ]
: teta2: 0.0079252 0.017224 [ 1.2100e-06 0.43619 ]
: distX: 27.109 38.586 [ 3.8147e-06 250.00 ]
: distY: 20.564 28.494 [ 1.5259e-05 399.49 ]
: dSlope: 0.28782 0.22814 [ 2.2016e-06 1.3026 ]
: dSlopeY: 0.0054782 0.0099926 [ 1.8626e-09 0.14834 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
IdTransformation : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Separation
: --------------------------------
: 1 : chi2 : 6.095e-01
: 2 : distX : 4.727e-01
: 3 : distY : 1.428e-01
: 4 : dSlope : 7.613e-02
: 5 : dSlopeY : 5.967e-02
: 6 : teta2 : 5.937e-02
: --------------------------------
Factory : Train method: matching_mlp for Classification
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.44615 0.61947 [ -1.0000 1.0000 ]
: teta2: -0.96367 0.078975 [ -1.0000 1.0000 ]
: distX: -0.78313 0.30869 [ -1.0000 1.0000 ]
: distY: -0.89705 0.14265 [ -1.0000 1.0000 ]
: dSlope: -0.55809 0.35029 [ -1.0000 1.0000 ]
: dSlopeY: -0.92614 0.13472 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Training Network
:
: Elapsed time for training with 550000 events: 1.28e+03 sec
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (550000 events)
: Elapsed time for evaluation of 550000 events: 0.785 sec
: Creating xml weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
: Creating standalone class: MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C
: Write special histos to file: matching_ghost_mlp_training.root:/MatchNNDataSet/Method_MLP/matching_mlp
Factory : Training finished
:
: Ranking input variables (method specific)...
matching_mlp : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Importance
: --------------------------------
: 1 : teta2 : 7.346e+02
: 2 : distX : 1.891e+02
: 3 : dSlopeY : 5.900e+01
: 4 : distY : 4.639e+01
: 5 : dSlope : 1.074e+01
: 6 : chi2 : 2.093e+00
: --------------------------------
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
matching_mlp : Building Network.
: Initializing weights
Factory : Test all methods
Factory : Test method: matching_mlp for Classification performance
:
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on testing sample (120000 events)
: Elapsed time for evaluation of 120000 events: 0.169 sec
Factory : Evaluate all methods
Factory : Evaluate classifier: matching_mlp
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.15813 0.62626 [ -1.0000 0.99995 ]
: teta2: -0.97140 0.071651 [ -1.0000 0.85686 ]
: distX: -0.67477 0.35263 [ -1.0000 0.99866 ]
: distY: -0.87476 0.15583 [ -1.0000 0.99415 ]
: dSlope: -0.49635 0.36886 [ -0.99993 0.97372 ]
: dSlopeY: -0.91980 0.13029 [ -1.0000 1.0219 ]
: -----------------------------------------------------------
matching_mlp : [MatchNNDataSet] : Loop over test events and fill histograms with classifier response...
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.15813 0.62626 [ -1.0000 0.99995 ]
: teta2: -0.97140 0.071651 [ -1.0000 0.85686 ]
: distX: -0.67477 0.35263 [ -1.0000 0.99866 ]
: distY: -0.87476 0.15583 [ -1.0000 0.99415 ]
: dSlope: -0.49635 0.36886 [ -0.99993 0.97372 ]
: dSlopeY: -0.91980 0.13029 [ -1.0000 1.0219 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: MatchNNDataSet matching_mlp : 0.970
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: MatchNNDataSet matching_mlp : 0.543 (0.551) 0.936 (0.936) 0.985 (0.985)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:MatchNNDataSet : Created tree 'TestTree' with 120000 events
:
Dataset:MatchNNDataSet : Created tree 'TrainTree' with 550000 events
:
Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
Transforming neural_net_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C ...
Found minimum and maximum values for 6 variables.
Found 3 matrices:
1. fWeightMatrix0to1 with 7 columns and 8 rows
2. fWeightMatrix1to2 with 9 columns and 6 rows
3. fWeightMatrix2to3 with 7 columns and 1 rows

268
outputs_nn/output_og_weights_B.txt Normal file
View File

@ -0,0 +1,268 @@
: Parsing option string:
: ... "V:!Silent:Color:DrawProgressBar:AnalysisType=Classification"
: The following options are set:
: - By User:
: V: "True" [Verbose flag]
: Color: "True" [Flag for coloured screen output (default: True, if in batch mode: False)]
: Silent: "False" [Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)]
: DrawProgressBar: "True" [Draw progress bar to display training, testing and evaluation schedule (default: True)]
: AnalysisType: "Classification" [Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)]
: - Default:
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Transformations: "I" [List of transformations to test; formatting example: "Transformations=I;D;P;U;G,D", for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations]
: Correlations: "False" [boolean to show correlation in output]
: ROC: "True" [boolean to show ROC in output]
: ModelPersistence: "True" [Option to save the trained model in xml file or using serialization]
DataSetInfo : [MatchNNDataSet] : Added class "Signal"
: Add Tree Signal of type Signal with 6590 events
DataSetInfo : [MatchNNDataSet] : Added class "Background"
: Add Tree Bkg of type Background with 14040318 events
: Dataset[MatchNNDataSet] : Class index : 0 name : Signal
: Dataset[MatchNNDataSet] : Class index : 1 name : Background
Factory : Booking method: matching_mlp
:
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: NCycles: "700" [Number of training cycles]
: HiddenLayers: "N+2,N" [Specification of hidden layer architecture]
: NeuronType: "ReLU" [Neuron activation function type]
: EstimatorType: "CE" [MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood]
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "Norm" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: TrainingMethod: "BP" [Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)]
: LearningRate: "2.000000e-02" [ANN learning rate parameter]
: DecayRate: "1.000000e-02" [Decay rate for learning parameter]
: TestRate: "50" [Test for overtraining performed at each #th epochs]
: Sampling: "1.000000e+00" [Only 'Sampling' (randomly selected) events are trained each epoch]
: SamplingImportance: "1.000000e+00" [ The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided.]
: UseRegulator: "False" [Use regulator to avoid over-training]
: - Default:
: RandomSeed: "1" [Random seed for initial synapse weights (0 means unique seed for each run; default value '1')]
: NeuronInputType: "sum" [Neuron input function type]
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: EpochMonitoring: "False" [Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)]
: SamplingEpoch: "1.000000e+00" [Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training]
: SamplingTraining: "True" [The training sample is sampled]
: SamplingTesting: "False" [The testing sample is sampled]
: ResetStep: "50" [How often BFGS should reset history]
: Tau: "3.000000e+00" [LineSearch "size step"]
: BPMode: "sequential" [Back-propagation learning mode: sequential or batch]
: BatchSize: "-1" [Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events]
: ConvergenceImprove: "1.000000e-30" [Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)]
: ConvergenceTests: "-1" [Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)]
: UpdateLimit: "10000" [Maximum times of regulator update]
: CalculateErrors: "False" [Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value]
: WeightRange: "1.000000e+00" [Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range]
matching_mlp : [MatchNNDataSet] : Create Transformation "Norm" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
matching_mlp : Building Network.
: Initializing weights
Factory : Train all methods
: Rebuilding Dataset MatchNNDataSet
: Parsing option string:
: ... "SplitMode=random:V:nTrain_Signal=0:nTrain_Background=200000.0:nTest_Signal=1000.0:nTest_Background=50000.0"
: The following options are set:
: - By User:
: SplitMode: "Random" [Method of picking training and testing events (default: random)]
: nTrain_Signal: "0" [Number of training events of class Signal (default: 0 = all)]
: nTest_Signal: "1000" [Number of test events of class Signal (default: 0 = all)]
: nTrain_Background: "200000" [Number of training events of class Background (default: 0 = all)]
: nTest_Background: "50000" [Number of test events of class Background (default: 0 = all)]
: V: "True" [Verbosity (default: true)]
: - Default:
: MixMode: "SameAsSplitMode" [Method of mixing events of different classes into one dataset (default: SameAsSplitMode)]
: SplitSeed: "100" [Seed for random event shuffling]
: NormMode: "EqualNumEvents" [Overall renormalisation of event-by-event weights used in the training (NumEvents: average weight of 1 per event, independently for signal and background; EqualNumEvents: average weight of 1 per event for signal, and sum of weights for background equal to sum of weights for signal)]
: ScaleWithPreselEff: "False" [Scale the number of requested events by the eff. of the preselection cuts (or not)]
: TrainTestSplit_Signal: "0.000000e+00" [Number of test events of class Signal (default: 0 = all)]
: TrainTestSplit_Background: "0.000000e+00" [Number of test events of class Background (default: 0 = all)]
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Correlations: "True" [Boolean to show correlation output (Default: true)]
: CalcCorrelations: "True" [Compute correlations and also some variable statistics, e.g. min/max (Default: true )]
: Building event vectors for type 2 Signal
: Dataset[MatchNNDataSet] : create input formulas for tree Signal
: Building event vectors for type 2 Background
: Dataset[MatchNNDataSet] : create input formulas for tree Bkg
DataSetFactory : [MatchNNDataSet] : Number of events in input trees
:
:
: Dataset[MatchNNDataSet] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ...
: Dataset[MatchNNDataSet] : such that the effective (weighted) number of events in each class is the same
: Dataset[MatchNNDataSet] : (and equals the number of events (entries) given for class=0 )
: Dataset[MatchNNDataSet] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ...
: Dataset[MatchNNDataSet] : ... (note that N_j is the sum of TRAINING events
: Dataset[MatchNNDataSet] : ..... Testing events are not renormalised nor included in the renormalisation factor!)
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 5590
: Signal -- testing events : 1000
: Signal -- training and testing events: 6590
: Background -- training events : 200000
: Background -- testing events : 50000
: Background -- training and testing events: 250000
:
DataSetInfo : Correlation matrix (Signal):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.083 +0.225 +0.287 +0.211 +0.054
: teta2: -0.083 +1.000 +0.035 +0.472 +0.174 +0.617
: distX: +0.225 +0.035 +1.000 -0.194 +0.684 +0.087
: distY: +0.287 +0.472 -0.194 +1.000 +0.330 +0.471
: dSlope: +0.211 +0.174 +0.684 +0.330 +1.000 +0.325
: dSlopeY: +0.054 +0.617 +0.087 +0.471 +0.325 +1.000
: --------------------------------------------------------
DataSetInfo : Correlation matrix (Background):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 +0.003 +0.359 +0.315 -0.004 +0.101
: teta2: +0.003 +1.000 +0.212 +0.622 +0.296 +0.492
: distX: +0.359 +0.212 +1.000 +0.060 +0.635 +0.204
: distY: +0.315 +0.622 +0.060 +1.000 +0.246 +0.530
: dSlope: -0.004 +0.296 +0.635 +0.246 +1.000 +0.360
: dSlopeY: +0.101 +0.492 +0.204 +0.530 +0.360 +1.000
: --------------------------------------------------------
DataSetFactory : [MatchNNDataSet] :
:
Factory : [MatchNNDataSet] : Create Transformation "I" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 13.730 8.0164 [ 0.00031556 30.000 ]
: teta2: 0.0041449 0.012655 [ 1.1428e-06 0.43138 ]
: distX: 69.832 60.841 [ 0.00027466 490.80 ]
: distY: 31.145 37.661 [ 0.00010300 497.14 ]
: dSlope: 0.36688 0.24104 [ 1.2597e-05 1.3582 ]
: dSlopeY: 0.0063738 0.010662 [ 4.9360e-08 0.14883 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
IdTransformation : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Separation
: --------------------------------
: 1 : chi2 : 8.858e-02
: 2 : distY : 5.736e-02
: 3 : teta2 : 3.110e-02
: 4 : distX : 2.441e-02
: 5 : dSlope : 2.026e-02
: 6 : dSlopeY : 1.556e-02
: --------------------------------
Factory : Train method: matching_mlp for Classification
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.084705 0.53444 [ -1.0000 1.0000 ]
: teta2: -0.98079 0.058673 [ -1.0000 1.0000 ]
: distX: -0.71544 0.24793 [ -1.0000 1.0000 ]
: distY: -0.87470 0.15151 [ -1.0000 1.0000 ]
: dSlope: -0.45977 0.35494 [ -1.0000 1.0000 ]
: dSlopeY: -0.91435 0.14328 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Training Network
:
: Elapsed time for training with 205590 events: 465 sec
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (205590 events)
: Elapsed time for evaluation of 205590 events: 0.252 sec
: Creating xml weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
: Creating standalone class: MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C
: Write special histos to file: matching_ghost_mlp_training.root:/MatchNNDataSet/Method_MLP/matching_mlp
Factory : Training finished
:
: Ranking input variables (method specific)...
matching_mlp : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Importance
: --------------------------------
: 1 : distY : 2.139e+02
: 2 : teta2 : 1.005e+02
: 3 : dSlopeY : 9.191e+01
: 4 : distX : 8.898e+01
: 5 : dSlope : 1.082e+01
: 6 : chi2 : 1.776e+00
: --------------------------------
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
matching_mlp : Building Network.
: Initializing weights
Factory : Test all methods
Factory : Test method: matching_mlp for Classification performance
:
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on testing sample (51000 events)
: Elapsed time for evaluation of 51000 events: 0.0702 sec
Factory : Evaluate all methods
Factory : Evaluate classifier: matching_mlp
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.011828 0.57705 [ -0.99996 0.99998 ]
: teta2: -0.97507 0.067138 [ -0.99998 0.27868 ]
: distX: -0.72636 0.26123 [ -1.0000 0.90538 ]
: distY: -0.84283 0.18429 [ -0.99999 1.0037 ]
: dSlope: -0.48676 0.36013 [ -0.99980 0.87659 ]
: dSlopeY: -0.90653 0.13847 [ -1.0000 1.0030 ]
: -----------------------------------------------------------
matching_mlp : [MatchNNDataSet] : Loop over test events and fill histograms with classifier response...
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.011828 0.57705 [ -0.99996 0.99998 ]
: teta2: -0.97507 0.067138 [ -0.99998 0.27868 ]
: distX: -0.72636 0.26123 [ -1.0000 0.90538 ]
: distY: -0.84283 0.18429 [ -0.99999 1.0037 ]
: dSlope: -0.48676 0.36013 [ -0.99980 0.87659 ]
: dSlopeY: -0.90653 0.13847 [ -1.0000 1.0030 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: MatchNNDataSet matching_mlp : 0.850
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: MatchNNDataSet matching_mlp : 0.050 (0.050) 0.446 (0.447) 0.869 (0.869)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:MatchNNDataSet : Created tree 'TestTree' with 51000 events
:
Dataset:MatchNNDataSet : Created tree 'TrainTree' with 205590 events
:
Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
Transforming nn_electron_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C ...
Found minimum and maximum values for 6 variables.
Found 3 matrices:
1. fWeightMatrix0to1 with 7 columns and 8 rows
2. fWeightMatrix1to2 with 9 columns and 6 rows
3. fWeightMatrix2to3 with 7 columns and 1 rows

268
outputs_nn/output_og_weights_res_bkg_B.txt Normal file
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@ -0,0 +1,268 @@
: Parsing option string:
: ... "V:!Silent:Color:DrawProgressBar:AnalysisType=Classification"
: The following options are set:
: - By User:
: V: "True" [Verbose flag]
: Color: "True" [Flag for coloured screen output (default: True, if in batch mode: False)]
: Silent: "False" [Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)]
: DrawProgressBar: "True" [Draw progress bar to display training, testing and evaluation schedule (default: True)]
: AnalysisType: "Classification" [Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)]
: - Default:
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Transformations: "I" [List of transformations to test; formatting example: "Transformations=I;D;P;U;G,D", for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations]
: Correlations: "False" [boolean to show correlation in output]
: ROC: "True" [boolean to show ROC in output]
: ModelPersistence: "True" [Option to save the trained model in xml file or using serialization]
DataSetInfo : [MatchNNDataSet] : Added class "Signal"
: Add Tree Signal of type Signal with 6590 events
DataSetInfo : [MatchNNDataSet] : Added class "Background"
: Add Tree Bkg of type Background with 10981310 events
: Dataset[MatchNNDataSet] : Class index : 0 name : Signal
: Dataset[MatchNNDataSet] : Class index : 1 name : Background
Factory : Booking method: matching_mlp
:
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator"
: The following options are set:
: - By User:
: NCycles: "700" [Number of training cycles]
: HiddenLayers: "N+2,N" [Specification of hidden layer architecture]
: NeuronType: "ReLU" [Neuron activation function type]
: EstimatorType: "CE" [MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood]
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "Norm" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: TrainingMethod: "BP" [Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)]
: LearningRate: "2.000000e-02" [ANN learning rate parameter]
: DecayRate: "1.000000e-02" [Decay rate for learning parameter]
: TestRate: "50" [Test for overtraining performed at each #th epochs]
: Sampling: "1.000000e+00" [Only 'Sampling' (randomly selected) events are trained each epoch]
: SamplingImportance: "1.000000e+00" [ The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided.]
: UseRegulator: "False" [Use regulator to avoid over-training]
: - Default:
: RandomSeed: "1" [Random seed for initial synapse weights (0 means unique seed for each run; default value '1')]
: NeuronInputType: "sum" [Neuron input function type]
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: EpochMonitoring: "False" [Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)]
: SamplingEpoch: "1.000000e+00" [Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training]
: SamplingTraining: "True" [The training sample is sampled]
: SamplingTesting: "False" [The testing sample is sampled]
: ResetStep: "50" [How often BFGS should reset history]
: Tau: "3.000000e+00" [LineSearch "size step"]
: BPMode: "sequential" [Back-propagation learning mode: sequential or batch]
: BatchSize: "-1" [Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events]
: ConvergenceImprove: "1.000000e-30" [Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)]
: ConvergenceTests: "-1" [Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)]
: UpdateLimit: "10000" [Maximum times of regulator update]
: CalculateErrors: "False" [Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value]
: WeightRange: "1.000000e+00" [Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range]
matching_mlp : [MatchNNDataSet] : Create Transformation "Norm" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
matching_mlp : Building Network.
: Initializing weights
Factory : Train all methods
: Rebuilding Dataset MatchNNDataSet
: Parsing option string:
: ... "SplitMode=random:V:nTrain_Signal=0:nTrain_Background=200000.0:nTest_Signal=5000.0:nTest_Background=50000.0"
: The following options are set:
: - By User:
: SplitMode: "Random" [Method of picking training and testing events (default: random)]
: nTrain_Signal: "0" [Number of training events of class Signal (default: 0 = all)]
: nTest_Signal: "5000" [Number of test events of class Signal (default: 0 = all)]
: nTrain_Background: "200000" [Number of training events of class Background (default: 0 = all)]
: nTest_Background: "50000" [Number of test events of class Background (default: 0 = all)]
: V: "True" [Verbosity (default: true)]
: - Default:
: MixMode: "SameAsSplitMode" [Method of mixing events of different classes into one dataset (default: SameAsSplitMode)]
: SplitSeed: "100" [Seed for random event shuffling]
: NormMode: "EqualNumEvents" [Overall renormalisation of event-by-event weights used in the training (NumEvents: average weight of 1 per event, independently for signal and background; EqualNumEvents: average weight of 1 per event for signal, and sum of weights for background equal to sum of weights for signal)]
: ScaleWithPreselEff: "False" [Scale the number of requested events by the eff. of the preselection cuts (or not)]
: TrainTestSplit_Signal: "0.000000e+00" [Number of test events of class Signal (default: 0 = all)]
: TrainTestSplit_Background: "0.000000e+00" [Number of test events of class Background (default: 0 = all)]
: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
: Correlations: "True" [Boolean to show correlation output (Default: true)]
: CalcCorrelations: "True" [Compute correlations and also some variable statistics, e.g. min/max (Default: true )]
: Building event vectors for type 2 Signal
: Dataset[MatchNNDataSet] : create input formulas for tree Signal
: Building event vectors for type 2 Background
: Dataset[MatchNNDataSet] : create input formulas for tree Bkg
DataSetFactory : [MatchNNDataSet] : Number of events in input trees
:
:
: Dataset[MatchNNDataSet] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ...
: Dataset[MatchNNDataSet] : such that the effective (weighted) number of events in each class is the same
: Dataset[MatchNNDataSet] : (and equals the number of events (entries) given for class=0 )
: Dataset[MatchNNDataSet] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ...
: Dataset[MatchNNDataSet] : ... (note that N_j is the sum of TRAINING events
: Dataset[MatchNNDataSet] : ..... Testing events are not renormalised nor included in the renormalisation factor!)
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 1590
: Signal -- testing events : 5000
: Signal -- training and testing events: 6590
: Background -- training events : 200000
: Background -- testing events : 50000
: Background -- training and testing events: 250000
:
DataSetInfo : Correlation matrix (Signal):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.090 +0.192 +0.272 +0.184 +0.049
: teta2: -0.090 +1.000 +0.041 +0.483 +0.208 +0.628
: distX: +0.192 +0.041 +1.000 -0.179 +0.680 +0.101
: distY: +0.272 +0.483 -0.179 +1.000 +0.363 +0.496
: dSlope: +0.184 +0.208 +0.680 +0.363 +1.000 +0.350
: dSlopeY: +0.049 +0.628 +0.101 +0.496 +0.350 +1.000
: --------------------------------------------------------
DataSetInfo : Correlation matrix (Background):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.018 +0.249 +0.222 +0.061 +0.061
: teta2: -0.018 +1.000 +0.219 +0.658 +0.336 +0.485
: distX: +0.249 +0.219 +1.000 -0.003 +0.782 +0.189
: distY: +0.222 +0.658 -0.003 +1.000 +0.284 +0.551
: dSlope: +0.061 +0.336 +0.782 +0.284 +1.000 +0.379
: dSlopeY: +0.061 +0.485 +0.189 +0.551 +0.379 +1.000
: --------------------------------------------------------
DataSetFactory : [MatchNNDataSet] :
:
Factory : [MatchNNDataSet] : Create Transformation "I" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'chi2' <---> Output : variable 'chi2'
: Input : variable 'teta2' <---> Output : variable 'teta2'
: Input : variable 'distX' <---> Output : variable 'distX'
: Input : variable 'distY' <---> Output : variable 'distY'
: Input : variable 'dSlope' <---> Output : variable 'dSlope'
: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 14.983 7.6381 [ 0.13393 30.000 ]
: teta2: 0.0045002 0.013698 [ 1.0756e-06 0.36197 ]
: distX: 74.269 61.998 [ 0.00010681 495.87 ]
: distY: 33.972 40.646 [ 0.00016022 498.46 ]
: dSlope: 0.35639 0.24245 [ 9.2713e-06 1.3376 ]
: dSlopeY: 0.0069454 0.011901 [ 8.8476e-08 0.14989 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
IdTransformation : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Separation
: --------------------------------
: 1 : chi2 : 9.899e-02
: 2 : distY : 8.544e-02
: 3 : teta2 : 4.508e-02
: 4 : dSlope : 3.745e-02
: 5 : dSlopeY : 2.733e-02
: 6 : distX : 1.657e-02
: --------------------------------
Factory : Train method: matching_mlp for Classification
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.0056167 0.51149 [ -1.0000 1.0000 ]
: teta2: -0.97514 0.075684 [ -1.0000 1.0000 ]
: distX: -0.70045 0.25006 [ -1.0000 1.0000 ]
: distY: -0.86369 0.16308 [ -1.0000 1.0000 ]
: dSlope: -0.46715 0.36251 [ -1.0000 1.0000 ]
: dSlopeY: -0.90733 0.15880 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Training Network
:
: Elapsed time for training with 201590 events: 424 sec
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (201590 events)
: Elapsed time for evaluation of 201590 events: 0.244 sec
: Creating xml weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
: Creating standalone class: MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C
: Write special histos to file: matching_ghost_mlp_training.root:/MatchNNDataSet/Method_MLP/matching_mlp
Factory : Training finished
:
: Ranking input variables (method specific)...
matching_mlp : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Importance
: --------------------------------
: 1 : distY : 7.131e+01
: 2 : teta2 : 3.522e+01
: 3 : distX : 2.316e+01
: 4 : dSlopeY : 1.020e+01
: 5 : dSlope : 6.822e+00
: 6 : chi2 : 2.546e+00
: --------------------------------
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
matching_mlp : Building Network.
: Initializing weights
Factory : Test all methods
Factory : Test method: matching_mlp for Classification performance
:
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on testing sample (55000 events)
: Elapsed time for evaluation of 55000 events: 0.0744 sec
Factory : Evaluate all methods
Factory : Evaluate classifier: matching_mlp
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 0.12456 0.50663 [ -0.99498 1.0000 ]
: teta2: -0.96870 0.085208 [ -1.0000 0.96194 ]
: distX: -0.68732 0.27168 [ -0.99998 0.99743 ]
: distY: -0.83061 0.19253 [ -1.0000 1.0027 ]
: dSlope: -0.50226 0.36648 [ -0.99996 0.94917 ]
: dSlopeY: -0.90192 0.14156 [ -1.0000 0.98588 ]
: -----------------------------------------------------------
matching_mlp : [MatchNNDataSet] : Loop over test events and fill histograms with classifier response...
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: 0.12456 0.50663 [ -0.99498 1.0000 ]
: teta2: -0.96870 0.085208 [ -1.0000 0.96194 ]
: distX: -0.68732 0.27168 [ -0.99998 0.99743 ]
: distY: -0.83061 0.19253 [ -1.0000 1.0027 ]
: dSlope: -0.50226 0.36648 [ -0.99996 0.94917 ]
: dSlopeY: -0.90192 0.14156 [ -1.0000 0.98588 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: MatchNNDataSet matching_mlp : 0.838
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: MatchNNDataSet matching_mlp : 0.067 (0.067) 0.460 (0.458) 0.828 (0.824)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:MatchNNDataSet : Created tree 'TestTree' with 55000 events
:
Dataset:MatchNNDataSet : Created tree 'TrainTree' with 201590 events
:
Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
Transforming nn_electron_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C ...
Found minimum and maximum values for 6 variables.
Found 3 matrices:
1. fWeightMatrix0to1 with 7 columns and 8 rows
2. fWeightMatrix1to2 with 9 columns and 6 rows
3. fWeightMatrix2to3 with 7 columns and 1 rows

189
parameterisations/losses_train_matching_ghost_mlps.py Normal file
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@ -0,0 +1,189 @@
# flake8: noqaq
import os
import argparse
import ROOT
from ROOT import TMVA, TList, TTree, TMath
def train_matching_ghost_mlp(
input_file: str = "data/tracking_losses_ntuple_B.root",
tree_name: str = "PrDebugTrackingLosses.PrDebugTrackingTool/Tuple",
b_input_file: str = "data/ghost_data_B.root",
b_tree_name: str = "PrMatchNN_3e224c41.PrMCDebugMatchToolNN/MVAInputAndOutput",
only_electrons: bool = True,
n_train_signal: int = 2e3, # 50e3
n_train_bkg: int = 5e3, # 500e3
n_test_signal: int = 1e3,
n_test_bkg: int = 2e3,
prepare_data: bool = True,
outdir: str = "nn_trackinglosses_training",
):
"""Trains an MLP to classify the match between Velo and Seed track.
Args:
input_file (str, optional): Defaults to "data/ghost_data.root".
tree_name (str, optional): Defaults to "PrMatchNN.PrMCDebugMatchToolNN/Tuple".
exclude_electrons (bool, optional): Defaults to False.
only_electrons (bool, optional): Signal only of electrons, but bkg of all particles. Defaults to True.
residuals (bool, optional): Signal only of mlp<0.215. Defaults to False.
n_train_signal (int, optional): Number of true matches to train on. Defaults to 200e3.
n_train_bkg (int, optional): Number of fake matches to train on. Defaults to 200e3.
n_test_signal (int, optional): Number of true matches to test on. Defaults to 75e3.
n_test_bkg (int, optional): Number of fake matches to test on. Defaults to 75e3.
prepare_data (bool, optional): Split data into signal and background file. Defaults to False.
"""
# vec = ROOT.std.vector("string")(13)
colList = [
"mc_chi2",
"mc_teta2",
"mc_distX",
"mc_distY",
"mc_dSlope",
"mc_dSlopeY",
"mc_quality",
"mc_end_velo_qop",
"mc_end_velo_tx",
"mc_end_velo_ty",
"mc_end_t_qop",
"mc_end_t_tx",
"mc_end_t_ty",
]
# for i in range(13):
# vec[i] = colList[i]
if prepare_data:
rdf = ROOT.RDataFrame(tree_name, input_file)
rdf_b = ROOT.RDataFrame(b_tree_name, b_input_file)
if only_electrons:
rdf_signal = rdf.Filter(
"mc_quality == -1 && lost == 0 && fromSignal == 1", # electron that is true match but mlp said no match
"Signal is defined as one label (only electrons)",
)
else:
rdf_signal = rdf.Filter(
"lost == 0 && fromSignal == 1",
"Signal is defined as non-zero label",
)
rdf_bkg = rdf_b.Filter(
"mc_quality == 0",
"Ghosts are defined as zero label",
)
rdf_signal.Snapshot(
"Signal",
outdir + "/" + input_file.strip(".root") + "_matching_signal.root",
colList,
)
rdf_bkg.Snapshot(
"Bkg",
outdir + "/" + input_file.strip(".root") + "_matching_bkg.root",
)
signal_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_matching_signal.root",
"READ",
)
signal_tree = signal_file.Get("Signal")
bkg_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_matching_bkg.root"
)
bkg_tree = bkg_file.Get("Bkg")
os.chdir(outdir + "/result")
output = ROOT.TFile(
"matching_ghost_mlp_training.root",
"RECREATE",
)
factory = TMVA.Factory(
"TMVAClassification",
output,
"V:!Silent:Color:DrawProgressBar:AnalysisType=Classification",
)
factory.SetVerbose(True)
dataloader = TMVA.DataLoader("MatchNNDataSet")
dataloader.AddVariable("mc_chi2", "F")
dataloader.AddVariable("mc_teta2", "F")
dataloader.AddVariable("mc_distX", "F")
dataloader.AddVariable("mc_distY", "F")
dataloader.AddVariable("mc_dSlope", "F")
dataloader.AddVariable("mc_dSlopeY", "F")
dataloader.AddSignalTree(signal_tree, 1.0)
dataloader.AddBackgroundTree(bkg_tree, 1.0)
# these cuts are also applied in the algorithm
preselectionCuts = ROOT.TCut(
"!TMath::IsNaN(mc_chi2) && !TMath::IsNaN(mc_distX) && !TMath::IsNaN(mc_distY) && !TMath::IsNaN(mc_dSlope) && !TMath::IsNaN(mc_dSlopeY) && !TMath::IsNaN(mc_teta2)",
# "mc_chi2<30 && mc_distX<500 && mc_distY<500 && mc_dSlope<2.0 && mc_dSlopeY<0.15", #### ganz raus für elektronen
)
dataloader.PrepareTrainingAndTestTree(
preselectionCuts,
f"SplitMode=random:V:nTrain_Signal={n_train_signal}:nTrain_Background={n_train_bkg}:nTest_Signal={n_test_signal}:nTest_Background={n_test_bkg}",
# normmode default is EqualNumEvents
)
factory.BookMethod(
dataloader,
TMVA.Types.kMLP,
"matching_mlp",
"!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:UseRegulator",
)
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
output.Close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-file",
type=str,
help="Path to the input file",
required=False,
)
parser.add_argument(
"--exclude_electrons",
action="store_true",
help="Excludes electrons from training.",
required=False,
)
parser.add_argument(
"--only_electrons",
action="store_true",
help="Only electrons for signal training.",
required=False,
)
parser.add_argument(
"--n-train-signal",
type=int,
help="Number of training tracks for signal.",
required=False,
)
parser.add_argument(
"--n-train-bkg",
type=int,
help="Number of training tracks for bkg.",
required=False,
)
parser.add_argument(
"--n-test-signal",
type=int,
help="Number of testing tracks for signal.",
required=False,
)
parser.add_argument(
"--n-test-bkg",
type=int,
help="Number of testing tracks for bkg.",
required=False,
)
args = parser.parse_args()
args_dict = {arg: val for arg, val in vars(args).items() if val is not None}
train_matching_ghost_mlp(**args_dict)

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import uproot\n",
"import awkward as ak\n",
"import numpy as np\n",
"input_tree = uproot.open({\"/work/guenther/reco_tuner/data/param_data_selected.root\": \"Selected\"})\n",
"array = input_tree.arrays()\n",
"array[\"dSlope_xEndT\"] = array[\"tx_l11\"] - array[\"tx\"]\n",
"array[\"dSlope_yEndT\"] = array[\"ty_l11\"] - array[\"ty\"]\n",
"array[\"dSlope_xEndT_abs\"] = abs(array[\"dSlope_xEndT\"])\n",
"array[\"dSlope_yEndT_abs\"] = abs(array[\"dSlope_yEndT\"])\n",
"array[\"yStraightEndT\"] = array[\"y\"] + array[\"ty\"] * ( 9410. - array[\"z\"])\n",
"array[\"yDiffEndT\"] = (array[\"y_l11\"] + array[\"ty_l11\"] * ( 9410. - array[\"z_l11\"])) - array[\"yStraightEndT\"]\n",
"\n",
"def format_array(name, coef):\n",
" coef = [str(c)+\"f\" for c in coef if c != 0.0]\n",
" code = f\"constexpr std::array {name}\"\n",
" code += \"{\" + \", \".join(list(coef)) +\"};\"\n",
" return code"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['dSlope_yEndT' 'ty dSlope_yEndT_abs' 'ty tx dSlope_xEndT'\n",
" 'ty dSlope_xEndT^2' 'ty dSlope_yEndT^2' 'tx^2 dSlope_yEndT'\n",
" 'ty tx^2 dSlope_xEndT_abs' 'ty^3 tx dSlope_xEndT']\n",
"intercept= 0.0\n",
"coef= {}\n",
"r2 score= 0.9971571295750978\n",
"RMSE = 2.422206064647647\n",
"straight RMSE = 45.67726454181064\n",
"constexpr std::array y_xEndT_diff{4039.5218935644916f, 1463.501458069602f, 2210.102099471291f, 1537.0718454152473f, -411.54564619803864f, 2594.7244053238287f, -1030.7643414023526f, 14904.842115636024f};\n"
]
}
],
"source": [
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.linear_model import LinearRegression, Lasso, Ridge\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.metrics import mean_squared_error\n",
"\n",
"features = [\n",
" \"ty\", \n",
" \"tx\",\n",
" \"dSlope_xEndT\",\n",
" \"dSlope_yEndT\",\n",
" \"dSlope_xEndT_abs\",\n",
" \"dSlope_yEndT_abs\",\n",
"]\n",
"target_feat = \"yDiffEndT\"\n",
"\n",
"data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
"target = ak.to_numpy(array[target_feat])\n",
"X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)\n",
"\n",
"poly = PolynomialFeatures(degree=6, include_bias=False)\n",
"X_train_model = poly.fit_transform( X_train )\n",
"X_test_model = poly.fit_transform( X_test )\n",
"poly_features = poly.get_feature_names_out(input_features=features)\n",
"keep = [\n",
" #'dSlope_xEndT',\n",
" 'dSlope_yEndT', # keep\n",
" #'dSlope_yEndT_abs',\n",
" #'ty dSlope_xEndT',\n",
" #'ty dSlope_yEndT',\n",
" 'ty dSlope_xEndT_abs', # keep\n",
" 'ty dSlope_yEndT_abs', #keep\n",
" 'ty dSlope_yEndT^2', # keep \n",
" 'ty dSlope_xEndT^2', # keep\n",
" #'tx dSlope_xEndT',\n",
" #'tx dSlope_xEndT_abs',\n",
" #'tx dSlope_yEndT',\n",
" 'ty tx dSlope_xEndT', #keep\n",
" 'tx^2 dSlope_yEndT', # keep\n",
" #'ty^2 dSlope_xEndT',\n",
" #'ty^2 dSlope_yEndT', \n",
" #'ty^2 dSlope_xEndT_abs',\n",
" #'ty^2 tx dSlope_xEndT',\n",
" #'ty tx^2 dSlope_yEndT',\n",
" 'ty tx^2 dSlope_xEndT_abs', # keep\n",
" 'ty^3 tx dSlope_xEndT', #keep\n",
" #'ty tx^3 dSlope_xEndT',\n",
" #'ty^3 dSlope_yEndT_abs',\n",
"]\n",
"do_not_keep = [\n",
" 'dSlope_xEndT',\n",
" 'dSlope_yEndT_abs',\n",
" 'ty dSlope_xEndT',\n",
" 'tx dSlope_xEndT',\n",
" 'tx dSlope_xEndT_abs',\n",
" 'tx dSlope_yEndT',\n",
" 'ty^2 dSlope_xEndT',\n",
" 'ty^3 dSlope_yEndT_abs',\n",
" 'ty tx dSlope_yEndT',\n",
" 'ty tx^3 dSlope_xEndT',\n",
" 'ty tx^2 dSlope_yEndT',\n",
"]\n",
"reduce = True\n",
"if reduce:\n",
" remove = [i for i, f in enumerate(poly_features) if (keep and f not in keep )]\n",
" X_train_model = np.delete( X_train_model, remove, axis=1)\n",
" X_test_model = np.delete( X_test_model, remove, axis=1)\n",
" poly_features = np.delete(poly_features, remove )\n",
" print(poly_features)\n",
"if not reduce:\n",
" remove = [i for i, f in enumerate(poly_features) if (\"dSlope_\" not in f) or (\"EndT^\" in f) or (\"abs^\" in f) or (\"EndT dSlope\" in f) or (\"abs dSlope\" in f)]\n",
" X_train_model = np.delete( X_train_model, remove, axis=1)\n",
" X_test_model = np.delete( X_test_model, remove, axis=1)\n",
" poly_features = np.delete(poly_features, remove )\n",
" #print(poly_features)\n",
" lin_reg = Lasso(fit_intercept=False, alpha=0.000001)\n",
"else:\n",
" lin_reg = LinearRegression(fit_intercept=False)\n",
"lin_reg.fit( X_train_model, y_train)\n",
"y_pred_test = lin_reg.predict( X_test_model )\n",
"print(\"intercept=\", lin_reg.intercept_)\n",
"print(\"coef=\", {k: v for k, v in zip(poly_features, lin_reg.coef_) if abs(v) > 1.0 and k not in keep and k not in do_not_keep})\n",
"print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
"print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
"print(\"straight RMSE =\", mean_squared_error(array[\"y_l11\"], array[\"y\"] + array[\"ty\"] * ( array[\"z_l11\"] - array[\"z\"] ), squared=False))\n",
"print(format_array(\"y_xEndT_diff\", lin_reg.coef_))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.10.6 (conda)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "a2eff8b4da8b8eebf5ee2e5f811f31a557e0a202b4d2f04f849b065340a6eda6"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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from parameterisations.utils.fit_linear_regression_model import (
fit_linear_regression_model,
)
from parameterisations.utils.parse_regression_coef_to_array import (
parse_regression_coef_to_array,
)
import uproot
import argparse
from pathlib import Path
def parameterise_field_integral(
input_file: str = "data/param_data_selected_all_p.root",
tree_name: str = "Selected",
) -> Path:
"""Function to estimate parameters describing the magnetic field integral.
Args:
input_file (str, optional): Defaults to "data/param_data_selected_all_p.root".
tree_name (str, optional): Defaults to "Selected".
Returns:
Path: Path to the parameters in cpp format.
"""
input_tree = uproot.open({input_file: tree_name})
# this is an event list of dictionaries containing awkward arrays
array = input_tree.arrays()
array["dSlope_fringe"] = array["tx_ref"] - array["tx"]
array["poqmag_gev"] = 1.0 / (array["signed_rel_current"] * array["qop"] * 1000.0)
array["B_integral"] = array["poqmag_gev"] * array["dSlope_fringe"]
model_ref, poly_features_ref = fit_linear_regression_model(
array,
target_feat="B_integral",
features=[
"ty",
"tx",
"tx_ref",
],
keep=[
"ty^2",
"tx^2",
"tx tx_ref",
"tx_ref^2",
"ty^2 tx tx_ref",
"ty^2 tx^2",
"ty^2 tx_ref^2",
"tx^4",
"ty^4",
"tx_ref^4",
"tx^3 tx_ref",
],
degree=5,
fit_intercept=True,
)
cpp_ref = parse_regression_coef_to_array(
model_ref,
poly_features_ref,
"fieldIntegralParamsRef",
)
outpath = Path("parameterisations/result/field_integral_params.hpp")
outpath.parent.mkdir(parents=True, exist_ok=True)
with open(outpath, "w") as result:
result.writelines(cpp_ref)
return outpath
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-file",
type=str,
help="Path to the input file",
required=False,
)
parser.add_argument(
"--tree-name",
type=str,
help="Path to the input file",
required=False,
)
args = parser.parse_args()
args_dict = {arg: val for arg, val in vars(args).items() if val is not None}
outfile = parameterise_field_integral(**args_dict)
try:
import subprocess
# run clang-format for nicer looking result
subprocess.run(
[
"clang-format",
"-i",
f"{outfile}",
],
check=True,
)
except:
pass

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import uproot
import awkward as ak
from pathlib import Path
from scipy.optimize import curve_fit
import numpy as np
import pandas as pd
from math import ceil
import argparse
def fastSigmoid(x, p0, p1, p2):
return p0 + p1 * x / (1 + abs(p2 * x))
def parameterise_hough_histogram(
input_file: str = "data/param_data_selected_all_p.root",
tree_name: str = "Selected",
n_bins_start: int = 900,
hist_range: tuple[float, float] = (-3000.0, 3000.0),
first_bin_center: float = 2.5,
) -> Path:
"""Function to parameterise the binning of the Hough histogram using the occupancy on the reference plane.
Args:
input_file (str, optional): Defaults to "data/param_data_selected_all_p.root".
tree_name (str, optional): Defaults to "Selected".
n_bins_start (int, optional): Starting (minimal) number of bins in histogram. Defaults to 900.
hist_range (tuple[float, float], optional): Range in mm the histogram covers. Defaults to (-3000.0, 3000.0).
first_bin_center (float, optional): Calculated bin center at lower range. Defaults to 2.5.
Returns:
Path: Path to cpp code file.
"""
input_tree = uproot.open({input_file: tree_name})
# this is an event list of dictionaries containing awkward arrays
array = input_tree.arrays()
array = array[[field for field in ak.fields(array) if "scifi_hit" not in field]]
df = ak.to_pandas(array)
selection = (df["x_ref"] > hist_range[0]) & (df["x_ref"] < hist_range[1])
data = df.loc[selection, "x_ref"]
_, equal_bins = pd.qcut(data, q=n_bins_start, retbins=True)
bin_numbering = np.arange(0, n_bins_start + 1)
equalbins_center = equal_bins[int(n_bins_start / 10) : int(9 * n_bins_start / 10)]
bin_numbering_center = bin_numbering[
int(n_bins_start / 10) : int(9 * n_bins_start / 10)
]
func = fastSigmoid
popt, _ = curve_fit(func, xdata=equalbins_center, ydata=bin_numbering_center)
print("Parameterisation for central occupancy:")
print("fastSigmoid(x,", *popt, ")")
print("Scan shift to match first bin center ...")
shift = 0.0
while func(hist_range[0], popt[0] + shift, *popt[1:]) < first_bin_center:
shift += 0.1
popt[0] += shift - 0.1
popt[2] = abs(popt[2])
print("shifted: fastSigmoid(x,", *popt, ")")
n_bins_final = ceil(func(hist_range[1], *popt))
print(
"Final number of bins:",
n_bins_final,
"including offset of",
int(first_bin_center),
)
comment = f"// p[0] + p[1] * x / (1 + p[2] * abs(x)) for nBins = {n_bins_final}\n"
cpp = (
"constexpr auto p = std::array{"
+ ", ".join([str(p) + "f" for p in popt])
+ "};"
)
outpath = Path("parameterisations/result/hough_histogram_binning_params.hpp")
outpath.parent.mkdir(parents=True, exist_ok=True)
with open(outpath, "w") as result:
result.writelines([comment, cpp])
return outpath
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-file",
type=str,
help="Path to the input file",
required=False,
)
parser.add_argument(
"--tree-name",
type=str,
help="Path to the input file",
required=False,
)
args = parser.parse_args()
args_dict = {arg: val for arg, val in vars(args).items() if val is not None}
outfile = parameterise_hough_histogram(**args_dict)

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from parameterisations.utils.parse_regression_coef_to_array import (
parse_regression_coef_to_array,
)
from parameterisations.utils.fit_linear_regression_model import (
fit_linear_regression_model,
)
import uproot
import argparse
from pathlib import Path
def parameterise_magnet_kink(
input_file: str = "data/param_data_selected.root",
tree_name: str = "Selected",
per_layer=False,
) -> Path:
"""Function that calculates parameters for estimating the magnet kink z position.
Args:
input_file (str, optional): Defaults to "data/param_data_selected.root".
tree_name (str, optional): Defaults to "Selected".
per_layer (bool, optional): If true also calculates parameters per SciFi layer. Defaults to False.
Returns:
Path: Path to cpp code file.
"""
input_tree = uproot.open({input_file: tree_name})
# this is an event list of dictionaries containing awkward arrays
array = input_tree.arrays()
array["dSlope_fringe"] = array["tx_ref"] - array["tx"]
# the magnet kink position is the point of intersection of the track tagents
array["z_mag_x_fringe"] = (
array["x"]
- array["x_ref"]
- array["tx"] * array["z"]
+ array["tx_ref"] * array["z_ref"]
) / array["dSlope_fringe"]
array["dSlope_xEndT"] = array["tx_l11"] - array["tx"]
array["dSlope_xEndT_abs"] = abs(array["dSlope_xEndT"])
array["x_EndT_abs"] = abs(
array["x_l11"] + array["tx_l11"] * (9410.0 - array["z_l11"]),
)
# the magnet kink position is the point of intersection of the track tagents
array["z_mag_xEndT"] = (
array["x"]
- array["x_l11"]
- array["tx"] * array["z"]
+ array["tx_l11"] * array["z_l11"]
) / array["dSlope_xEndT"]
if per_layer:
layered_features = [f"x_diff_straight_l{layer}" for layer in range(12)]
rows = []
for i, feat in enumerate(layered_features):
scale = 3000
if "dSlope" not in feat:
array[f"x_l{i}_rel"] = array[f"x_l{i}"] / scale
array[f"x_diff_straight_l{i}"] = (
array[f"x_l{i}"]
- array["x"]
- array["tx"] * (array[f"z_l{i}"] - array["z"])
)
model, poly_features = fit_linear_regression_model(
array,
target_feat="z_mag_x_fringe",
features=[
"tx",
"ty",
feat,
],
keep=[
"tx^2",
f"tx x_diff_straight_l{i}",
"ty^2",
f"x_diff_straight_l{i}^2",
],
degree=2,
fit_intercept=True,
)
rows.append(
"{"
+ str(model.intercept_)
+ "f,"
+ ",".join([str(coef) + "f" for coef in model.coef_ if coef != 0.0])
+ "}",
)
cpp_decl = parse_regression_coef_to_array(
model,
poly_features,
"zMagnetParamsLayers",
rows=rows,
)
# now fit model for the reference plane
model_ref, poly_features_ref = fit_linear_regression_model(
array,
target_feat="z_mag_x_fringe",
features=["tx", "ty", "dSlope_fringe"],
keep=["tx^2", "tx dSlope_fringe", "ty^2", "dSlope_fringe^2"],
degree=2,
fit_intercept=True,
)
cpp_ref = parse_regression_coef_to_array(
model_ref,
poly_features_ref,
"zMagnetParamsRef",
)
model_endt, poly_features_endt = fit_linear_regression_model(
array,
target_feat="z_mag_xEndT",
features=["tx", "dSlope_xEndT", "dSlope_xEndT_abs", "x_EndT_abs"],
keep=["tx^2", "dSlope_xEndT^2", "dSlope_xEndT_abs", "x_EndT_abs"],
degree=2,
fit_intercept=True,
)
cpp_ref += parse_regression_coef_to_array(
model_endt,
poly_features_endt,
"zMagnetParamsEndT",
)
outpath = Path("parameterisations/result/z_mag_kink_params.hpp")
outpath.parent.mkdir(parents=True, exist_ok=True)
with open(outpath, "w") as result:
result.writelines(cpp_decl + cpp_ref if per_layer else cpp_ref)
return outpath
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-file",
type=str,
help="Path to the input file",
required=False,
)
parser.add_argument(
"--tree-name",
type=str,
help="Path to the input file",
required=False,
)
args = parser.parse_args()
args_dict = {arg: val for arg, val in vars(args).items() if val is not None}
outfile = parameterise_magnet_kink(**args_dict)
try:
import subprocess
# run clang-format for nicer looking result
subprocess.run(
[
"clang-format",
"-i",
f"{outfile}",
],
check=True,
)
except:
pass

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from parameterisations.utils.parse_regression_coef_to_array import (
parse_regression_coef_to_array,
)
from parameterisations.utils.fit_linear_regression_model import (
fit_linear_regression_model,
)
import uproot
import argparse
from pathlib import Path
def parameterise_search_window(
input_file: str = "data/param_data_selected_all_p.root",
tree_name: str = "Selected",
) -> Path:
"""Function that calculates parameters for estimating the hit search window border.
Args:
input_file (str, optional): Defaults to "data/param_data_selected.root".
tree_name (str, optional): Defaults to "Selected".
Returns:
Path: Path to cpp code file.
"""
input_tree = uproot.open({input_file: tree_name})
# this is an event list of dictionaries containing awkward arrays
array = input_tree.arrays()
array["x_straight_diff_ref"] = (
array["x"] + array["tx"] * (array["z_ref"] - array["z"]) - array["x_ref"]
)
array["x_straight_diff_ref_abs"] = abs(array["x_straight_diff_ref"])
array["inv_p_gev"] = 1000.0 / array["p"]
array["pol_qop_gev"] = array["signed_rel_current"] * array["qop"] * 1000.0
# now fit model for the reference plane
model_ref, poly_features_ref = fit_linear_regression_model(
array,
target_feat="x_straight_diff_ref_abs",
features=["ty", "tx", "inv_p_gev", "pol_qop_gev"],
keep=[
"inv_p_gev",
"ty^2 inv_p_gev",
"tx^2 inv_p_gev",
"tx inv_p_gev pol_qop_gev",
"inv_p_gev^3",
"tx^3 pol_qop_gev",
"tx^2 inv_p_gev^2",
"tx inv_p_gev^2 pol_qop_gev",
"inv_p_gev^4",
"ty^2 tx^2 inv_p_gev",
"ty^2 tx inv_p_gev pol_qop_gev",
"ty^2 inv_p_gev^3",
"tx^4 inv_p_gev",
],
degree=5,
fit_intercept=False,
)
cpp_ref = parse_regression_coef_to_array(
model_ref,
poly_features_ref,
"momentumWindowParamsRef",
)
outpath = Path("parameterisations/result/search_window_params.hpp")
outpath.parent.mkdir(parents=True, exist_ok=True)
with open(outpath, "w") as result:
result.writelines(cpp_ref)
return outpath
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-file",
type=str,
help="Path to the input file",
required=False,
)
parser.add_argument(
"--tree-name",
type=str,
help="Path to the input file",
required=False,
)
args = parser.parse_args()
args_dict = {arg: val for arg, val in vars(args).items() if val is not None}
outfile = parameterise_search_window(**args_dict)
try:
import subprocess
# run clang-format for nicer looking result
subprocess.run(
[
"clang-format",
"-i",
f"{outfile}",
],
check=True,
)
except:
pass

256
parameterisations/parameterise_track_model.py Normal file
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from parameterisations.utils.parse_regression_coef_to_array import (
parse_regression_coef_to_array,
)
from parameterisations.utils.fit_linear_regression_model import (
fit_linear_regression_model,
)
import uproot
import argparse
from pathlib import Path
def parameterise_track_model(
input_file: str = "data/param_data_selected.root",
tree_name: str = "Selected",
) -> Path:
"""Function that calculates the parameterisations to estimate track model coefficients.
Args:
input_file (str, optional): Defaults to "data/param_data_selected.root".
tree_name (str, optional): Defaults to "Selected".
Returns:
Path: Path to cpp code files containing the found parameters.
"""
input_tree = uproot.open({input_file: tree_name})
# this is an event list of dictionaries containing awkward arrays
array = input_tree.arrays()
array["dSlope_fringe"] = array["tx_ref"] - array["tx"]
array["dSlope_fringe_abs"] = abs(array["dSlope_fringe"])
array["yStraightRef"] = array["y"] + array["ty"] * (array["z_ref"] - array["z"])
array["y_ref_straight_diff"] = array["y_ref"] - array["yStraightRef"]
array["ty_ref_straight_diff"] = array["ty_ref"] - array["ty"]
array["dSlope_xEndT"] = array["tx_l11"] - array["tx"]
array["dSlope_yEndT"] = array["ty_l11"] - array["ty"]
array["dSlope_xEndT_abs"] = abs(array["dSlope_xEndT"])
array["dSlope_yEndT_abs"] = abs(array["dSlope_yEndT"])
array["yStraightOut"] = array["y"] + array["ty"] * (array["z_out"] - array["z"])
array["yDiffOut"] = array["y_out"] - array["yStraightOut"]
array["yStraightEndT"] = array["y"] + array["ty"] * (9410.0 - array["z"])
array["yDiffEndT"] = (
array["y_l11"] + array["ty_l11"] * (9410.0 - array["z_l11"])
) - array["yStraightEndT"]
stereo_layers = [1, 2, 5, 6, 9, 10]
for layer in stereo_layers:
array[f"y_straight_diff_l{layer}"] = (
array[f"y_l{layer}"]
- array["y"]
- array["ty"] * (array[f"z_l{layer}"] - array["z"])
)
model_cx, poly_features_cx = fit_linear_regression_model(
array,
target_feat="CX_ex",
features=["tx", "ty", "dSlope_fringe"],
degree=3,
keep_only_linear_in="dSlope_fringe",
fit_intercept=False,
)
model_dx, poly_features_dx = fit_linear_regression_model(
array,
target_feat="DX_ex",
features=["tx", "ty", "dSlope_fringe"],
degree=3,
keep_only_linear_in="dSlope_fringe",
fit_intercept=False,
)
# this list has been found empirically by C.Hasse
keep_y_corr = [
"ty dSlope_fringe_abs",
"ty tx^2 dSlope_fringe_abs",
"ty^3 dSlope_fringe_abs",
"ty^3 tx^2 dSlope_fringe_abs",
"dSlope_fringe",
"ty tx dSlope_fringe",
"ty tx^3 dSlope_fringe",
"ty^3 tx dSlope_fringe",
]
model_y_corr_ref, poly_features_y_corr_ref = fit_linear_regression_model(
array,
target_feat="y_ref_straight_diff",
features=["ty", "tx", "dSlope_fringe", "dSlope_fringe_abs"],
keep=keep_y_corr,
degree=6,
fit_intercept=False,
)
rows = []
for layer in stereo_layers:
model_y_corr_l, poly_features_y_corr_l = fit_linear_regression_model(
array,
target_feat=f"y_straight_diff_l{layer}",
features=["ty", "tx", "dSlope_fringe", "dSlope_fringe_abs"],
keep=keep_y_corr,
degree=6,
fit_intercept=False,
)
rows.append(
"{"
+ ",".join(
[str(coef) + "f" for coef in model_y_corr_l.coef_ if coef != 0.0],
)
+ "}",
)
model_ty_corr_ref, poly_features_ty_corr_ref = fit_linear_regression_model(
array,
target_feat="ty_ref_straight_diff",
features=["ty", "tx", "dSlope_fringe", "dSlope_fringe_abs"],
# this list was found by using Lasso regularisation to drop useless features
keep=[
"ty dSlope_fringe^2",
"ty tx^2 dSlope_fringe_abs",
"ty^3 dSlope_fringe_abs",
"ty^3 tx^2 dSlope_fringe_abs",
"ty tx dSlope_fringe",
"ty tx^3 dSlope_fringe",
],
degree=6,
fit_intercept=False,
)
model_cy, poly_features_cy = fit_linear_regression_model(
array,
target_feat="CY_ex",
features=["ty", "tx", "dSlope_fringe", "dSlope_fringe_abs"],
# this list was found by using Lasso regularisation to drop useless features
keep=[
"ty dSlope_fringe^2",
"ty dSlope_fringe_abs",
"ty tx^2 dSlope_fringe_abs",
"ty^3 dSlope_fringe_abs",
"ty tx dSlope_fringe",
],
degree=4,
fit_intercept=False,
)
model_y_match, poly_features_y_match = fit_linear_regression_model(
array,
target_feat="yDiffOut",
features=[
"ty",
"dSlope_xEndT",
"dSlope_yEndT",
],
keep=[
"ty dSlope_yEndT^2",
"ty dSlope_xEndT^2",
],
degree=3,
fit_intercept=False,
)
keep_y_match_precise = [
"dSlope_yEndT",
"ty dSlope_xEndT_abs",
"ty dSlope_yEndT_abs",
"ty dSlope_yEndT^2",
"ty dSlope_xEndT^2",
"ty tx dSlope_xEndT",
"tx^2 dSlope_yEndT",
"ty tx^2 dSlope_xEndT_abs",
"ty^3 tx dSlope_xEndT",
]
model_y_match_precise, poly_features_y_match_precise = fit_linear_regression_model(
array,
"yDiffEndT",
[
"ty",
"tx",
"dSlope_xEndT",
"dSlope_yEndT",
"dSlope_xEndT_abs",
"dSlope_yEndT_abs",
],
keep=keep_y_match_precise,
degree=5,
)
cpp_cx = parse_regression_coef_to_array(model_cx, poly_features_cx, "cxParams")
cpp_dx = parse_regression_coef_to_array(model_dx, poly_features_dx, "dxParams")
cpp_y_corr_layers = parse_regression_coef_to_array(
model_y_corr_l,
poly_features_y_corr_l,
"yCorrParamsLayers",
rows=rows,
)
cpp_y_corr_ref = parse_regression_coef_to_array(
model_y_corr_ref,
poly_features_y_corr_ref,
"yCorrParamsRef",
)
cpp_ty_corr_ref = parse_regression_coef_to_array(
model_ty_corr_ref,
poly_features_ty_corr_ref,
"tyCorrParamsRef",
)
cpp_cy = parse_regression_coef_to_array(model_cy, poly_features_cy, "cyParams")
cpp_y_match = parse_regression_coef_to_array(
model_y_match,
poly_features_y_match,
"bendYParamsMatch",
)
cpp_y_match_precise = parse_regression_coef_to_array(
model_y_match_precise,
poly_features_y_match_precise,
"bendYParams",
)
outpath = Path("parameterisations/result/track_model_params.hpp")
outpath.parent.mkdir(parents=True, exist_ok=True)
with open(outpath, "w") as result:
result.writelines(
cpp_cx
+ cpp_dx
+ cpp_y_corr_layers
+ cpp_y_corr_ref
+ cpp_ty_corr_ref
+ cpp_cy
+ cpp_y_match
+ cpp_y_match_precise,
)
return outpath
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-file",
type=str,
help="Path to the input file",
required=False,
)
parser.add_argument(
"--tree-name",
type=str,
help="Path to the input file",
required=False,
)
args = parser.parse_args()
args_dict = {arg: val for arg, val in vars(args).items() if val is not None}
outfile = parameterise_track_model(**args_dict)
try:
import subprocess
# run clang-format for nicer looking result
subprocess.run(
[
"clang-format",
"-i",
f"{outfile}",
],
check=True,
)
except:
pass

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parameterisations/residual_train_matching_ghost_mlps_electron.py Normal file
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# flake8: noqaq
import os
import argparse
import ROOT
from ROOT import TMVA, TList, TTree
def res_train_matching_ghost_mlp(
input_file: str = "data/ghost_data_B.root",
tree_name: str = "PrMatchNN.PrMCDebugMatchToolNN/MVAInputAndOutput",
exclude_electrons: bool = False,
only_electrons: bool = True,
residuals: str = "None",
n_train_signal: int = 2e3, # 50e3
n_train_bkg: int = 5e3, # 500e3
n_test_signal: int = 1e3,
n_test_bkg: int = 2e3,
prepare_data: bool = True,
outdir: str = "nn_electron_training",
):
"""Trains an MLP to classify the match between Velo and Seed track.
Args:
input_file (str, optional): Defaults to "data/ghost_data.root".
tree_name (str, optional): Defaults to "PrMatchNN.PrMCDebugMatchToolNN/Tuple".
exclude_electrons (bool, optional): Defaults to False.
only_electrons (bool, optional): Signal only of electrons, but bkg of all particles. Defaults to True.
residuals (bool, optional): Signal only of mlp<0.215. Defaults to False.
n_train_signal (int, optional): Number of true matches to train on. Defaults to 200e3.
n_train_bkg (int, optional): Number of fake matches to train on. Defaults to 200e3.
n_test_signal (int, optional): Number of true matches to test on. Defaults to 75e3.
n_test_bkg (int, optional): Number of fake matches to test on. Defaults to 75e3.
prepare_data (bool, optional): Split data into signal and background file. Defaults to False.
"""
if prepare_data and not residuals == "None":
resrdf = ROOT.RDataFrame(residuals, input_file)
rdf = ROOT.RDataFrame(tree_name, input_file)
if exclude_electrons:
rdf_signal = rdf.Filter(
"quality == 1 && mlp<0.215", # -1 elec, 0 ghost, 1 all part wo elec
"Signal is defined as one label (excluding electrons)",
)
rdf_bkg = rdf.Filter(
"quality == 0 && mlp<0.215",
"Ghosts are defined as zero label",
)
resrdf_signal = resrdf.Filter(
"quality == 1", # -1 elec, 0 ghost, 1 all part wo elec
"Signal is defined as one label (excluding electrons)",
)
resrdf_bkg = resrdf.Filter(
"quality == 0",
"Ghosts are defined as zero label",
)
else:
if only_electrons:
rdf_signal = rdf.Filter(
"quality == -1 && mlp<0.215", # electron that is true match but mlp said no match
"Signal is defined as one label (only electrons)",
)
resrdf_signal = resrdf.Filter(
"quality == -1", # electron that is true match but mlp said no match
"Signal is defined as one label (only electrons)",
)
else:
rdf_signal = rdf.Filter(
"abs(quality) > 0 && mlp<0.215",
"Signal is defined as non-zero label",
)
resrdf_signal = resrdf.Filter(
"abs(quality) > 0",
"Signal is defined as non-zero label",
)
rdf_bkg = rdf.Filter(
"quality == 0 && mlp<0.215",
"Ghosts are defined as zero label",
)
resrdf_bkg = resrdf.Filter(
"quality == 0",
"Ghosts are defined as zero label",
)
rdf_signal.Snapshot(
"Signal",
outdir + "/" + input_file.strip(".root") + "_mlp_matching_signal.root",
)
rdf_bkg.Snapshot(
"Bkg",
outdir + "/" + input_file.strip(".root") + "_mlp_matching_bkg.root",
)
resrdf_signal.Snapshot(
"Signal",
outdir + "/" + input_file.strip(".root") + "_res_matching_signal.root",
)
resrdf_bkg.Snapshot(
"Bkg",
outdir + "/" + input_file.strip(".root") + "_res_matching_bkg.root",
)
mlp_signal_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_mlp_matching_signal.root",
"READ",
)
mlp_signal_tree = mlp_signal_file.Get("Signal")
mlp_bkg_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_mlp_matching_bkg.root",
"READ",
)
mlp_bkg_tree = mlp_bkg_file.Get("Bkg")
####
res_signal_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_res_matching_signal.root",
"READ",
)
res_signal_tree = res_signal_file.Get("Signal")
outputsignalFile = ROOT.TFile(
outdir + "/" + input_file.strip(".root") + "_merged_matching_signal.root",
"RECREATE",
)
signaltreeList = TList()
signaltreeList.Add(mlp_signal_tree)
signaltreeList.Add(res_signal_tree)
mergedsignalTree = TTree.MergeTrees(signaltreeList)
mergedsignalTree.Write()
outputsignalFile.Close()
res_bkg_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_res_matching_bkg.root",
"READ",
)
res_bkg_tree = res_bkg_file.Get("Bkg")
outputbkgFile = ROOT.TFile(
outdir + "/" + input_file.strip(".root") + "_merged_matching_bkg.root",
"RECREATE",
)
bkgtreeList = TList()
bkgtreeList.Add(mlp_bkg_tree)
bkgtreeList.Add(res_bkg_tree)
mergedbkgTree = TTree.MergeTrees(bkgtreeList)
mergedbkgTree.Write()
outputbkgFile.Close()
#####
signal_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_merged_matching_signal.root",
"READ",
)
signal_tree = signal_file.Get("Signal")
bkg_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_merged_matching_bkg.root"
)
bkg_tree = bkg_file.Get("Bkg")
###
os.chdir(outdir + "/result")
output = ROOT.TFile(
"matching_ghost_mlp_training.root",
"RECREATE",
)
factory = TMVA.Factory(
"TMVAClassification",
output,
"V:!Silent:Color:DrawProgressBar:AnalysisType=Classification",
)
factory.SetVerbose(True)
dataloader = TMVA.DataLoader("MatchNNDataSet")
dataloader.AddVariable("chi2", "F")
dataloader.AddVariable("teta2", "F")
dataloader.AddVariable("distX", "F")
dataloader.AddVariable("distY", "F")
dataloader.AddVariable("dSlope", "F")
dataloader.AddVariable("dSlopeY", "F")
dataloader.AddSignalTree(signal_tree, 1.0)
dataloader.AddBackgroundTree(bkg_tree, 1.0)
# these cuts are also applied in the algorithm
preselectionCuts = ROOT.TCut(
# "chi2<30 && distX<500 && distY<500 && dSlope<2.0 && dSlopeY<0.15", #### ganz raus für elektronen
)
dataloader.PrepareTrainingAndTestTree(
preselectionCuts,
f"SplitMode=random:V:nTrain_Signal={n_train_signal}:nTrain_Background={n_train_bkg}:nTest_Signal={n_test_signal}:nTest_Background={n_test_bkg}",
# normmode default is EqualNumEvents
)
factory.BookMethod(
dataloader,
TMVA.Types.kMLP,
"matching_mlp",
"!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator",
)
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
output.Close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-file",
type=str,
help="Path to the input file",
required=False,
)
parser.add_argument(
"--exclude_electrons",
action="store_true",
help="Excludes electrons from training.",
required=False,
)
parser.add_argument(
"--only_electrons",
action="store_true",
help="Only electrons for signal training.",
required=False,
)
parser.add_argument(
"--n-train-signal",
type=int,
help="Number of training tracks for signal.",
required=False,
)
parser.add_argument(
"--n-train-bkg",
type=int,
help="Number of training tracks for bkg.",
required=False,
)
parser.add_argument(
"--n-test-signal",
type=int,
help="Number of testing tracks for signal.",
required=False,
)
parser.add_argument(
"--n-test-bkg",
type=int,
help="Number of testing tracks for bkg.",
required=False,
)
args = parser.parse_args()
args_dict = {arg: val for arg, val in vars(args).items() if val is not None}
res_train_matching_ghost_mlp(**args_dict)

108
parameterisations/result/default_forward_ghost.hpp Normal file
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constexpr auto fMin = std::array<float, 9>{
{0.00086611090228, 0.000137108087074, 9.1552734375e-05, 1.52587890625e-05,
1.49011611938e-08, 0, 3.38108901987e-10, 0, 0}};
constexpr auto fMax =
std::array<float, 9>{{7.99757671356, 139.755401611, 499.902832031,
139.934204102, 0.0548411794007, 0.33653563261,
0.0011771483114, 0.614122271538, 0.0932129621506}};
constexpr auto fWeightMatrix0to1 = std::array<std::array<float, 10>, 13>{
{{0.272388837195353, 1.04297766281443, 7.10858971290199, 8.20176699323914,
-17.4998198279343, -0.903938445734019, -4.45523353351358,
0.202604346228416, -0.343763626116408, -6.0398577123475},
{1.17259942394887, -0.372252986403353, -0.172055869721126,
4.32425941771619, 0.980522044689679, -0.486163242078544, 4.89499262471543,
-2.19861130758734, -9.43992619200679, -2.37510165732473},
{-0.312463651685767, -0.114031703331406, -0.643524084362763,
-0.130218804467335, 0.0672200809874041, 0.492579418283905,
10.0423165027703, -9.1229718440813, -3.21081732473868, -3.78681415782996},
{0.285964399088691, 3.50382532157828, 0.202967804648741, -2.34731912244972,
-1.01724465412901, 6.94879051931226, 1.42757737963066, -0.414886005380144,
-5.12456720768917, -5.42877386327517},
{-1.17951731489478, -0.786765172581322, -4.2989135483231, 4.05294030963229,
-0.127313249850962, -4.14839099916227, 3.23904819574731,
-13.6552536266634, 20.5968822768501, 6.85315498878814},
{0.103392918358953, 1.44880697882548, 0.577811396176936, 1.10045098601948,
-0.178340433397024, -17.8770425761109, 3.7017773013521, -23.3892167651105,
15.0495143106538, -17.4605298799865},
{-1.64979711432504, 0.774908843327995, 3.31059510110807, -3.1819692768259,
7.12231795634781, -0.241753227182394, -0.977884374115893,
0.952532388892299, 0.723099651883065, 6.54591062809547},
{-0.0534432249645241, 3.9887257817946, -1.49200429968719, 11.8855727958475,
1.04666107895933, 0.238167385927067, -17.6503013604389, -0.6355065389129,
34.3524751991456, 32.5893217368421},
{0.386236757608688, -3.68207271228384, -1.59827939590235,
-5.63561468820375, -2.05612305429069, -0.414007692878055,
-2.30218891934988, -1.45254018219727, -41.888511388421,
-55.4154956571825},
{-0.33882942035142, 0.828500879691617, -1.11913145963814, 5.44432070997378,
0.593216106072808, -0.335356938266522, -4.29488215708929,
-0.45349431026542, 12.6168245530257, 11.203180125896},
{-5.06732270572528, -0.747932010258911, -1.32944569630483,
0.399754582341283, -1.22419379102021, -0.0632059754142294,
-3.9176205612916, 4.95338770012354, -10.0874931996749, -13.4899867496953},
{-0.652708751114843, 0.153993426617753, 0.507696533435112,
-0.0829575859982346, -1.26075671332944, -1.72921422048723,
0.436623404900834, 1.2481716555653, 4.75489497214422, 0.917647460712304},
{-0.337064695166363, 1.6829713570542, -1.24667087979073, 4.38935163725774,
0.546771935887102, 0.380216805759757, -4.27094569696754, 1.60030195739679,
21.1019766790379, 22.9277879180437}}};
constexpr auto fWeightMatrix1to2 = std::array<std::array<float, 14>, 11>{
{{0.942639197400156, -0.0931922637028017, 1.16131136847027,
-0.463586546886379, -0.00734730684531865, 0.659865738065189,
-0.0819640968388477, 0.716168152659691, 0.0594465496519534,
-3.09809338025968, -0.993314598112327, -0.626796644903994,
1.18112507293241, -7.53912207323744},
{-0.290157117133191, -1.44319213353958, 0.976801127908426,
1.83059667355593, -0.579127895773621, -0.959358564216034,
-0.200943614057267, 2.26102190218572, 0.680574054232225,
0.512044291258324, 0.441279132836691, 0.195148753454237,
-4.40558418761793, -2.59697427283538},
{0.231019193167673, -0.33534671921076, -4.02952877004057,
0.328301743837591, -0.935217897351683, 0.656308147872124,
0.148770195853327, -1.02822979925373, -1.15074999231744, 1.25931257478392,
-5.51506732446672, 2.76878002159781, 1.15670995216412, -1.81920686548505},
{-1.4433538095335, -1.39651294706804, -0.0384612540687588,
-0.643375798593072, -0.125102719515762, -0.654090297545811,
-1.64699393573932, -0.508246308405062, -0.781867310732538,
1.0126843726006, -1.62926724862902, 1.36812357325107, -0.529386420395731,
-0.0138925908234309},
{-1.41898718038457, 0.477953102899375, -0.322619989760896,
1.09732380296345, -0.782655793929218, 0.462713092659323,
-2.94842712863178, -0.132292667509809, 0.0769059927035169,
1.19584278425479, -1.05534832662164, -0.531211272706436,
-0.611338365394562, 3.05377051979242},
{-1.24565900961033, -0.803882286327658, 0.779616435095014,
-0.149080238970127, -1.12090436198206, -0.881759817062632,
-2.29233937767415, -1.47774128243345, 1.08239889708401, 2.24462771397501,
0.485265744305715, -1.48553084966211, -0.0143728439705144,
3.27583681932498},
{-0.28996288685836, 0.780550033512762, -2.15121583289839, 1.20922563714172,
-0.644843512306469, -0.234457523529768, -0.231658959155198,
0.218579418768696, -0.395509623165805, -0.411218621945028,
-1.02932631809835, -0.376223727062923, -0.340041262998441,
3.63881067894002},
{1.44978573970303, -2.18583173535086, -0.846483915375948,
0.105328656470861, 1.87072932363927, -0.00190765575147529,
0.584012973055999, -1.39776672542255, -0.180232469445207,
-1.98952098967519, -1.37262959684684, -0.90548142705001,
-0.920429811986854, -2.99277232574331},
{0.921784086695698, 0.803938386481379, -0.793436984418934,
-0.198036997127606, 0.88955947123962, 0.62100428659737, 1.37636249959335,
0.820454358047461, -0.632502523362294, -2.07934437515782,
-0.181199371886594, -3.08568993123453, -0.36401687310168,
-2.43212452146954},
{0.699249590603178, 0.278024565615921, -0.198350259368371,
-0.783418866551348, 0.118700073145684, 0.21833431656626, 1.65512324292893,
-0.111147682703641, 1.3283515189562, -0.246599469063488,
0.754508475403135, 0.00506528019287302, 0.166494023039331,
-4.84019659854236},
{0.298019259877464, 0.00346409235528412, 1.35256951642456,
0.869362943235745, -1.52238605698392, 1.83110259539772,
-0.878203306047072, -0.0145711167869491, -2.02333355061148,
0.870770188694429, 0.379444379601702, 0.418591035142932, 0.19006790384741,
-4.59660818674123}}};
constexpr auto fWeightMatrix2to3 = std::array<float, 12>{
{0.283357190373951, -0.505821393459412, -0.992928878077031,
1.46980063870956, -0.393221447900897, 0.68377817358151, -0.516508953958371,
-0.282023879956352, -0.65334083539925, 0.526593979331363,
-0.157977106282126, 1.39115062576625}};

9
parameterisations/result/field_integral_params.hpp Normal file
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// param[0] + param[1]*ty^2 + param[2]*tx^2 + param[3]*tx tx_ref +
// param[4]*tx_ref^2 + param[5]*ty^4 + param[6]*ty^2 tx^2 + param[7]*ty^2 tx
// tx_ref + param[8]*ty^2 tx_ref^2 + param[9]*tx^4 + param[10]*tx^3 tx_ref +
// param[11]*tx_ref^4
static constexpr std::array<float, 12> fieldIntegralParamsRef{
-1.2094486121528516f, -2.7897043324822492f, -0.35976930628193077f,
-0.47138558705675454f, -0.5600847231491961f, 14.009315350693472f,
-16.162818973243674f, -8.807994419844437f, -0.8753190393972976f,
2.98254201374128f, 0.9625408279466898f, 0.10200564097830103f};

3
parameterisations/result/hough_histogram_binning_params.hpp Normal file
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// p[0] + p[1] * x / (1 + p[2] * abs(x)) for nBins = 1152
constexpr auto p =
std::array{576.9713937732083f, 0.5780266207743059f, 0.0006728484590464921f};

36
parameterisations/result/matching.hpp Normal file
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const auto fMin = std::array<simd::float_v, 6>{{6.2234539655e-06, 1.07554035367e-06, 0, 0, 1.38022005558e-06, 0}};
const auto fMax = std::array<simd::float_v, 6>{
{14.9999675751, 0.414966464043, 249.946044922, 399.411682129, 1.32134592533, 0.148659110069}};
const auto fWeightMatrix0to1 = std::array<std::array<simd::float_v, 7>, 8>{
{{-1.81318680192985, 11.5306183035191, -1.52244588205196, -2.18285669265567, 5.01352644485465,
-5.51296033910149, 5.73927468893956},
{-0.672534709795381, -3.00002957605882, 6.88356805276872, -6.22160659721202, 6.77446979297102,
3.22745998562836, 2.16560576533548},
{0.671467962865227, -5.25794414846222, 19.3828230421486, 11.0803546893003, -6.38234816567783,
-8.90286557784295, 10.7684525390767},
{-0.2692056487945, -45.0124720478328, 3.02956760827695, -5.39985352881923, 2.33235637852444, 3.67377088731803,
-41.6892338123688},
{-1.7097866252219, -2.44815463022872, -6.25060061923427, -2.9527155271918, -2.82646287573035,
-2.57930159017213, -15.3820440704287},
{-1.05477315994645, 10.922735030486, 3.15543979640938, -1.83775727341147, 7.65261550754585, -6.94317448033313,
6.86131922732798},
{-0.79066972900182, -0.617757099680603, 0.740878002718091, 0.681870030239224, -1.20759406685829,
0.769290467724204, -1.8437808630988},
{-0.184133955272691, 1.92932229057759, 10.2040343486098, 4.08783185462586, -2.02695228923391,
-3.00792235466827, 10.2821397360227}}};
const auto fWeightMatrix1to2 = std::array<std::array<simd::float_v, 9>, 6>{
{{-0.529669554811976, -2.45282233466048, 1.45989990967879, 3.56480948423982, 0.687553026936273,
1.78027012856298, 1.63438201788813, -2.94255147008571, -2.10797233521637},
{1.36475059953963, 0.542190986793164, -0.135276688209357, -0.761685823733301, 0.679401991574712,
-1.40198671179551, -1.61531096417457, -0.791464040720268, 0.852677079400607},
{0.767942415115046, -2.97714597002192, -3.5629451506092, -2.69040161409325, 3.21229316674369,
0.688654835034672, -0.825543426908553, -1.84996857815595, -7.69537697905136},
{0.114639040310829, -0.37219550277267, -1.42908394861416, -1.86752756108709, -0.839837159377482,
-1.70735346337309, 1.61348068527877, -1.66550797875971, -0.949665027488677},
{-0.0439008856537062, 0.14714685191285, -0.900218617709006, 0.734110875341394, -3.26381964641836,
-0.903556360012639, -0.848898627795279, 2.4264150318668, 0.290359165274663},
{0.404515384352441, -0.158287682443141, -1.5660040193724, -1.64457334373498, 0.883554107720622,
-1.48730815915072, -1.52203810494393, 3.67527716420631, -0.393484682839}}};
const auto fWeightMatrix2to3 =
std::array<simd::float_v, 7>{{-0.776910463978178, 0.811895970822024, 0.775804138783722, 0.282335113136984,
-0.612856158181358, 0.786801771324536, -2.16123706007375}};

11
parameterisations/result/search_window_params.hpp Normal file
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// param[0]*inv_p_gev + param[1]*ty^2 inv_p_gev + param[2]*tx^2 inv_p_gev +
// param[3]*tx inv_p_gev pol_qop_gev + param[4]*inv_p_gev^3 + param[5]*tx^3
// pol_qop_gev + param[6]*tx^2 inv_p_gev^2 + param[7]*tx inv_p_gev^2 pol_qop_gev
// + param[8]*inv_p_gev^4 + param[9]*ty^2 tx^2 inv_p_gev + param[10]*ty^2 tx
// inv_p_gev pol_qop_gev + param[11]*ty^2 inv_p_gev^3 + param[12]*tx^4 inv_p_gev
static constexpr std::array<float, 13> momentumWindowParamsRef{
4018.896625676043f, 6724.789549369031f, 3970.9093976497766f,
-4363.5807241252905f, 1421.1056758688073f, 4934.07761471779f,
6985.252911263751f, -5538.28013195104f, 1642.8616070452542f,
106068.96918885755f, -94446.81037767915f, 26489.793756692892f,
-23936.54391006025f};

58
parameterisations/result/track_model_params.hpp Normal file
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// param[0]*dSlope_fringe + param[1]*tx dSlope_fringe + param[2]*ty
// dSlope_fringe + param[3]*tx^2 dSlope_fringe + param[4]*tx ty dSlope_fringe +
// param[5]*ty^2 dSlope_fringe
static constexpr std::array<float, 6> cxParams{
2.335283084724005e-05f, -5.394341220986507e-08f, -1.1353152524130453e-06f,
9.213281616649267e-06f, -6.76457896718169e-07f, -0.0003740758569392804f};
// param[0]*dSlope_fringe + param[1]*tx dSlope_fringe + param[2]*ty
// dSlope_fringe + param[3]*tx^2 dSlope_fringe + param[4]*tx ty dSlope_fringe +
// param[5]*ty^2 dSlope_fringe
static constexpr std::array<float, 6> dxParams{
-7.057523874477465e-09f, 1.0524178059699073e-11f, 6.46124765440666e-10f,
2.595690034874298e-09f, 8.044356540608104e-11f, 9.933758467661586e-08f};
// param[0]*dSlope_fringe + param[1]*ty dSlope_fringe_abs + param[2]*ty tx
// dSlope_fringe + param[3]*ty^3 dSlope_fringe_abs + param[4]*ty tx^2
// dSlope_fringe_abs + param[5]*ty^3 tx dSlope_fringe + param[6]*ty tx^3
// dSlope_fringe + param[7]*ty^3 tx^2 dSlope_fringe_abs
static constexpr std::array<std::array<float, 8>, 6> yCorrParamsLayers{
{{1.9141402652138315f, 154.61935746400832f, 3719.298754021463f,
-6981.575944838166f, -67.7612042340458f, 41484.88865215446f,
30544.717526101966f, 211219.00520598015f},
{1.9802106454737567f, 146.34197177414035f, 3766.9995843145575f,
-7381.001822418669f, 18.407833054380728f, 42635.398541425144f,
31434.95400997568f, 218404.36150766257f},
{2.6036680178541256f, 53.231282135657125f, 4236.335446831202f,
-10844.798302911375f, 986.1498917330866f, 52670.269097485856f,
39380.4857744525f, 281250.90766092145f},
{2.6802443731107797f, 40.75834605688442f, 4296.645356936966f,
-11234.776424245354f, 1115.363228090216f, 53813.817216417505f,
40299.07624778942f, 288431.507847565f},
{3.3827128857688793f, -76.61325300322648f, 4875.424130053332f,
-14585.199358667853f, 2322.162251501158f, 63618.048819648175f,
48278.83901554796f, 350657.56046107266f},
{3.4657288815375846f, -90.58976402034898f, 4946.538479838353f,
-14962.319670402725f, 2464.758450826609f, 64707.51942328425f,
49179.43246319585f, 357681.17176708044f}}};
// param[0]*dSlope_fringe + param[1]*ty dSlope_fringe_abs + param[2]*ty tx
// dSlope_fringe + param[3]*ty^3 dSlope_fringe_abs + param[4]*ty tx^2
// dSlope_fringe_abs + param[5]*ty^3 tx dSlope_fringe + param[6]*ty tx^3
// dSlope_fringe + param[7]*ty^3 tx^2 dSlope_fringe_abs
static constexpr std::array<float, 8> yCorrParamsRef{
2.5415524238347658f, 63.25841388467006f, 4187.534822693825f,
-10520.25391602297f, 881.6859925052617f, 51730.04107647908f,
38622.50428524951f, 275325.5721020971f};
// param[0]*ty tx dSlope_fringe + param[1]*ty dSlope_fringe^2 + param[2]*ty^3
// dSlope_fringe_abs + param[3]*ty tx^2 dSlope_fringe_abs + param[4]*ty tx^3
// dSlope_fringe + param[5]*ty^3 tx^2 dSlope_fringe_abs
static constexpr std::array<float, 6> tyCorrParamsRef{
0.9346197967408639f, -0.4658007458482092f, -4.119808929050499f,
2.9514781492224613f, 12.5961355543964f, 39.98472114588754f};
// param[0]*ty dSlope_fringe_abs + param[1]*ty tx dSlope_fringe + param[2]*ty
// dSlope_fringe^2 + param[3]*ty^3 dSlope_fringe_abs + param[4]*ty tx^2
// dSlope_fringe_abs
static constexpr std::array<float, 5> cyParams{
-1.2034772990836242e-05f, 8.344645618037317e-05f, -3.924972865228243e-05f,
0.00024639290417116324f, 0.0001867723161873795f};
// param[0]*ty dSlope_xEndT^2 + param[1]*ty dSlope_yEndT^2
static constexpr std::array<float, 2> bendYParams{-1974.6355416889746f,
-35933.837494833504f};

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parameterisations/result/veloUT_forward_ghost.hpp Normal file
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constexpr auto fMin =
std::array<float, 10>{{-5.79507064819, 0.000539337401278, 6.44962929073e-05,
9.1552734375e-05, 0, 0, 0, 1.27572263864e-09, 0, 0}};
constexpr auto fMax = std::array<float, 10>{
{21.3727073669, 7.9990901947, 139.93737793, 499.828094482, 139.837768555,
0.0549617446959, 0.281096696854, 0.00079078116687, 0.349586248398,
0.0576412677765}};
constexpr auto fWeightMatrix0to1 = std::array<std::array<float, 11>, 12>{
{{-5.19794815568925, 1.87197537853549, -1.10717797757926, -2.86970252748238,
-6.34055081230473, -5.00759687179371, 0.31193986693587, -8.01387747621716,
-0.120803639675188, -22.2071141316253, -38.8490339403707},
{20.4100238181115, 0.792781537499611, -2.30137034971144, 1.16999587784412,
0.889469621266034, 0.239667010403193, -0.969309214660315,
5.21461903375948, 0.348799845137009, -3.7347620794893, 0.25771517766665},
{-2.13629241669652, -0.353986293257471, 9.61086066828255,
-2.33661235049463, 1.93528728163342, -0.843722079096251,
0.999688424438972, -8.55711877917878, 1.02394998490757, -15.267288590286,
-13.235596389738},
{23.4650215184851, -0.726082462224456, 0.941255141296868,
-1.25219178023961, 1.87830610438659, -0.0626703177011942,
0.798264835732211, 2.24872165119832, 0.499173544293671, -1.38568068487979,
-2.73161981770289},
{2.57560609203081, 0.129379326313481, -4.39493675912134, 0.755378151374909,
-4.00108724778843, 0.203477875797884, -1.08477900245397,
-7.03054484748858, -1.09623688169448, -26.3844077677349,
-41.2491346895123},
{-0.768854476049515, 0.0931802742811194, 0.0821972098693852,
-3.89193022157862, 14.9873083975585, 1.28580626432386, -0.181845844796471,
-8.47241051331203, -0.943962517139566, 6.01103622570362,
9.48437982003848},
{0.178040151771032, 0.136134754761245, -0.787571684703651,
2.32182273660402, 7.30437794491034, -16.4278614062814, -0.113422629084682,
-1.0680200019718, -0.568606303594401, 1.0249516698155, -7.95920000382392},
{0.675546296267863, -0.385104312033727, -0.205638813395668,
-3.31653776098415, 10.5616483640347, 0.218068680685705, 0.605728183391577,
-4.63802179325434, -0.254055135097754, 5.28618861484003,
6.64663231117868},
{-4.21416802017974, -1.23106312717449, -1.75652044911551,
-1.53276007675347, -1.36027716886005, -1.6755055391045, 0.843394761591634,
6.71332112562865, -3.21984188557587, -2.49871614350354,
-5.16629424142249},
{-3.17947657486281, -2.70445335081074, 1.17896471286998, -1.88889268162012,
0.115966139146512, -1.69860246319196, -0.329404631248515,
-6.09206258757561, 3.39801515225553, -3.44730923665625,
-7.36048093738012},
{-12.1422669814385, -0.937172442798055, 2.04047673532808, -0.2149743778283,
-2.6487701795534, -2.62694503937013, -0.530083657842696, -14.923114328578,
-0.317336092090262, 23.1177068701846, 6.21557901179705},
{2.9112902347786, -4.39915513290615, -6.29077091232232, -3.50206937799633,
-0.367072002518522, 0.145186305220916, -6.21085587377794,
11.6250310635655, -7.43312502309394, 3.26005495282282,
-11.6647818884921}}};
constexpr auto fWeightMatrix1to2 = std::array<float, 13>{
{0.737121712699059, -1.04254794966614, -0.789570860582197, 1.11549851568754,
1.5426749194788, -1.2610505044793, -0.630483186934049, 1.57472828085649,
0.790891919516845, 0.937883258898894, -0.781778561713509,
-0.610089040707349, -0.385566736632017}};

10
parameterisations/result/z_mag_kink_params.hpp Normal file
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// param[0] + param[1]*tx^2 + param[2]*tx dSlope_fringe + param[3]*ty^2 +
// param[4]*dSlope_fringe^2
static constexpr std::array<float, 5> zMagnetParamsRef{
5205.144186525624f, -320.7206595710594f, 702.1384894815535f,
-316.36350963107543f, 441.59909857558097f};
// param[0] + param[1]*dSlope_xEndT_abs + param[2]*x_EndT_abs + param[3]*tx^2 +
// param[4]*dSlope_xEndT^2
static constexpr std::array<float, 5> zMagnetParamsEndT{
5286.687877988849f, -3.259689996453795f, 0.015615778872337033f,
-1377.3175211789967f, 282.9821232487341f};

261
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import os
import argparse
import ROOT
from ROOT import TMVA
def train_default_forward_ghost_mlp(
input_file: str = "data/ghost_data.root",
tree_name: str = "PrForwardTrackingVelo.PrMCDebugForwardTool/MVAInput",
exclude_electrons: bool = False,
n_train_signal: int = 300e3,
n_train_bkg: int = 300e3,
n_test_signal: int = 50e3,
n_test_bkg: int = 50e3,
prepare_data: bool = False,
):
"""Trains an MLP to classify track candidates from PrForwardTrackingVelo as ghost or Long Track.
Args:
input_file (str, optional): Defaults to "data/ghost_data.root".
tree_name (str, optional): Defaults to "PrForwardTrackingVelo.PrMCDebugForwardTool/MVAInput".
exclude_electrons (bool, optional): Defaults to False.
n_train_signal (int, optional): Number of true tracks to train on. Defaults to 750e3.
n_train_bkg (int, optional): Number of fake tracks to train on. Defaults to 750e3.
n_test_signal (int, optional): Number of true tracks to test on. Defaults to 50e3.
n_test_bkg (int, optional): umber of fake tracks to test on. Defaults to 50e3.
prepare_data (bool, optional): Split data into signal and background file. Defaults to False.
"""
if prepare_data:
rdf = ROOT.RDataFrame(tree_name, input_file)
if exclude_electrons:
rdf_signal = rdf.Filter(
"label == 1",
"Signal is defined as one label (excluding electrons)",
)
rdf_bkg = rdf.Filter("label == 0", "Ghosts are defined as zero label")
else:
rdf_signal = rdf.Filter("label > 0", "Signal is defined as non-zero label")
rdf_bkg = rdf.Filter("label == 0", "Ghosts are defined as zero label")
rdf_signal.Snapshot(
"Signal",
input_file.strip(".root") + "_forward_signal.root",
)
rdf_bkg.Snapshot("Bkg", input_file.strip(".root") + "_forward_bkg.root")
signal_file = ROOT.TFile.Open(
input_file.strip(".root") + "_forward_signal.root",
"READ",
)
signal_tree = signal_file.Get("Signal")
bkg_file = ROOT.TFile.Open(input_file.strip(".root") + "_forward_bkg.root")
bkg_tree = bkg_file.Get("Bkg")
os.chdir("neural_net_training/result")
output = ROOT.TFile(
"default_forward_ghost_mlp_training.root",
"RECREATE",
)
factory = TMVA.Factory(
"TMVAClassification",
output,
"V:!Silent:Color:DrawProgressBar:AnalysisType=Classification",
)
factory.SetVerbose(True)
dataloader = TMVA.DataLoader("GhostNNDataSet")
dataloader.AddVariable("redChi2", "F")
dataloader.AddVariable(
"distXMatch := abs((x + ( zMagMatch - 770.0 ) * tx) - (xEndT + ( zMagMatch - 9410.0 ) * txEndT))",
"F",
)
dataloader.AddVariable(
"distY := abs(ySeedMatch - yEndT)",
"F",
)
dataloader.AddVariable("abs(yParam0Final-yParam0Init)", "F")
dataloader.AddVariable("abs(yParam1Final-yParam1Init)", "F")
dataloader.AddVariable("abs(ty)", "F")
dataloader.AddVariable("abs(qop)", "F")
dataloader.AddVariable("abs(tx)", "F")
dataloader.AddVariable("abs(xParam1Final-xParam1Init)", "F")
dataloader.AddSignalTree(signal_tree, 1.0)
dataloader.AddBackgroundTree(bkg_tree, 1.0)
preselectionCuts = ROOT.TCut(
"redChi2 < 8 && abs((x + ( zMagMatch - 770.0 ) * tx) - (xEndT + ( zMagMatch - 9410.0 ) * txEndT)) < 140 && abs(ySeedMatch - yEndT) < 500 && abs(yParam0Final-yParam0Init) < 140 && abs(yParam1Final-yParam1Init) < 0.055",
)
dataloader.PrepareTrainingAndTestTree(
preselectionCuts,
f"NormMode=NumEvents:SplitMode=random:V:nTrain_Signal={n_train_signal}:nTrain_Background={n_train_bkg}:nTest_Signal={n_test_signal}:nTest_Background={n_test_bkg}",
)
factory.BookMethod(
dataloader,
TMVA.Types.kMLP,
"default_forward_ghost_mlp",
"!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=750:HiddenLayers=N+4,N+2:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.005:!UseRegulator",
)
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
output.Close()
def train_veloUT_forward_ghost_mlp(
input_file: str = "data/ghost_data.root",
tree_name: str = "PrForwardTracking.PrMCDebugForwardTool/MVAInput",
exclude_electrons: bool = False,
n_train_signal: int = 300e3,
n_train_bkg: int = 300e3,
n_test_signal: int = 50e3,
n_test_bkg: int = 50e3,
prepare_data: bool = False,
):
"""Trains an MLP to classify track candidates from PrForwardTracking as ghost or Long Track.
Args:
input_file (str, optional): Defaults to "data/ghost_data.root".
tree_name (str, optional): Defaults to "PrForwardTracking.PrMCDebugForwardTool/MVAInput".
exclude_electrons (bool, optional): Defaults to False.
n_train_signal (int, optional): Number of true tracks to train on. Defaults to 750e3.
n_train_bkg (int, optional): Number of fake tracks to train on. Defaults to 750e3.
n_test_signal (int, optional): Number of true tracks to test on. Defaults to 50e3.
n_test_bkg (int, optional): umber of fake tracks to test on. Defaults to 50e3.
prepare_data (bool, optional): Split data into signal and background file. Defaults to False.
"""
if prepare_data:
rdf = ROOT.RDataFrame(tree_name, input_file)
if exclude_electrons:
rdf_signal = rdf.Filter(
"label == 1",
"Signal is defined as one label (excluding electrons)",
)
rdf_bkg = rdf.Filter("label == 0", "Ghosts are defined as zero label")
else:
rdf_signal = rdf.Filter("label > 0", "Signal is defined as non-zero label")
rdf_bkg = rdf.Filter("label == 0", "Ghosts are defined as zero label")
rdf_signal.Snapshot(
"Signal",
input_file.strip(".root") + "_forward_velout_signal.root",
)
rdf_bkg.Snapshot("Bkg", input_file.strip(".root") + "_forward_velout_bkg.root")
signal_file = ROOT.TFile.Open(
input_file.strip(".root") + "_forward_velout_signal.root",
"READ",
)
signal_tree = signal_file.Get("Signal")
bkg_file = ROOT.TFile.Open(input_file.strip(".root") + "_forward_velout_bkg.root")
bkg_tree = bkg_file.Get("Bkg")
os.chdir("neural_net_training/result")
output = ROOT.TFile(
"veloUT_forward_ghost_mlp_training.root",
"RECREATE",
)
factory = TMVA.Factory(
"TMVAClassification",
output,
"V:!Silent:Color:DrawProgressBar:AnalysisType=Classification",
)
factory.SetVerbose(True)
dataloader = TMVA.DataLoader("GhostNNDataSet")
dataloader.AddVariable("dMom := log(abs((1.0/qop) - (1.0/qopUT) ))", "F")
dataloader.AddVariable("redChi2", "F")
dataloader.AddVariable(
"distXMatch := abs((x + ( zMagMatch - 770.0 ) * tx) - (xEndT + ( zMagMatch - 9410.0 ) * txEndT))",
"F",
)
dataloader.AddVariable(
"distY := abs(ySeedMatch - yEndT)",
"F",
)
dataloader.AddVariable("abs(yParam0Final-yParam0Init)", "F")
dataloader.AddVariable("abs(yParam1Final-yParam1Init)", "F")
dataloader.AddVariable("abs(ty)", "F")
dataloader.AddVariable("abs(qop)", "F")
dataloader.AddVariable("abs(tx)", "F")
dataloader.AddVariable("abs(xParam1Final-xParam1Init)", "F")
dataloader.AddSignalTree(signal_tree, 1.0)
dataloader.AddBackgroundTree(bkg_tree, 1.0)
preselectionCuts = ROOT.TCut(
"redChi2 < 8 && abs((x + ( zMagMatch - 770.0 ) * tx) - (xEndT + ( zMagMatch - 9410.0 ) * txEndT)) < 140 && abs(ySeedMatch - yEndT) < 500 && abs(yParam0Final-yParam0Init) < 140 && abs(yParam1Final-yParam1Init) < 0.055",
)
dataloader.PrepareTrainingAndTestTree(
preselectionCuts,
f"NormMode=NumEvents:SplitMode=random:V:nTrain_Signal={n_train_signal}:nTrain_Background={n_train_bkg}:nTest_Signal={n_test_signal}:nTest_Background={n_test_bkg}",
)
factory.BookMethod(
dataloader,
TMVA.Types.kMLP,
"veloUT_forward_ghost_mlp",
"!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=550:HiddenLayers=N+2:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.005:!UseRegulator",
)
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
output.Close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-file",
type=str,
help="Path to the input file",
required=False,
)
parser.add_argument(
"--exclude_electrons",
action="store_true",
help="Excludes electrons from training.",
required=False,
)
parser.add_argument(
"--n-train-signal",
type=int,
help="Number of training tracks for signal.",
required=False,
)
parser.add_argument(
"--n-train-bkg",
type=int,
help="Number of training tracks for bkg.",
required=False,
)
parser.add_argument(
"--n-test-signal",
type=int,
help="Number of testing tracks for signal.",
required=False,
)
parser.add_argument(
"--n-test-bkg",
type=int,
help="Number of testing tracks for bkg.",
required=False,
)
parser.add_argument(
"--veloUT",
action="store_true",
help="Toggle whether the NN for upstream tracks input is trained.",
)
parser.add_argument(
"--all",
action="store_true",
help="Toggle whether both NNs are trained, for VELO and VeloUT input.",
)
args = parser.parse_args()
args_dict = {
arg: val
for arg, val in vars(args).items()
if val is not None and arg not in ["veloUT", "all"]
}
if args.all:
train_default_forward_ghost_mlp(**args_dict)
train_veloUT_forward_ghost_mlp(**args_dict)
elif args.veloUT:
train_veloUT_forward_ghost_mlp(**args_dict)
else:
train_default_forward_ghost_mlp(**args_dict)

154
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# flake8: noqaq
import os
import argparse
import ROOT
from ROOT import TMVA
def train_matching_ghost_mlp(
input_file: str = "data/ghost_data_B.root",
tree_name: str = "PrMatchNN.PrMCDebugMatchToolNN/MVAInputAndOutput",
exclude_electrons: bool = True,
n_train_signal: int = 50e3,
n_train_bkg: int = 500e3,
n_test_signal: int = 10e3,
n_test_bkg: int = 100e3,
prepare_data: bool = True,
outdir: str = "neural_net_training",
):
"""Trains an MLP to classify the match between Velo and Seed track.
Args:
input_file (str, optional): Defaults to "data/ghost_data_B.root".
tree_name (str, optional): Defaults to "PrMatchNN.PrMCDebugMatchToolNN/Tuple".
exclude_electrons (bool, optional): Defaults to True.
n_train_signal (int, optional): Number of true matches to train on. Defaults to 200e3.
n_train_bkg (int, optional): Number of fake matches to train on. Defaults to 200e3.
n_test_signal (int, optional): Number of true matches to test on. Defaults to 75e3.
n_test_bkg (int, optional): Number of fake matches to test on. Defaults to 75e3.
prepare_data (bool, optional): Split data into signal and background file. Defaults to False.
"""
if prepare_data:
rdf = ROOT.RDataFrame(tree_name, input_file)
if exclude_electrons:
rdf_signal = rdf.Filter(
"quality == 1", # -1 elec, 0 ghost, 1 all part wo elec
"Signal is defined as one label (excluding electrons)",
)
rdf_bkg = rdf.Filter("quality == 0", "Ghosts are defined as zero label")
else:
rdf_signal = rdf.Filter(
"abs(quality) > 0",
"Signal is defined as non-zero label",
)
rdf_bkg = rdf.Filter("quality == 0", "Ghosts are defined as zero label")
rdf_signal.Snapshot(
"Signal",
outdir + "/" + input_file.strip(".root") + "_matching_signal.root",
)
rdf_bkg.Snapshot(
"Bkg", outdir + "/" + input_file.strip(".root") + "_matching_bkg.root"
)
signal_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_matching_signal.root",
"READ",
)
signal_tree = signal_file.Get("Signal")
bkg_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_matching_bkg.root"
)
bkg_tree = bkg_file.Get("Bkg")
os.chdir(outdir + "/result")
output = ROOT.TFile(
"matching_ghost_mlp_training.root",
"RECREATE",
)
factory = TMVA.Factory(
"TMVAClassification",
output,
"V:!Silent:Color:DrawProgressBar:AnalysisType=Classification",
)
factory.SetVerbose(True)
dataloader = TMVA.DataLoader("MatchNNDataSet")
dataloader.AddVariable("chi2", "F")
dataloader.AddVariable("teta2", "F")
dataloader.AddVariable("distX", "F")
dataloader.AddVariable("distY", "F")
dataloader.AddVariable("dSlope", "F")
dataloader.AddVariable("dSlopeY", "F")
# dataloader.AddVariable("tx", "F")
# dataloader.AddVariable("ty", "F")
# dataloader.AddVariable("tx_scifi", "F")
# dataloader.AddVariable("ty_scifi", "F")
dataloader.AddSignalTree(signal_tree, 1.0)
dataloader.AddBackgroundTree(bkg_tree, 1.0)
# these cuts are also applied in the algorithm
preselectionCuts = ROOT.TCut(
"chi2<15 && distX<250 && distY<400 && dSlope<1.5 && dSlopeY<0.15", #### ganz raus für elektronen
)
dataloader.PrepareTrainingAndTestTree(
preselectionCuts,
f"SplitMode=random:V:nTrain_Signal={n_train_signal}:nTrain_Background={n_train_bkg}:nTest_Signal={n_test_signal}:nTest_Background={n_test_bkg}",
)
factory.BookMethod(
dataloader,
TMVA.Types.kMLP,
"matching_mlp",
"!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator",
)
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
output.Close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-file",
type=str,
help="Path to the input file",
required=False,
)
parser.add_argument(
"--exclude_electrons",
action="store_true",
help="Excludes electrons from training.",
required=False,
)
parser.add_argument(
"--n-train-signal",
type=int,
help="Number of training tracks for signal.",
required=False,
)
parser.add_argument(
"--n-train-bkg",
type=int,
help="Number of training tracks for bkg.",
required=False,
)
parser.add_argument(
"--n-test-signal",
type=int,
help="Number of testing tracks for signal.",
required=False,
)
parser.add_argument(
"--n-test-bkg",
type=int,
help="Number of testing tracks for bkg.",
required=False,
)
args = parser.parse_args()
args_dict = {arg: val for arg, val in vars(args).items() if val is not None}
train_matching_ghost_mlp(**args_dict)

177
parameterisations/train_matching_ghost_mlps_electron.py Normal file
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# flake8: noqaq
import os
import argparse
import ROOT
from ROOT import TMVA, TList, TTree
def train_matching_ghost_mlp(
input_file: str = "data/ghost_data_B.root",
tree_name: str = "PrMatchNN.PrMCDebugMatchToolNN/MVAInputAndOutput",
exclude_electrons: bool = False,
only_electrons: bool = True,
n_train_signal: int = 20e3, # 50e3
n_train_bkg: int = 50e3, # 500e3
n_test_signal: int = 10e3,
n_test_bkg: int = 20e3,
prepare_data: bool = True,
outdir: str = "nn_electron_training",
):
"""Trains an MLP to classify the match between Velo and Seed track.
Args:
input_file (str, optional): Defaults to "data/ghost_data.root".
tree_name (str, optional): Defaults to "PrMatchNN.PrMCDebugMatchToolNN/Tuple".
exclude_electrons (bool, optional): Defaults to False.
only_electrons (bool, optional): Signal only of electrons, but bkg of all particles. Defaults to True.
n_train_signal (int, optional): Number of true matches to train on. Defaults to 200e3.
n_train_bkg (int, optional): Number of fake matches to train on. Defaults to 200e3.
n_test_signal (int, optional): Number of true matches to test on. Defaults to 75e3.
n_test_bkg (int, optional): Number of fake matches to test on. Defaults to 75e3.
prepare_data (bool, optional): Split data into signal and background file. Defaults to False.
"""
if prepare_data:
rdf = ROOT.RDataFrame(tree_name, input_file)
if exclude_electrons:
rdf_signal = rdf.Filter(
"mc_quality == 1", # -1 elec, 0 ghost, 1 all part wo elec
"Signal is defined as one label (excluding electrons)",
)
rdf_bkg = rdf.Filter(
"mc_quality == 0",
"Ghosts are defined as zero label",
)
else:
if only_electrons:
rdf_signal = rdf.Filter(
"mc_quality == -1", # electron that is true match but mlp said no match
"Signal is defined as negative one label (only electrons)",
)
else:
rdf_signal = rdf.Filter(
"abs(mc_quality) > 0",
"Signal is defined as non-zero label",
)
rdf_bkg = rdf.Filter(
"mc_quality == 0",
"Ghosts are defined as zero label",
)
rdf_signal.Snapshot(
"Signal",
outdir + "/" + input_file.strip(".root") + "_matching_signal.root",
)
rdf_bkg.Snapshot(
"Bkg",
outdir + "/" + input_file.strip(".root") + "_matching_bkg.root",
)
signal_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_matching_signal.root",
"READ",
)
signal_tree = signal_file.Get("Signal")
bkg_file = ROOT.TFile.Open(
outdir + "/" + input_file.strip(".root") + "_matching_bkg.root"
)
bkg_tree = bkg_file.Get("Bkg")
os.chdir(outdir + "/result")
output = ROOT.TFile(
"matching_ghost_mlp_training.root",
"RECREATE",
)
factory = TMVA.Factory(
"TMVAClassification",
output,
"V:!Silent:Color:DrawProgressBar:AnalysisType=Classification",
)
factory.SetVerbose(True)
dataloader = TMVA.DataLoader("MatchNNDataSet")
dataloader.AddVariable("mc_chi2", "F")
dataloader.AddVariable("mc_teta2", "F")
dataloader.AddVariable("mc_distX", "F")
dataloader.AddVariable("mc_distY", "F")
dataloader.AddVariable("mc_dSlope", "F")
dataloader.AddVariable("mc_dSlopeY", "F")
dataloader.AddVariable("mc_zMag", "F")
dataloader.AddSignalTree(signal_tree, 1.0)
dataloader.AddBackgroundTree(bkg_tree, 1.0)
# these cuts are also applied in the algorithm
preselectionCuts = ROOT.TCut(
# "chi2<30 && distX<500 && distY<500 && dSlope<2.0 && dSlopeY<0.15", #### ganz raus für elektronen
)
dataloader.PrepareTrainingAndTestTree(
preselectionCuts,
f"SplitMode=random:V:nTrain_Signal={n_train_signal}:nTrain_Background={n_train_bkg}:nTest_Signal={n_test_signal}:nTest_Background={n_test_bkg}",
# normmode default is EqualNumEvents
)
factory.BookMethod(
dataloader,
TMVA.Types.kMLP,
"matching_mlp",
"!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator",
)
factory.TrainAllMethods()
factory.TestAllMethods()
factory.EvaluateAllMethods()
output.Close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-file",
type=str,
help="Path to the input file",
required=False,
)
parser.add_argument(
"--exclude_electrons",
action="store_true",
help="Excludes electrons from training.",
required=False,
)
parser.add_argument(
"--only_electrons",
action="store_true",
help="Only electrons for signal training.",
required=False,
)
parser.add_argument(
"--n-train-signal",
type=int,
help="Number of training tracks for signal.",
required=False,
)
parser.add_argument(
"--n-train-bkg",
type=int,
help="Number of training tracks for bkg.",
required=False,
)
parser.add_argument(
"--n-test-signal",
type=int,
help="Number of testing tracks for signal.",
required=False,
)
parser.add_argument(
"--n-test-bkg",
type=int,
help="Number of testing tracks for bkg.",
required=False,
)
args = parser.parse_args()
args_dict = {arg: val for arg, val in vars(args).items() if val is not None}
train_matching_ghost_mlp(**args_dict)

91
parameterisations/utils/fit_linear_regression_model.py Normal file
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import awkward as ak
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import numpy as np
def fit_linear_regression_model(
array: ak.Array,
target_feat: str,
features: list[str],
degree: int,
keep: list[str] = None,
keep_only_linear_in: str = "",
remove: list[str] = None,
include_bias: bool = False,
fit_intercept: bool = False,
test_size=0.2,
random_state=42,
) -> tuple[LinearRegression, list[str]]:
"""Wrapper around sklearn's LinearRegression with PolynomialFeatures.
Args:
array (ak.Array): The data.
target_feat (str): Target feature to be fitted.
features (list[str]): Features the target depends on.
degree (int): Highest order of the polynomial.
keep (list[str], optional): Monomials to keep. Defaults to None.
keep_only_linear_in (str, optional): Keep only terms that are linear in this feature. Defaults to "".
remove (list[str], optional): Monomials to remove. Defaults to None.
include_bias (bool, optional): Inlcude bias term in polynomial. Defaults to False.
fit_intercept (bool, optional): Fit zeroth order. Defaults to False.
test_size (float, optional): Fraction of data used for testing. Defaults to 0.2.
random_state (int, optional): Defaults to 42.
Raises:
NotImplementedError: Simultaneous removing and keeping is not implemented.
Returns:
tuple[LinearRegression, list[str]]: The linear regression object and the kept features.
"""
data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])
target = ak.to_numpy(array[target_feat])
X_train, X_test, y_train, y_test = train_test_split(
data,
target,
test_size=test_size,
random_state=random_state,
)
poly = PolynomialFeatures(degree=degree, include_bias=include_bias)
X_train_model = poly.fit_transform(X_train)
X_test_model = poly.fit_transform(X_test)
poly_features = poly.get_feature_names_out(input_features=features)
if not remove:
if keep:
remove = [i for i, f in enumerate(poly_features) if f not in keep]
elif keep_only_linear_in:
# remove everything that's not linear in variable
# the corrections should vanish
remove = [
i
for i, f in enumerate(poly_features)
if (keep_only_linear_in not in f) or (keep_only_linear_in + "^" in f)
]
else:
remove = []
elif remove and keep:
raise NotImplementedError
X_train_model = np.delete(X_train_model, remove, axis=1)
X_test_model = np.delete(X_test_model, remove, axis=1)
poly_features = np.delete(poly_features, remove)
lin_reg = LinearRegression(fit_intercept=fit_intercept)
lin_reg.fit(X_train_model, y_train)
y_pred_test = lin_reg.predict(X_test_model)
print(f"Parameterisation for {target_feat}:")
print("intercept=", lin_reg.intercept_)
print(
"coef=",
dict(
zip(
poly_features,
lin_reg.coef_,
),
),
)
print("r2 score=", lin_reg.score(X_test_model, y_test))
print("RMSE =", mean_squared_error(y_test, y_pred_test, squared=False))
print()
return (lin_reg, poly_features)

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parameterisations/utils/parse_regression_coef_to_array.py Normal file
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from sklearn.linear_model import LinearRegression
def parse_regression_coef_to_array(
model: LinearRegression,
poly_features: list[str],
array_name: str,
rows: list[str] = [],
) -> list[str]:
"""Convenient function to parse the model coefficients into a cpp code string.
Args:
model (LinearRegression): A fitted linear regression model.
poly_features (list[str]): A list with the names of the polynomial features.
array_name (str): The name of the created cpp array.
rows (list[str], optional): In case of a matrix, list of rows. Defaults to [].
Returns:
list[str]: List of strings, first entry is the comment, second the cpp code.
"""
intercept = model.intercept_ != 0.0
indices = [i for i in range(len(poly_features)) if model.coef_[i] != 0.0]
feature_comment = (
("// param[0] + " if intercept else "// ")
+ " + ".join(
[
f"param[{idx}]*{poly_features[param_index]}"
for idx, param_index in enumerate(indices, start=intercept)
],
)
+ "\n"
)
n_col = sum(model.coef_ != 0.0) + model.fit_intercept
if not rows:
cpp_decl = f"static constexpr std::array<float, {n_col}> {array_name}"
cpp_decl += (
"{"
+ (str(model.intercept_) + "f," if intercept else "")
+ ",".join([str(coef) + "f" for coef in model.coef_ if coef != 0.0])
+ "};\n"
)
return [feature_comment, cpp_decl]
else:
n_row = len(rows)
cpp_decl = (
f"static constexpr std::array<std::array<float, {n_col}>, {n_row}> {array_name}"
+ "{{"
)
cpp_decl += ",".join(rows)
cpp_decl += "}};\n"
return [feature_comment, cpp_decl]

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import re
from pathlib import Path
def parse_tmva_matrix_to_array(
input_class_files: list[str],
simd_type: bool = False,
outdir: str = "neural_net_training",
) -> list[str]:
"""Function to transform the TMVA output MLP C++ class into a more modern form.
Args:
input_class_file (str): Path to the .C MLP class created by TMVA.
simd_type (bool, optional): If true, type in array is set to simd::float_v. Defaults to False.
Returns:
(str) : Path to the resulting C++ file containing the matrices.
Note:
The created C++ code is written to a `hpp` file in `neural_net_training/result`.
"""
data_type = "float" if not simd_type else "simd::float_v"
# TODO: as of writing this code, constexpr is not supported for the SIMD types
constness = "constexpr" if not simd_type else "const"
outfiles = []
for input_class_file in input_class_files:
print(f"Transforming {input_class_file} ...")
input_class_file = Path(input_class_file)
with open(input_class_file) as f:
lines = f.readlines()
# the name of the outputfile is the middle part of the input class file name
outfile = (
outdir
+ "/result/"
+ "_".join(str(input_class_file.stem).split("_")[1:-1])
+ ".hpp"
)
# this only supports category 2 for the transformation, which is the largest/smallest for fMax_1/fMin_1
min_lines = [
line.replace(";", "").split("=")[-1].strip()
for line in lines
if "fMin_1[2]" in line and "Scal" not in line and "double" not in line
]
max_lines = [
line.replace(";", "").split("=")[-1].strip()
for line in lines
if "fMax_1[2]" in line and "Scal" not in line and "double" not in line
]
minima_cpp_decl = (
f"{constness} auto fMin = std::array<{data_type}, {len(min_lines)}>"
+ "{{"
+ ", ".join(min_lines)
+ "}};\n"
)
maxima_cpp_decl = (
f"{constness} auto fMax = std::array<{data_type}, {len(max_lines)}>"
+ "{{"
+ ", ".join(max_lines)
+ "}};\n"
)
print(f"Found minimum and maximum values for {len(min_lines)} variables.")
with open(outfile, "w") as out:
out.writelines([minima_cpp_decl, maxima_cpp_decl])
# this list contains all lines that define a matrix element
matrix_lines = [
line.replace(";", "").strip()
for line in lines
if "fWeightMatrix" in line
and "double" not in line
and "*" not in line
and "= fWeightMatrix" not in line
]
# there are several matrices, figure out how many and loop accordingly
n_matrices = int(re.findall(re.compile(r"(\d+)\["), matrix_lines[-1])[0])
print(f"Found {n_matrices} matrices: ")
for matrix in range(n_matrices):
# get only entries for corresponding matrix
matrix_entries = [m for m in matrix_lines if f"{matrix}to{(matrix+1)}" in m]
# figure out the dimensions of the matrix, by checking largest index at the end
dim_string = re.findall(re.compile(r"\[(\d+)\]"), matrix_entries[-1])
# actual size is last index + 1
n_rows = int(dim_string[0]) + 1
n_cols = int(dim_string[1]) + 1
# get the name of the matrix
matrix_string = matrix_entries[matrix * n_rows].split("=")[0].split("[")[0]
if n_rows > 1:
cpp_decl = (
f"{constness} auto {matrix_string} = std::array<std::array<{data_type}, {n_cols}>, {n_rows}>"
+ "{{"
)
else:
cpp_decl = (
f"{constness} auto {matrix_string} = std::array<{data_type}, {n_cols}>"
+ "{"
)
rows = [[] for _ in range(n_rows)]
for i_col in range(n_cols):
for i_row, row in enumerate(rows):
# only keep number (right side of the equality sign)
entry = (
matrix_entries[i_col * n_rows + i_row].split("=")[-1].strip()
)
row.append(entry)
array_strings = ["{" + ", ".join(row) + "}" for row in rows]
cpp_decl += ", ".join(array_strings) + ("}};\n" if n_rows > 1 else "};\n")
print(
f" {matrix+1}. {matrix_string} with {n_cols} columns and {n_rows} rows",
)
with open(outfile, "a") as out:
out.write(cpp_decl)
outfiles.append(outfile)
return outfiles
if __name__ == "__main__":
outfiles = parse_tmva_matrix_to_array(
[
"neural_net_training/result/GhostNNDataSet/weights/TMVAClassification_default_forward_ghost_mlp.class.C",
"neural_net_training/result/GhostNNDataSet/weights/TMVAClassification_veloUT_forward_ghost_mlp.class.C",
],
)
try:
import subprocess
# run clang-format for nicer looking result
for outfile in outfiles:
subprocess.run(
[
"clang-format",
"-i",
f"{outfile}",
],
check=True,
)
except:
pass

142
parameterisations/utils/parse_tmva_matrix_to_array_TrLo.py Normal file
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import re
from pathlib import Path
# flake8: noqaq
def parse_tmva_matrix_to_array(
input_class_files: list[str],
simd_type: bool = False,
outdir: str = "nn_trackinglosses_training",
) -> list[str]:
"""Function to transform the TMVA output MLP C++ class into a more modern form.
Args:
input_class_file (str): Path to the .C MLP class created by TMVA.
simd_type (bool, optional): If true, type in array is set to simd::float_v. Defaults to False.
Returns:
(str) : Path to the resulting C++ file containing the matrices.
Note:
The created C++ code is written to a `hpp` file in `neural_net_training/result`.
"""
data_type = "float" if not simd_type else "simd::float_v"
# TODO: as of writing this code, constexpr is not supported for the SIMD types
constness = "constexpr" if not simd_type else "const"
outfiles = []
for input_class_file in input_class_files:
print(f"Transforming {input_class_file} ...")
input_class_file = Path(input_class_file)
with open(input_class_file) as f:
lines = f.readlines()
# the name of the outputfile is the middle part of the input class file name
outfile = (
outdir
+ "/result/"
+ "_".join(str(input_class_file.stem).split("_")[1:-1])
+ ".hpp"
)
# this only supports category 2 for the transformation, which is the largest/smallest for fMax_1/fMin_1
min_lines = [
line.replace(";", "").split("=")[-1].strip()
for line in lines
if "fMin_1[2]" in line and "Scal" not in line and "double" not in line
]
max_lines = [
line.replace(";", "").split("=")[-1].strip()
for line in lines
if "fMax_1[2]" in line and "Scal" not in line and "double" not in line
]
minima_cpp_decl = (
f"{constness} auto ResfMin = std::array<{data_type}, {len(min_lines)}>"
+ "{{"
+ ", ".join(min_lines)
+ "}};\n"
)
maxima_cpp_decl = (
f"{constness} auto ResfMax = std::array<{data_type}, {len(max_lines)}>"
+ "{{"
+ ", ".join(max_lines)
+ "}};\n"
)
print(f"Found minimum and maximum values for {len(min_lines)} variables.")
with open(outfile, "w") as out:
out.writelines([minima_cpp_decl, maxima_cpp_decl])
# this list contains all lines that define a matrix element
matrix_lines = [
line.replace(";", "").strip()
for line in lines
if "fWeightMatrix" in line
and "double" not in line
and "*" not in line
and "= fWeightMatrix" not in line
]
# there are several matrices, figure out how many and loop accordingly
n_matrices = int(re.findall(re.compile(r"(\d+)\["), matrix_lines[-1])[0])
print(f"Found {n_matrices} matrices: ")
for matrix in range(n_matrices):
# get only entries for corresponding matrix
matrix_entries = [m for m in matrix_lines if f"{matrix}to{(matrix+1)}" in m]
# figure out the dimensions of the matrix, by checking largest index at the end
dim_string = re.findall(re.compile(r"\[(\d+)\]"), matrix_entries[-1])
# actual size is last index + 1
n_rows = int(dim_string[0]) + 1
n_cols = int(dim_string[1]) + 1
# get the name of the matrix
matrix_string = matrix_entries[matrix * n_rows].split("=")[0].split("[")[0]
if n_rows > 1:
cpp_decl = (
f"{constness} auto Res{matrix_string} = std::array<std::array<{data_type}, {n_cols}>, {n_rows}>"
+ "{{"
)
else:
cpp_decl = (
f"{constness} auto Res{matrix_string} = std::array<{data_type}, {n_cols}>"
+ "{"
)
rows = [[] for _ in range(n_rows)]
for i_col in range(n_cols):
for i_row, row in enumerate(rows):
# only keep number (right side of the equality sign)
entry = (
matrix_entries[i_col * n_rows + i_row].split("=")[-1].strip()
)
row.append(entry)
array_strings = ["{" + ", ".join(row) + "}" for row in rows]
cpp_decl += ", ".join(array_strings) + ("}};\n" if n_rows > 1 else "};\n")
print(
f" {matrix+1}. {matrix_string} with {n_cols} columns and {n_rows} rows",
)
with open(outfile, "a") as out:
out.write(cpp_decl)
outfiles.append(outfile)
return outfiles
if __name__ == "__main__":
outfiles = parse_tmva_matrix_to_array(
[
"nn_trackinglosses_training/result/GhostNNDataSet/weights/TMVAClassification_default_forward_ghost_mlp.class.C",
"nn_trackinglosses_training/result/GhostNNDataSet/weights/TMVAClassification_veloUT_forward_ghost_mlp.class.C",
],
)
try:
import subprocess
# run clang-format for nicer looking result
for outfile in outfiles:
subprocess.run(
[
"clang-format",
"-i",
f"{outfile}",
],
check=True,
)
except:
pass

142
parameterisations/utils/parse_tmva_matrix_to_array_electron.py Normal file
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import re
from pathlib import Path
# flake8: noqaq
def parse_tmva_matrix_to_array(
input_class_files: list[str],
simd_type: bool = False,
outdir: str = "nn_electron_training",
) -> list[str]:
"""Function to transform the TMVA output MLP C++ class into a more modern form.
Args:
input_class_file (str): Path to the .C MLP class created by TMVA.
simd_type (bool, optional): If true, type in array is set to simd::float_v. Defaults to False.
Returns:
(str) : Path to the resulting C++ file containing the matrices.
Note:
The created C++ code is written to a `hpp` file in `nn_electron_training/result`.
"""
data_type = "float" if not simd_type else "simd::float_v"
# TODO: as of writing this code, constexpr is not supported for the SIMD types
constness = "constexpr" if not simd_type else "const"
outfiles = []
for input_class_file in input_class_files:
print(f"Transforming {input_class_file} ...")
input_class_file = Path(input_class_file)
with open(input_class_file) as f:
lines = f.readlines()
# the name of the outputfile is the middle part of the input class file name
outfile = (
outdir
+ "/result/"
+ "_".join(str(input_class_file.stem).split("_")[1:-1])
+ ".hpp"
)
# this only supports category 2 for the transformation, which is the largest/smallest for fMax_1/fMin_1
min_lines = [
line.replace(";", "").split("=")[-1].strip()
for line in lines
if "fMin_1[2]" in line and "Scal" not in line and "double" not in line
]
max_lines = [
line.replace(";", "").split("=")[-1].strip()
for line in lines
if "fMax_1[2]" in line and "Scal" not in line and "double" not in line
]
minima_cpp_decl = (
f"{constness} auto fMin = std::array<{data_type}, {len(min_lines)}>"
+ "{{"
+ ", ".join(min_lines)
+ "}};\n"
)
maxima_cpp_decl = (
f"{constness} auto fMax = std::array<{data_type}, {len(max_lines)}>"
+ "{{"
+ ", ".join(max_lines)
+ "}};\n"
)
print(f"Found minimum and maximum values for {len(min_lines)} variables.")
with open(outfile, "w") as out:
out.writelines([minima_cpp_decl, maxima_cpp_decl])
# this list contains all lines that define a matrix element
matrix_lines = [
line.replace(";", "").strip()
for line in lines
if "fWeightMatrix" in line
and "double" not in line
and "*" not in line
and "= fWeightMatrix" not in line
]
# there are several matrices, figure out how many and loop accordingly
n_matrices = int(re.findall(re.compile(r"(\d+)\["), matrix_lines[-1])[0])
print(f"Found {n_matrices} matrices: ")
for matrix in range(n_matrices):
# get only entries for corresponding matrix
matrix_entries = [m for m in matrix_lines if f"{matrix}to{(matrix+1)}" in m]
# figure out the dimensions of the matrix, by checking largest index at the end
dim_string = re.findall(re.compile(r"\[(\d+)\]"), matrix_entries[-1])
# actual size is last index + 1
n_rows = int(dim_string[0]) + 1
n_cols = int(dim_string[1]) + 1
# get the name of the matrix
matrix_string = matrix_entries[matrix * n_rows].split("=")[0].split("[")[0]
if n_rows > 1:
cpp_decl = (
f"{constness} auto {matrix_string} = std::array<std::array<{data_type}, {n_cols}>, {n_rows}>"
+ "{{"
)
else:
cpp_decl = (
f"{constness} auto {matrix_string} = std::array<{data_type}, {n_cols}>"
+ "{"
)
rows = [[] for _ in range(n_rows)]
for i_col in range(n_cols):
for i_row, row in enumerate(rows):
# only keep number (right side of the equality sign)
entry = (
matrix_entries[i_col * n_rows + i_row].split("=")[-1].strip()
)
row.append(entry)
array_strings = ["{" + ", ".join(row) + "}" for row in rows]
cpp_decl += ", ".join(array_strings) + ("}};\n" if n_rows > 1 else "};\n")
print(
f" {matrix+1}. {matrix_string} with {n_cols} columns and {n_rows} rows",
)
with open(outfile, "a") as out:
out.write(cpp_decl)
outfiles.append(outfile)
return outfiles
if __name__ == "__main__":
outfiles = parse_tmva_matrix_to_array(
[
"nn_electron_training/result/GhostNNDataSet/weights/TMVAClassification_default_forward_ghost_mlp.class.C",
"nn_electron_training/result/GhostNNDataSet/weights/TMVAClassification_veloUT_forward_ghost_mlp.class.C",
],
)
try:
import subprocess
# run clang-format for nicer looking result
for outfile in outfiles:
subprocess.run(
[
"clang-format",
"-i",
f"{outfile}",
],
check=True,
)
except:
pass

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parameterisations/utils/preselection.py Normal file
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import ROOT
def preselection(
cuts: str = "",
input_file: str = None,
outfile_postfix: str = "selected",
tree_name: str = "PrParameterisationData.PrMCDebugReconstructibleLong/Tuple",
) -> str:
"""Function that apply a selection to given data.
Args:
cuts (str, optional): String specifying the selection. Defaults to "".
input_file (str, optional): Defaults to None.
outfile_postfix (str, optional): Defaults to "selected".
tree_name (str, optional): Defaults to "PrParameterisationData.PrMCDebugReconstructibleLong/Tuple".
Returns:
str: Path to the output file.
"""
rdf = ROOT.RDataFrame(tree_name, input_file)
rdf = rdf.Filter(cuts, "Selection")
out_file = input_file.strip(".root") + f"_{outfile_postfix}.root"
rdf.Snapshot("Selected", out_file)
return out_file
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-file",
type=str,
help="Path to the input file",
)
parser.add_argument(
"--cuts",
type=str,
default="chi2_comb < 5 && pt > 10 && p > 1500 && p < 100000 && pid != 11",
help="Cuts of the preselection",
)
args = parser.parse_args()
preselection(**vars(args))

464
scripts/BakPrCheckerEfficiency.py Normal file
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# flake8: noqa
import argparse
from ROOT import TMultiGraph, TLatex, TCanvas, TFile, TGaxis
from ROOT import kGreen, kBlue, kBlack, kAzure, kOrange, kMagenta, kCyan
from ROOT import gROOT, gStyle, gPad
from ROOT import TEfficiency
from array import array
gROOT.SetBatch(True)
def getEfficiencyHistoNames():
return ["p", "pt", "phi", "eta", "nPV"]
def getTrackers(trackers):
return trackers
def getOriginFolders():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
}
basedict["Velo"]["folder"] = "VeloTrackChecker/"
basedict["Upstream"]["folder"] = "UpstreamTrackChecker/"
basedict["Forward"]["folder"] = "ForwardTrackChecker/"
basedict["Match"]["folder"] = "MatchTrackChecker/"
basedict["BestLong"]["folder"] = "BestLongTrackChecker/"
return basedict
def getTrackNames():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
}
basedict["Velo"] = "Velo"
basedict["Upstream"] = "VeloUT"
basedict["Forward"] = "Forward"
basedict["Match"] = "Match"
basedict["BestLong"] = "BestLong"
return basedict
def get_colors():
return [kBlack, kAzure, kGreen + 3, kMagenta + 2, kOrange, kCyan + 2]
def get_markers():
return [20, 24, 21, 22, 23, 25]
def get_fillstyles():
return [1003, 1002, 3002, 3325, 3144, 3244, 3444]
def getGhostHistoNames():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
}
basedict["Velo"] = ["eta", "nPV"]
basedict["Upstream"] = ["eta", "p", "pt", "nPV"]
basedict["Forward"] = ["eta", "p", "pt", "nPV"]
basedict["Match"] = basedict["Forward"]
basedict["BestLong"] = basedict["Forward"]
return basedict
def argument_parser():
parser = argparse.ArgumentParser(description="location of the tuple file")
parser.add_argument(
"--filename",
type=str,
default=["data/resolutions_and_effs_Bs2PhiPhi_MD_default.root"],
nargs="+",
help="input files, including path",
)
parser.add_argument(
"--outfile",
type=str,
default="data/efficiency_plots.root",
help="output file",
)
parser.add_argument(
"--trackers",
type=str,
nargs="+",
default=["Forward", "Match", "BestLong"],
help="Trackers to plot.",
)
parser.add_argument(
"--label",
nargs="+",
default=["EffChecker"],
help="label for files",
)
parser.add_argument(
"--savepdf",
action="store_true",
help="save plots in pdf format",
)
parser.add_argument(
"--plot-electrons",
action="store_true",
help="plot electrons")
parser.add_argument(
"--plot-electrons-only",
action="store_true",
help="plot only electrons",
)
return parser
def get_files(tf, filename, label):
for i, f in enumerate(filename):
tf[label[i]] = TFile(f, "read")
return tf
def get_nicer_var_string(var: str):
nice_vars = dict(pt="p_{T}", eta="#eta", phi="#phi")
try:
return nice_vars[var]
except KeyError:
return var
def get_eff(eff, hist, tf, histoName, label, var):
eff = {}
hist = {}
var = get_nicer_var_string(var)
for i, lab in enumerate(label):
numeratorName = histoName + "_reconstructed"
numerator = tf[lab].Get(numeratorName)
denominatorName = histoName + "_reconstructible"
denominator = tf[lab].Get(denominatorName)
if numerator.GetEntries() == 0 or denominator.GetEntries() == 0:
continue
teff = TEfficiency(numerator, denominator)
teff.SetStatisticOption(7)
eff[lab] = teff.CreateGraph()
eff[lab].SetName(lab)
eff[lab].SetTitle(lab + " not e^{-}")
if histoName.find("strange") != -1:
eff[lab].SetTitle(lab + " from stranges")
if histoName.find("electron") != -1:
eff[lab].SetTitle(lab + " e^{-}")
hist[lab] = denominator.Clone()
hist[lab].SetName("h_numerator_notElectrons")
hist[lab].SetTitle(var + " distribution, not e^{-}")
if histoName.find("strange") != -1:
hist[lab].SetTitle(var + " distribution, stranges")
if histoName.find("electron") != -1:
hist[lab].SetTitle(var + " distribution, e^{-}")
return eff, hist
def get_ghost(eff, hist, tf, histoName, label):
ghost = {}
for i, lab in enumerate(label):
numeratorName = histoName + "_Ghosts"
denominatorName = histoName + "_Total"
numerator = tf[lab].Get(numeratorName)
denominator = tf[lab].Get(denominatorName)
print("Numerator = " + numeratorName)
print("Denominator = " + denominatorName)
teff = TEfficiency(numerator, denominator)
teff.SetStatisticOption(7)
ghost[lab] = teff.CreateGraph()
print(lab)
ghost[lab].SetName(lab)
return ghost
def PrCheckerEfficiency(
filename,
outfile,
label,
trackers,
savepdf,
plot_electrons,
plot_electrons_only,
):
from utils.LHCbStyle import setLHCbStyle, set_style
from utils.ConfigHistos import (
efficiencyHistoDict,
ghostHistoDict,
categoriesDict,
getCuts,
)
from utils.Legend import place_legend
setLHCbStyle()
markers = get_markers()
colors = get_colors()
styles = get_fillstyles()
tf = {}
tf = get_files(tf, filename, label)
outputfile = TFile(outfile, "recreate")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.05)
efficiencyHistoDict = efficiencyHistoDict()
efficiencyHistos = getEfficiencyHistoNames()
ghostHistos = getGhostHistoNames()
ghostHistoDict = ghostHistoDict()
categories = categoriesDict()
cuts = getCuts()
trackers = getTrackers(trackers)
folders = getOriginFolders()
# names = getTrackNames()
for tracker in trackers:
outputfile.cd()
trackerDir = outputfile.mkdir(tracker)
trackerDir.cd()
for cut in cuts[tracker]:
cutDir = trackerDir.mkdir(cut)
cutDir.cd()
folder = folders[tracker]["folder"]
print(folder)
histoBaseName = "Track/" + folder + tracker + "/" + cut + "_"
# calculate efficiency
for histo in efficiencyHistos:
canvastitle = (
"efficiency_" + histo + ", " + categories[tracker][cut]["title"]
)
# get efficiency for not electrons category
histoName = histoBaseName + "" + efficiencyHistoDict[histo]["variable"]
print("not electrons: " + histoName)
eff = {}
hist_den = {}
eff, hist_den = get_eff(eff, hist_den, tf, histoName, label, histo)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
histoNameElec = (
"Track/"
+ folder
+ tracker
+ "/"
+ categories[tracker][cut]["Electrons"]
)
histoName_e = (
histoNameElec + "_" + efficiencyHistoDict[histo]["variable"]
)
print("electrons: " + histoName_e)
eff_elec = {}
hist_elec = {}
eff_elec, hist_elec = get_eff(
eff_elec,
hist_elec,
tf,
histoName_e,
label,
histo,
)
name = "efficiency_" + histo
canvas = TCanvas(name, canvastitle)
canvas.SetRightMargin(0.1)
mg = TMultiGraph()
for i, lab in enumerate(label):
if not plot_electrons_only:
mg.Add(eff[lab])
set_style(eff[lab], colors[i], markers[i], styles[i])
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
mg.Add(eff_elec[lab])
set_style(eff_elec[lab], kBlue - 7, markers[i + 1], styles[i])
mg.Draw("AP")
mg.GetYaxis().SetRangeUser(0, 1.05)
xtitle = efficiencyHistoDict[histo]["xTitle"]
unit_l = xtitle.split("[")
if "]" in unit_l[-1]:
unit = unit_l[-1].replace("]", "")
else:
unit = ""
print(unit)
mg.GetXaxis().SetTitle(xtitle)
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitle(
"Efficiency of Long Tracks",
) # (" + str(round(hist_den[label[0]].GetBinWidth(1), 2)) + f"{unit})"+"^{-1}")
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
mygray = 18
myblue = kBlue - 9
for i, lab in enumerate(label):
rightmax = 1.05 * hist_den[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
hist_den[lab].Scale(scale)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
rightmax = 1.05 * hist_elec[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
hist_elec[lab].Scale(scale)
if i == 0:
if not plot_electrons_only:
set_style(hist_den[lab], mygray, markers[i], styles[i])
gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
hist_den[lab].Draw("HIST PLC SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
set_style(hist_elec[lab], myblue, markers[i], styles[i])
hist_elec[lab].SetFillColorAlpha(myblue, 0.35)
hist_elec[lab].Draw("HIST PLC SAME")
else:
print(
"No distribution plotted for other labels.",
"Can be added by uncommenting the code below this print statement.",
)
# set_style(hist_den[lab], mygray, markers[i], styles[i])
# gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
# hist_den[lab].Draw("HIST PLC SAME")
if histo == "p":
pos = [0.53, 0.4, 1.01, 0.71]
elif histo == "pt":
pos = [0.5, 0.4, 0.98, 0.71]
else:
pos = [0.4, 0.37, 0.88, 0.68]
legend = place_legend(
canvas, *pos, header="LHCb Simulation", option="LPE"
)
for le in legend.GetListOfPrimitives():
if "distribution" in le.GetLabel():
le.SetOption("LF")
legend.SetTextFont(132)
legend.SetTextSize(0.045)
legend.Draw()
for lab in label:
if not plot_electrons_only:
eff[lab].Draw("P SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
eff_elec[lab].Draw("P SAME")
cutName = categories[tracker][cut]["title"]
latex.DrawLatex(legend.GetX1() + 0.01, legend.GetY1() - 0.05, cutName)
low = 0
high = 1.05
gPad.Update()
axis = TGaxis(
gPad.GetUxmax(),
gPad.GetUymin(),
gPad.GetUxmax(),
gPad.GetUymax(),
low,
high,
510,
"+U",
)
axis.SetTitleFont(132)
axis.SetTitleSize(0.06)
axis.SetTitleOffset(0.55)
axis.SetTitle(
"# Tracks " + get_nicer_var_string(histo) + " distribution [a.u.]",
)
axis.SetLabelSize(0)
axis.Draw()
canvas.RedrawAxis()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
if not plot_electrons_only:
canvasName = tracker + "_" + cut + "_" + histo + "." + ftype
else:
canvasName = (
tracker + "Electrons_" + cut + "_" + histo + "." + ftype
)
canvas.SaveAs("checks/" + canvasName)
# canvas.SetRightMargin(0.05)
canvas.Write()
# calculate ghost rate
histoBaseName = "Track/" + folder + tracker + "/"
for histo in ghostHistos[tracker]:
trackerDir.cd()
title = "ghost_rate_vs_" + histo
gPad.SetTicks()
histoName = histoBaseName + ghostHistoDict[histo]["variable"]
ghost = {}
hist_den = {}
ghost = get_ghost(ghost, hist_den, tf, histoName, label)
canvas = TCanvas(title, title)
mg = TMultiGraph()
for i, lab in enumerate(label):
mg.Add(ghost[lab])
set_style(ghost[lab], colors[i], markers[i], styles[i])
xtitle = ghostHistoDict[histo]["xTitle"]
mg.GetXaxis().SetTitle(xtitle)
mg.GetYaxis().SetTitle("Fraction of fake tracks")
mg.Draw("ap")
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
# for lab in label:
# ghost[lab].Draw("P SAME")
if histo == "p":
pos = [0.53, 0.4, 1.01, 0.71]
elif histo == "pt":
pos = [0.5, 0.4, 0.98, 0.71]
elif histo == "eta":
pos = [0.35, 0.6, 0.85, 0.9]
else:
pos = [0.4, 0.37, 0.88, 0.68]
legend = place_legend(canvas, *pos, header="LHCb Simulation", option="LPE")
legend.SetTextFont(132)
legend.SetTextSize(0.045)
legend.Draw()
# if histo != "nPV":
# latex.DrawLatex(0.7, 0.85, "LHCb simulation")
# else:
# latex.DrawLatex(0.2, 0.85, "LHCb simulation")
# mg.GetYaxis().SetRangeUser(0, 0.4)
if histo == "eta":
mg.GetYaxis().SetRangeUser(0, 0.4)
# track_name = names[tracker] + " tracks"
# latex.DrawLatex(0.7, 0.75, track_name)
# canvas.PlaceLegend()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
canvas.SaveAs(
"checks/" + tracker + "ghost_rate_" + histo + "." + ftype,
)
canvas.Write()
outputfile.Write()
outputfile.Close()
if __name__ == "__main__":
parser = argument_parser()
args = parser.parse_args()
PrCheckerEfficiency(**vars(args))

309
scripts/BakPrCheckerTrackResolution.py Normal file
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import argparse
from ROOT import TLatex, TCanvas, TFile, TGaxis
from ROOT import kOrange, kGray, kMagenta, kCyan, kGreen, kBlue, kBlack, gPad, TF1
from ROOT import gROOT
from ROOT import TObjArray
gROOT.SetBatch(True)
def get_colors():
return [kBlack, kBlue, kGreen + 3, kMagenta + 2, kOrange, kCyan + 2]
def get_markers():
return [20, 24, 21, 22, 23, 25]
def get_fillstyles():
return [3004, 3003, 3325, 3144, 3244, 3444]
def get_files(tf, filename, label):
for i, f in enumerate(filename):
tf[label[i]] = TFile(f, "read")
return tf
def argument_parser():
parser = argparse.ArgumentParser(description="location of the histogram file")
parser.add_argument("--filename", nargs="+", default=[], help="name of input files")
parser.add_argument(
"--label",
nargs="+",
default=["TrackRes"],
help="name of input tuple files",
)
parser.add_argument(
"--trackers",
type=str,
nargs="+",
default=["Forward", "BestLong", "BestForward"],
help="Trackers to plot.",
)
parser.add_argument(
"--outfile",
type=str,
default="checks/TrackResolution_plots.root",
help="name of output files",
)
parser.add_argument(
"--savepdf",
default=False,
action="store_true",
help="save plots in pdf format",
)
return parser
def PrCheckerTrackResolution(filename, label, trackers, outfile, savepdf):
from utils.LHCbStyle import setLHCbStyle, set_style
from utils.Legend import place_legend
setLHCbStyle()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.05)
markers = get_markers()
colors = get_colors()
styles = get_fillstyles()
tf = {}
tf = get_files(tf, filename, label)
outputfile = TFile(outfile, "recreate")
outputfile.cd()
for tracker in trackers:
hres_p = {}
hres_eta = {}
canvas1 = TCanvas("res_p", "res v.s. p")
canvas1.cd()
arrp = TObjArray()
gaus = TF1("gaus", "gaus", -1, 1)
for idx, lab in enumerate(label):
hdp_p = tf[lab].Get(
f"Track/TrackResChecker{tracker}/ALL/vertex/dpoverp_vs_p",
)
hdp_p.SetName("dpoverp_p_" + lab)
hmom = hdp_p.ProjectionX()
hmom.SetTitle("p distribution Long Tracks")
hdp_p.FitSlicesY(gaus, 0, -1, 0, "Q", arrp)
hres_p[lab] = arrp[2]
hres_p[lab].GetYaxis().SetTitle("dp/p [%]")
hres_p[lab].GetXaxis().SetTitle("p [GeV]")
hres_p[lab].GetYaxis().SetRangeUser(0, 1.2)
hres_p[lab].SetTitle(lab)
set_style(hres_p[lab], colors[idx], markers[idx], 0)
if idx == 0:
hres_p[lab].Draw("E1 p1")
set_style(hmom, kGray + 1, markers[idx], styles[idx])
hmom.Scale(gPad.GetUymax() / hmom.GetMaximum())
hmom.Draw("hist same")
else:
hres_p[lab].Draw("E1 p1 same")
set_style(hmom, colors[idx] - 10, markers[idx], styles[idx])
# hmom.Scale(gPad.GetUymax() / hmom.GetMaximum())
# hmom.Draw("hist same")
for i in range(1, hres_p[lab].GetNbinsX() + 1):
hres_p[lab].SetBinContent(i, hres_p[lab].GetBinContent(i) * 100)
hres_p[lab].SetBinError(i, hres_p[lab].GetBinError(i) * 100)
print(
lab
+ ": Track resolution (dp/p) in p region: ("
+ format(hres_p[lab].GetBinLowEdge(i), ".2f")
+ ", "
+ format(
hres_p[lab].GetBinLowEdge(i) + hres_p[lab].GetBinWidth(i),
".2f",
)
+ ") [GeV/c]"
+ " --- ("
+ format(hres_p[lab].GetBinContent(i), ".2f")
+ "+-"
+ format(hres_p[lab].GetBinError(i), ".2f")
+ ")%",
)
print("-----------------------------------------------------")
if "Best" not in tracker:
legend = place_legend(
canvas1,
0.45,
0.33,
0.93,
0.63,
header="LHCb Simulation",
option="LPE",
)
else:
legend = place_legend(
canvas1,
0.45,
0.53,
0.93,
0.83,
header="LHCb Simulation",
option="LPE",
)
for le in legend.GetListOfPrimitives():
if "distribution" in le.GetLabel():
le.SetOption("LF")
legend.Draw()
for lab in label:
hres_p[lab].Draw("E1 p1 same")
canvas1.SetRightMargin(0.1)
if "Best" not in tracker:
latex.DrawLatex(
legend.GetX1() + 0.01,
legend.GetY1() - 0.05,
"without Kalman Filter",
)
low = 0
high = 1.2
gPad.Update()
axis = TGaxis(
gPad.GetUxmax(),
gPad.GetUymin(),
gPad.GetUxmax(),
gPad.GetUymax(),
low,
high,
510,
"+U",
)
axis.SetTitleFont(132)
axis.SetTitleSize(0.06)
axis.SetTitleOffset(0.55)
axis.SetTitle("# Tracks p distribution [a.u.]")
axis.SetLabelSize(0)
axis.Draw()
canvas1.Write()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
canvas1.SaveAs(f"checks/{tracker}_trackres_p." + ftype)
gaus.Delete()
arrp.Delete()
canvas2 = TCanvas("res_eta", "res v.s. eta")
canvas2.cd()
arreta = TObjArray()
gaus = TF1("gaus", "gaus", -1, 1)
for idx, lab in enumerate(label):
hdp_eta = tf[lab].Get(
f"Track/TrackResChecker{tracker}/ALL/vertex/dpoverp_vs_eta",
)
hdp_eta.SetName("dpoverp_eta_" + lab)
hdp_eta.FitSlicesY(gaus, 0, -1, 0, "Q", arreta)
heta = hdp_eta.ProjectionX()
heta.SetTitle("#eta distribution Long Tracks")
hres_eta[lab] = arreta[2]
hres_eta[lab].GetYaxis().SetTitle("dp/p [%]")
hres_eta[lab].GetXaxis().SetTitle("#eta")
hres_eta[lab].GetYaxis().SetRangeUser(0, 1.2)
hres_eta[lab].SetTitle(lab)
set_style(hres_eta[lab], colors[idx], markers[idx], 0)
if idx == 0:
hres_eta[lab].Draw("E1 p1")
set_style(heta, kGray + 1, markers[idx], styles[idx])
heta.Scale(gPad.GetUymax() / heta.GetMaximum())
heta.Draw("hist same")
else:
hres_eta[lab].Draw("E1 p1 same")
set_style(heta, colors[idx] - 10, markers[idx], styles[idx])
# heta.Scale(gPad.GetUymax() / heta.GetMaximum())
# heta.Draw("hist same")
for i in range(1, hres_eta[lab].GetNbinsX() + 1):
hres_eta[lab].SetBinContent(i, hres_eta[lab].GetBinContent(i) * 100)
hres_eta[lab].SetBinError(i, hres_eta[lab].GetBinError(i) * 100)
print(
lab
+ ": Track resolution (dp/p) in eta region: ("
+ format(hres_eta[lab].GetBinLowEdge(i), ".2f")
+ ", "
+ format(
hres_eta[lab].GetBinLowEdge(i) + hres_eta[lab].GetBinWidth(i),
".2f",
)
+ ")"
+ " --- ("
+ format(hres_eta[lab].GetBinContent(i), ".2f")
+ "+-"
+ format(hres_eta[lab].GetBinError(i), ".2f")
+ ")%",
)
print("-----------------------------------------------------")
legend = place_legend(
canvas2,
0.41,
0.27,
0.89,
0.57,
header="LHCb Simulation",
option="LPE",
)
for le in legend.GetListOfPrimitives():
if "distribution" in le.GetLabel():
le.SetOption("LF")
legend.SetTextFont(132)
legend.SetTextSize(0.045)
legend.Draw()
for lab in label:
hres_eta[lab].Draw("E1 p1 same")
canvas2.SetRightMargin(0.1)
if "Best" not in tracker:
latex.DrawLatex(
legend.GetX1() + 0.01,
legend.GetY1() - 0.05,
"without Kalman Filter",
)
low = 0
high = 1.2
gPad.Update()
axis = TGaxis(
gPad.GetUxmax(),
gPad.GetUymin(),
gPad.GetUxmax(),
gPad.GetUymax(),
low,
high,
510,
"+U",
)
axis.SetTitleFont(132)
axis.SetTitleSize(0.06)
axis.SetTitleOffset(0.55)
axis.SetTitle("# Tracks #eta distribution [a.u.]")
axis.SetLabelSize(0)
axis.Draw()
canvas2.Write()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
canvas2.SaveAs(f"checks/{tracker}_trackres_eta." + ftype)
gaus.Delete()
arreta.Delete()
outputfile.Write()
outputfile.Close()
if __name__ == "__main__":
parser = argument_parser()
args = parser.parse_args()
PrCheckerTrackResolution(**vars(args))

839
scripts/CompareEfficiency.py Normal file
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# flake8: noqa
"""
Script for accessing histograms of reconstructible and
reconstructed tracks for different tracking categories
and trackers.
The efficency is calculated usig TGraphAsymmErrors
and Bayesian error bars
author: Furkan Cetin
date: 10/2023
Takes data from Recent_get_resolution_and_eff_data.py and calculates efficiencies
python scripts/CompareEfficiency.py
--filename data/res_and_effs_B.root data/resolutions_and_effs_Bd2KstEE_MDmaster.root
--trackers Match --label new old --outfile data/compare_effs.root
python scripts/CompareEfficiency.py --filename data/resolutions_and_effs_D_default_weights.root data/resolutions_and_effs_D_with_electron_weights_as_residual.root --trackers BestLong Seed --label default new --outfile data_results/CompareEfficiencyDDefaultResidual.root
"""
import os, sys
import argparse
from ROOT import TMultiGraph, TLatex, TCanvas, TFile, TGaxis
from ROOT import (
kGreen,
kBlue,
kBlack,
kAzure,
kGray,
kOrange,
kMagenta,
kCyan,
kViolet,
kTeal,
kRed,
)
from ROOT import gROOT, gStyle, gPad
from ROOT import TEfficiency
from array import array
gROOT.SetBatch(True)
from utils.components import unique_name_ext_re, findRootObjByName
def getEfficiencyHistoNames():
return ["p", "pt", "phi", "eta", "nPV"]
def getTrackers(trackers):
return trackers
def getCompCuts(compare_cuts):
return compare_cuts
# data/resolutions_and_effs_Bd2KstEE_MDmaster.root:Track/...
def getOriginFolders():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
# evtl anpassen wenn die folders anders heissen
basedict["Velo"]["folder"] = "VeloTrackChecker/"
basedict["Upstream"]["folder"] = "UpstreamTrackChecker/"
basedict["Forward"]["folder"] = "ForwardTrackChecker" + unique_name_ext_re() + "/"
basedict["Match"]["folder"] = "MatchTrackChecker" + unique_name_ext_re() + "/"
basedict["BestLong"]["folder"] = "BestLongTrackChecker" + unique_name_ext_re() + "/"
basedict["Seed"]["folder"] = "SeedTrackChecker" + unique_name_ext_re() + "/"
# basedict["Forward"]["folder"] = "ForwardTrackChecker_7a0dbfa7/"
# basedict["Match"]["folder"] = "MatchTrackChecker_29e3152a/"
# basedict["BestLong"]["folder"] = "BestLongTrackChecker_4ddacce1/"
# basedict["Seed"]["folder"] = "SeedTrackChecker_1b1d5575/"
return basedict
def getTrackNames():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
basedict["Velo"] = "Velo"
basedict["Upstream"] = "VeloUT"
basedict["Forward"] = "Forward"
basedict["Match"] = "Match"
basedict["BestLong"] = "BestLong"
basedict["Seed"] = "Seed"
return basedict
def get_colors():
return [kBlack, kAzure, kGreen + 2, kMagenta + 2, kRed, kCyan + 2, kGray + 1]
def get_elec_colors():
return [
kGray + 2,
kBlue - 4,
kRed + 1,
kGreen + 1,
kViolet,
kOrange - 3,
kTeal - 1,
kGray + 1,
]
def get_markers():
return [20, 21, 24, 25, 22, 23, 26, 32]
def get_fillstyles():
return [1003, 3001, 3002, 3325, 3144, 3244, 3444]
def getGhostHistoNames():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
basedict["Velo"] = ["eta", "nPV"]
basedict["Upstream"] = ["eta", "p", "pt", "nPV"]
basedict["Forward"] = ["eta", "p", "pt", "nPV"]
basedict["Match"] = ["eta", "p", "pt", "nPV"]
basedict["BestLong"] = ["eta", "p", "pt", "nPV"]
basedict["Seed"] = ["eta", "p", "pt", "nPV"]
return basedict
def argument_parser():
parser = argparse.ArgumentParser(description="location of the tuple file")
parser.add_argument(
"--filename",
type=str,
default=["data/resolutions_and_effs_B.root"],
nargs="+",
help="input files, including path",
)
parser.add_argument(
"--outfile",
type=str,
default="data_results/compare_efficiency.root",
help="output file",
)
parser.add_argument(
"--trackers",
type=str,
nargs="+",
default=["Forward", "Match", "BestLong", "Seed"], # ---
help="Trackers to plot.",
)
parser.add_argument(
"--label",
nargs="+",
default=["Eff"],
help="label for files",
)
parser.add_argument(
"--savepdf",
action="store_true",
help="save plots in pdf format",
)
parser.add_argument(
"--compare",
default=True,
action="store_true",
help="compare efficiencies",
)
parser.add_argument(
"--compare-cuts",
type=str,
nargs="+",
default=["long", "long_fromB", "long_fromB_P>5GeV"],
help="which cuts get compared",
)
parser.add_argument(
"--plot-electrons",
default=True,
action="store_true",
help="plot electrons",
)
parser.add_argument(
"--plot-electrons-only",
action="store_true",
help="plot only electrons",
)
return parser
def get_files(tf, filename, label):
for i, f in enumerate(filename):
tf[label[i]] = TFile(f, "read")
return tf
def get_nicer_var_string(var: str):
nice_vars = dict(pt="p_{T}", eta="#eta", phi="#phi")
try:
return nice_vars[var]
except KeyError:
return var
def get_eff(eff, hist, tf, histoName, label, var):
eff = {}
hist = {}
var = get_nicer_var_string(var)
for i, lab in enumerate(label):
numeratorName = histoName + "_reconstructed"
numerator = findRootObjByName(tf[lab], numeratorName)
denominatorName = histoName + "_reconstructible"
denominator = findRootObjByName(tf[lab], denominatorName)
if numerator.GetEntries() == 0 or denominator.GetEntries() == 0:
continue
teff = TEfficiency(numerator, denominator)
teff.SetStatisticOption(7)
eff[lab] = teff.CreateGraph()
eff[lab].SetName(lab)
eff[lab].SetTitle(lab)
if histoName.find("Forward") != -1:
if histoName.find("electron") != -1:
eff[lab].SetTitle(lab + " Forward, e^{-}")
else:
eff[lab].SetTitle(lab + " Forward")
if histoName.find("Match") != -1:
if histoName.find("electron") != -1:
eff[lab].SetTitle(lab + " Match, e^{-}")
else:
eff[lab].SetTitle(lab + " Match")
if histoName.find("Seed") != -1:
if histoName.find("electron") != -1:
eff[lab].SetTitle(lab + " Seed, e^{-}")
else:
eff[lab].SetTitle(lab + " Seed")
if histoName.find("BestLong") != -1:
if histoName.find("electron") != -1:
eff[lab].SetTitle(lab + " BestLong, e^{-}")
else:
eff[lab].SetTitle(lab + " BestLong")
# eff[lab].SetTitle(lab + " not e^{-}")
# if histoName.find("strange") != -1:
# eff[lab].SetTitle(lab + " from stranges")
# if histoName.find("electron") != -1:
# eff[lab].SetTitle(lab + " e^{-}")
hist[lab] = denominator.Clone()
hist[lab].SetName("h_numerator_notElectrons")
hist[lab].SetTitle(var + " distribution, not e^{-}")
if histoName.find("strange") != -1:
hist[lab].SetTitle(var + " distribution, stranges")
if histoName.find("electron") != -1:
hist[lab].SetTitle(var + " distribution, e^{-}")
return eff, hist
def get_ghost(eff, hist, tf, histoName, label):
ghost = {}
for i, lab in enumerate(label):
numeratorName = histoName + "_Ghosts"
denominatorName = histoName + "_Total"
numerator = findRootObjByName(tf[lab], numeratorName)
denominator = findRootObjByName(tf[lab], denominatorName)
print("Numerator = " + numeratorName.replace(unique_name_ext_re(), ""))
print("Denominator = " + denominatorName.replace(unique_name_ext_re(), ""))
teff = TEfficiency(numerator, denominator)
teff.SetStatisticOption(7)
ghost[lab] = teff.CreateGraph()
print(lab)
ghost[lab].SetName(lab)
return ghost
def PrCheckerEfficiency(
filename,
outfile,
label,
trackers,
savepdf,
compare,
compare_cuts,
plot_electrons,
plot_electrons_only,
):
from utils.LHCbStyle import setLHCbStyle, set_style
from utils.ConfigHistos import (
efficiencyHistoDict,
ghostHistoDict,
categoriesDict,
getCuts,
)
from utils.CompareConfigHistos import getCompare, getCompColors
from utils.Legend import place_legend
setLHCbStyle()
markers = get_markers()
colors = get_colors()
elec_colors = get_elec_colors()
styles = get_fillstyles()
tf = {}
tf = get_files(tf, filename, label)
outputfile = TFile(outfile, "recreate")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.05)
efficiencyHistoDict = efficiencyHistoDict()
efficiencyHistos = getEfficiencyHistoNames()
ghostHistos = getGhostHistoNames()
ghostHistoDict = ghostHistoDict()
categories = categoriesDict()
cuts = getCuts()
compareDict = getCompare()
compareCuts = getCompCuts(compare_cuts)
compareColors = getCompColors()
trackers = getTrackers(trackers)
folders = getOriginFolders()
for tracker in trackers:
outputfile.cd()
trackerDir = outputfile.mkdir(tracker)
trackerDir.cd()
for cut in cuts[tracker]:
cutDir = trackerDir.mkdir(cut)
cutDir.cd()
folder = folders[tracker]["folder"]
print("folder: " + folder.replace(unique_name_ext_re(), ""))
histoBaseName = "Track/" + folder + tracker + "/" + cut + "_"
# calculate efficiency
for histo in efficiencyHistos:
canvastitle = (
"efficiency_" + histo + ", " + categories[tracker][cut]["title"]
)
# get efficiency for not electrons category
histoName = histoBaseName + "" + efficiencyHistoDict[histo]["variable"]
print("not electrons: " + histoName.replace(unique_name_ext_re(), ""))
eff = {}
hist_den = {}
eff, hist_den = get_eff(eff, hist_den, tf, histoName, label, histo)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
histoNameElec = (
"Track/"
+ folder
+ tracker
+ "/"
+ categories[tracker][cut]["Electrons"]
)
histoName_e = (
histoNameElec + "_" + efficiencyHistoDict[histo]["variable"]
)
print("electrons: " + histoName_e.replace(unique_name_ext_re(), ""))
eff_elec = {}
hist_elec = {}
eff_elec, hist_elec = get_eff(
eff_elec,
hist_elec,
tf,
histoName_e,
label,
histo,
)
name = "efficiency_" + histo
canvas = TCanvas(name, canvastitle)
canvas.SetRightMargin(0.1)
mg = TMultiGraph()
for i, lab in enumerate(label):
if not plot_electrons_only:
mg.Add(eff[lab])
set_style(eff[lab], colors[i], markers[i], styles[i])
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
mg.Add(eff_elec[lab])
set_style(eff_elec[lab], elec_colors[i], markers[i], styles[i])
mg.Draw("AP")
mg.GetYaxis().SetRangeUser(0, 1.05)
xtitle = efficiencyHistoDict[histo]["xTitle"]
unit_l = xtitle.split("[")
if "]" in unit_l[-1]:
unit = unit_l[-1].replace("]", "")
else:
unit = "a.u."
print(unit)
mg.GetXaxis().SetTitle(xtitle)
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitle(
"Efficiency of Long Tracks",
) # (" + str(round(hist_den[label[0]].GetBinWidth(1), 2)) + f"{unit})"+"^{-1}")
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
mygray = 18
myblue = kBlue - 9
for i, lab in enumerate(label):
rightmax = 1.05 * hist_den[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
hist_den[lab].Scale(scale)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
rightmax = 1.05 * hist_elec[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
hist_elec[lab].Scale(scale)
if i == 0:
if not plot_electrons_only:
set_style(hist_den[lab], mygray, markers[i], styles[i])
gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
hist_den[lab].Draw("HIST PLC SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
set_style(hist_elec[lab], myblue, markers[i], styles[i])
hist_elec[lab].SetFillColorAlpha(myblue, 0.35)
hist_elec[lab].Draw("HIST PLC SAME")
# else:
# print(
# "No distribution plotted for other labels.",
# "Can be added by uncommenting the code below this print statement.",
# )
# # set_style(hist_den[lab], mygray, markers[i], styles[i])
# # gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
# # hist_den[lab].Draw("HIST PLC SAME")
# if histo == "p":
# pos = [0.5, 0.3, 1.0, 0.6]
# elif histo == "pt":
# pos = [0.5, 0.3, 0.99, 0.6]
# elif histo == "phi":
# pos = [0.3, 0.25, 0.8, 0.55]
# else:
# pos = [0.3, 0.25, 0.8, 0.55]
if histo == "p":
pos = [0.5, 0.3, 1.0, 0.5] # [0.53, 0.4, 1.01, 0.71]
elif histo == "pt":
pos = [0.5, 0.3, 0.99, 0.5] # [0.5, 0.4, 0.98, 0.71]
elif histo == "phi":
pos = [0.4, 0.3, 0.9, 0.5]
elif histo == "eta":
pos = [0.5, 0.25, 1.0, 0.45]
else:
pos = [0.35, 0.25, 0.85, 0.45]
legend = place_legend(
canvas, *pos, header="LHCb Simulation", option="LPE"
)
for le in legend.GetListOfPrimitives():
if "distribution" in le.GetLabel():
le.SetOption("LF")
legend.SetTextFont(132)
legend.SetTextSize(0.04)
legend.Draw()
for lab in label:
if not plot_electrons_only:
eff[lab].Draw("P SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
eff_elec[lab].Draw("P SAME")
cutName = categories[tracker][cut]["title"]
latex.DrawLatex(legend.GetX1() + 0.01, legend.GetY1() - 0.05, cutName)
low = 0
high = 1.05
gPad.Update()
axis = TGaxis(
gPad.GetUxmax(),
gPad.GetUymin(),
gPad.GetUxmax(),
gPad.GetUymax(),
low,
high,
510,
"+U",
)
axis.SetTitleFont(132)
axis.SetTitleSize(0.06)
axis.SetTitleOffset(0.55)
axis.SetTitle(
"# Tracks " + get_nicer_var_string(histo) + " distribution [a.u.]",
)
axis.SetLabelSize(0)
axis.Draw()
canvas.RedrawAxis()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
if not plot_electrons_only:
canvasName = tracker + "_" + cut + "_" + histo + "." + ftype
else:
canvasName = (
tracker
+ "_Electrons_"
+ cut
+ "_"
+ histo
+ "."
+ ftype
)
canvas.SaveAs("checks/" + canvasName)
# canvas.SetRightMargin(0.05)
canvas.Write()
# calculate ghost rate
print("\ncalculate ghost rate: ")
histoBaseName = "Track/" + folder + tracker + "/"
for histo in ghostHistos[tracker]:
trackerDir.cd()
title = "ghost_rate_vs_" + histo
gPad.SetTicks()
histoName = histoBaseName + ghostHistoDict[histo]["variable"]
ghost = {}
hist_den = {}
ghost = get_ghost(ghost, hist_den, tf, histoName, label)
canvas = TCanvas(title, title)
mg = TMultiGraph()
for i, lab in enumerate(label):
mg.Add(ghost[lab])
set_style(ghost[lab], colors[i], markers[2 * i], styles[i])
xtitle = ghostHistoDict[histo]["xTitle"]
mg.GetXaxis().SetTitle(xtitle)
mg.GetYaxis().SetTitle("Fraction of fake tracks")
mg.Draw("ap")
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
# for lab in label:
# ghost[lab].Draw("P SAME")
if histo == "p":
pos = [0.53, 0.4, 1.00, 0.71]
elif histo == "pt":
pos = [0.5, 0.4, 0.98, 0.71]
elif histo == "eta":
pos = [0.35, 0.6, 0.85, 0.9]
elif histo == "phi":
pos = [0.3, 0.3, 0.9, 0.6]
else:
pos = [0.4, 0.37, 0.80, 0.68]
legend = place_legend(canvas, *pos, header="LHCb Simulation", option="LPE")
legend.SetTextFont(132)
legend.SetTextSize(0.04)
legend.Draw()
# if histo != "nPV":
# latex.DrawLatex(0.7, 0.85, "LHCb simulation")
# else:
# latex.DrawLatex(0.2, 0.85, "LHCb simulation")
# mg.GetYaxis().SetRangeUser(0, 0.4)
if histo == "eta":
mg.GetYaxis().SetRangeUser(0, 0.4)
# track_name = names[tracker] + " tracks"
# latex.DrawLatex(0.7, 0.75, track_name)
# canvas.PlaceLegend()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
canvas.SaveAs(
"checks/" + tracker + "_ghost_rate_" + histo + "." + ftype,
)
canvas.Write()
#
# Compare electron efficiencies of different trackers
#
plot_electrons_only = True
if compare:
print("\nCompare Efficiencies: ")
outputfile.cd()
for jcut in compareCuts: # [long, long_fromB, long_fromB_P>5GeV]
compareDir = outputfile.mkdir("compare_" + jcut)
compareDir.cd()
for histo in efficiencyHistos: # [p, pt, phi, eta, nPV]
canvastitle = "efficiency_" + histo + "_" + jcut
name = "efficiency_" + histo + "_" + jcut
canvas = TCanvas(name, canvastitle)
canvas.SetRightMargin(0.1)
mg = TMultiGraph()
dist_eff = {}
dist_hist_den = {}
dist_eff_elec = {}
dist_hist_elec = {}
First = True
dist_tracker = ""
markeritr = 0
for tracker in trackers: # [BestLong, Forward, Match, Seed]
cut = compareDict[jcut][tracker]
folder = folders[tracker]["folder"]
print("folder: " + folder.replace(unique_name_ext_re(), ""))
jcolor = compareColors[tracker]
histoName = (
"Track/"
+ folder
+ tracker
+ "/"
+ cut
+ "_"
+ ""
+ efficiencyHistoDict[histo]["variable"]
)
print(
"not electrons: " + histoName.replace(unique_name_ext_re(), "")
)
eff = {}
hist_den = {}
eff, hist_den = get_eff(eff, hist_den, tf, histoName, label, histo)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
histoNameElec = (
"Track/"
+ folder
+ tracker
+ "/"
+ categories[tracker][cut]["Electrons"]
)
histoName_e = (
histoNameElec + "_" + efficiencyHistoDict[histo]["variable"]
)
print(
"electrons: "
+ histoName_e.replace(unique_name_ext_re(), "")
)
eff_elec = {}
hist_elec = {}
eff_elec, hist_elec = get_eff(
eff_elec,
hist_elec,
tf,
histoName_e,
label,
histo,
)
if First:
dist_eff_elec = eff_elec
dist_hist_elec = hist_elec
if First:
dist_tracker = tracker
dist_eff = eff
dist_hist_den = hist_den
First = False
seeditr = 0
for i, lab in enumerate(label):
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
if (tracker == "Seed") and (seeditr != 0):
continue
if tracker == "Seed":
seeditr += 1
mg.Add(eff_elec[lab])
set_style(
eff_elec[lab],
colors[jcolor],
markers[i + markeritr],
styles[i],
)
else:
mg.Add(eff_elec[lab])
set_style(
eff_elec[lab],
elec_colors[jcolor + markeritr],
markers[i + markeritr],
styles[i],
)
markeritr = markeritr + 1
# set_style(
# eff_elec[lab], colors[jcolor], markers[i], styles[i]
# )
markeritr = 0
mg.Draw("AP")
mg.GetYaxis().SetRangeUser(0, 1.05)
xtitle = efficiencyHistoDict[histo]["xTitle"]
unit_l = xtitle.split("[")
if "]" in unit_l[-1]:
unit = unit_l[-1].replace("]", "")
else:
unit = "a.u."
print(unit)
mg.GetXaxis().SetTitle(xtitle)
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitle(
"Efficiency of Long Tracks",
) # (" + str(round(hist_den[label[0]].GetBinWidth(1), 2)) + f"{unit})"+"^{-1}")
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
mygray = 16
myblue = kBlue - 7
dist_cut = compareDict[jcut][dist_tracker]
for i, lab in enumerate(label):
rightmax = 1.05 * dist_hist_den[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
dist_hist_den[lab].Scale(scale)
if (
categories[dist_tracker][dist_cut]["plotElectrons"]
and plot_electrons
):
rightmax = 1.05 * dist_hist_elec[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
dist_hist_elec[lab].Scale(scale)
if i == len(label) - 1:
if not plot_electrons_only:
set_style(dist_hist_den[lab], mygray, markers[i], styles[i])
# gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
dist_hist_den[lab].SetFillColorAlpha(mygray, 0.5)
dist_hist_den[lab].Draw("HIST PLC SAME")
if (
categories[dist_tracker][dist_cut]["plotElectrons"]
and plot_electrons
):
set_style(
dist_hist_elec[lab], mygray, markers[i], styles[i]
)
# gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
# dist_hist_elec[lab].SetFillColor(myblue)
dist_hist_elec[lab].SetFillColorAlpha(myblue, 0.5)
dist_hist_elec[lab].Draw("HIST PLC SAME")
# else:
# print(
# "No distribution plotted for other labels.",
# "Can be added by uncommenting the code below this print statement.",
# )
# set_style(dist_hist_den[lab], mygray, markers[i], styles[i])
# gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
# dist_hist_den[lab].Draw("HIST PLC SAME")
if histo == "p":
pos = [0.5, 0.3, 1.0, 0.5] # [0.53, 0.4, 1.01, 0.71]
elif histo == "pt":
pos = [0.5, 0.3, 0.99, 0.5] # [0.5, 0.4, 0.98, 0.71]
elif histo == "phi":
pos = [0.4, 0.3, 0.9, 0.5]
elif histo == "eta":
pos = [0.5, 0.25, 1.0, 0.45]
else:
pos = [0.35, 0.25, 0.85, 0.45]
legend = place_legend(
canvas, *pos, header="LHCb Simulation", option="LPE"
)
for le in legend.GetListOfPrimitives():
if "distribution" in le.GetLabel():
le.SetOption("LF")
legend.SetTextFont(132)
legend.SetTextSize(0.04)
legend.Draw()
for lab in label:
if not plot_electrons_only:
dist_eff[lab].Draw("P SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
dist_eff_elec[lab].Draw("P SAME")
cutName = categories[tracker][cut]["title"]
latex.DrawLatex(legend.GetX1() + 0.01, legend.GetY1() - 0.05, cutName)
low = 0
high = 1.05
gPad.Update()
axis = TGaxis(
gPad.GetUxmax(),
gPad.GetUymin(),
gPad.GetUxmax(),
gPad.GetUymax(),
low,
high,
510,
"+U",
)
axis.SetTitleFont(132)
axis.SetTitleSize(0.06)
axis.SetTitleOffset(0.55)
axis.SetTitle(
"# Tracks " + get_nicer_var_string(histo) + " distribution [a.u.]",
)
axis.SetLabelSize(0)
axis.Draw()
canvas.RedrawAxis()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
if not plot_electrons_only:
canvasName = "Compare_" + cut + "_" + histo + "." + ftype
else:
canvasName = (
"Compare_Electrons_" + cut + "_" + histo + "." + ftype
)
canvas.SaveAs("checks/" + canvasName)
# canvas.SetRightMargin(0.05)
canvas.Write()
# # calculate ghost rate
# for histo in ghostHistos: # [p, pt, eta, nPV]
# canvastitle = "ghost_rate_vs_" + histo + "_" + jcut
# name = "ghost_rate_vs_" + histo + "_" + jcut
# canvas = TCanvas(name, canvastitle)
# canvas.SetRightMargin(0.1)
# mg = TMultiGraph()
outputfile.cd()
outputfile.Write()
outputfile.Close()
if __name__ == "__main__":
parser = argument_parser()
args = parser.parse_args()
PrCheckerEfficiency(**vars(args))

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scripts/CompareResidualEfficiency.py Normal file
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@ -0,0 +1,787 @@
# Script for accessing histograms of reconstructible and
# reconstructed tracks for different tracking categories
# created by hlt1_reco_baseline_with_mcchecking
#
# The efficency is calculated usig TGraphAsymmErrors
# and Bayesian error bars
#
# author: Furkan Cetin
# date: 10/2023
#
# flake8: noqa
#
# Takes data from Recent_get_resolution_and_eff_data.py and calculates efficiencies
# python scripts/CompareResidualEfficiency.py --filename data/resolutions_and_effs_B_residual.root data/resolutions_and_effs_B.root
# --trackers BestLong --label Residual Normal --outfile data/compare_effs_B_residual.root
#
import os, sys
import argparse
from ROOT import TMultiGraph, TLatex, TCanvas, TFile, TGaxis
from ROOT import kGreen, kBlue, kBlack, kAzure, kGray, kOrange, kMagenta, kCyan
from ROOT import gROOT, gStyle, gPad
from ROOT import TEfficiency
from array import array
gROOT.SetBatch(True)
from utils.components import unique_name_ext_re, findRootObjByName
def getEfficiencyHistoNames():
return ["p", "pt", "phi", "eta", "nPV"]
def getTrackers(trackers):
return trackers
def getCompCuts(compare_cuts):
return compare_cuts
# data/resolutions_and_effs_Bd2KstEE_MDmaster.root:Track/...
def getOriginFolders():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
# evtl anpassen wenn die folders anders heissen
basedict["Velo"]["folder"] = "VeloTrackChecker/"
basedict["Upstream"]["folder"] = "UpstreamTrackChecker/"
basedict["Forward"]["folder"] = "ForwardTrackChecker" + unique_name_ext_re() + "/"
basedict["Match"]["folder"] = "MatchTrackChecker" + unique_name_ext_re() + "/"
basedict["BestLong"]["folder"] = "BestLongTrackChecker" + unique_name_ext_re() + "/"
basedict["Seed"]["folder"] = "SeedTrackChecker" + unique_name_ext_re() + "/"
return basedict
def getTrackNames():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
basedict["Velo"] = "Velo"
basedict["Upstream"] = "VeloUT"
basedict["Forward"] = "Forward"
basedict["Match"] = "Match"
basedict["BestLong"] = "BestLong"
basedict["Seed"] = "Seed"
return basedict
def get_colors():
return [
kBlack,
kAzure,
kGreen + 2,
kMagenta + 1,
kOrange,
kCyan + 2,
kBlack,
kAzure,
kGreen + 3,
kMagenta + 2,
kOrange,
kCyan + 2,
]
def get_markers():
return [20, 21, 24, 25, 22, 23, 26, 32]
def get_fillstyles():
return [1003, 3001, 3002, 3325, 3144, 3244, 3444]
def getGhostHistoNames():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
basedict["Velo"] = ["eta", "nPV"]
basedict["Upstream"] = ["eta", "p", "pt", "nPV"]
basedict["Forward"] = ["eta", "p", "pt", "nPV"]
basedict["Match"] = ["eta", "p", "pt", "nPV"]
basedict["BestLong"] = ["eta", "p", "pt", "nPV"]
basedict["Seed"] = ["eta", "p", "pt", "nPV"]
return basedict
def argument_parser():
parser = argparse.ArgumentParser(description="location of the tuple file")
parser.add_argument(
"--filename",
type=str,
default=["data/resolutions_and_effs_B.root"],
nargs="+",
help="input files, including path",
)
parser.add_argument(
"--outfile",
type=str,
default="data/compare_efficiency.root",
help="output file",
)
parser.add_argument(
"--trackers",
type=str,
nargs="+",
default=["Forward", "Match", "BestLong", "Seed"], # ---
help="Trackers to plot.",
)
parser.add_argument(
"--label",
nargs="+",
default=["Eff"],
help="label for files",
)
parser.add_argument(
"--savepdf",
action="store_true",
help="save plots in pdf format",
)
parser.add_argument(
"--compare",
default=True,
action="store_true",
help="compare efficiencies",
)
parser.add_argument(
"--compare-cuts",
type=str,
nargs="+",
default=["long", "long_fromB", "long_fromB_P>5GeV"],
help="which cuts get compared",
)
parser.add_argument(
"--plot-electrons",
default=True,
action="store_true",
help="plot electrons",
)
parser.add_argument(
"--plot-electrons-only",
action="store_true",
help="plot only electrons",
)
return parser
def get_files(tf, filename, label):
for i, f in enumerate(filename):
tf[label[i]] = TFile(f, "read")
return tf
def get_nicer_var_string(var: str):
nice_vars = dict(pt="p_{T}", eta="#eta", phi="#phi")
try:
return nice_vars[var]
except KeyError:
return var
def get_eff(eff, hist, tf, histoName, label, var):
eff = {}
hist = {}
var = get_nicer_var_string(var)
for i, lab in enumerate(label):
numeratorName = histoName + "_reconstructed"
numerator = findRootObjByName(tf[lab], numeratorName)
# numerator = tf[lab].Get(numeratorName)
denominatorName = histoName + "_reconstructible"
denominator = findRootObjByName(tf[lab], denominatorName)
# denominator = tf[lab].Get(denominatorName)
if numerator.GetEntries() == 0 or denominator.GetEntries() == 0:
continue
teff = TEfficiency(numerator, denominator)
teff.SetStatisticOption(7)
# print("TBetaAlpha: "+ str(teff.GetBetaAlpha()))
# print("TBetaBeta: "+ str(teff.GetBetaBeta()))
eff[lab] = teff.CreateGraph()
eff[lab].SetName(lab)
eff[lab].SetTitle(lab)
if histoName.find("Forward") != -1:
if histoName.find("electron") != -1:
eff[lab].SetTitle(lab + " Forward, e^{-}")
else:
eff[lab].SetTitle(lab + " Forward")
elif histoName.find("Match") != -1:
if histoName.find("electron") != -1:
eff[lab].SetTitle(lab + " Match, e^{-}")
else:
eff[lab].SetTitle(lab + " Match")
elif histoName.find("Seed") != -1:
if histoName.find("electron") != -1:
eff[lab].SetTitle(lab + " Seed, e^{-}")
else:
eff[lab].SetTitle(lab + " Seed")
elif histoName.find("BestLong") != -1:
if histoName.find("electron") != -1:
eff[lab].SetTitle(lab + " BestLong, e^{-}")
else:
eff[lab].SetTitle(lab + " BestLong")
# eff[lab].SetTitle(lab + " not e^{-}")
# if histoName.find("strange") != -1:
# eff[lab].SetTitle(lab + " from stranges")
# if histoName.find("electron") != -1:
# eff[lab].SetTitle(lab + " e^{-}")
hist[lab] = denominator.Clone()
hist[lab].SetName("h_numerator_notElectrons")
hist[lab].SetTitle(var + " distribution, not e^{-}")
if histoName.find("strange") != -1:
hist[lab].SetTitle(var + " distribution, stranges")
if histoName.find("electron") != -1:
hist[lab].SetTitle(var + " distribution, e^{-}")
return eff, hist
def get_ghost(eff, hist, tf, histoName, label):
ghost = {}
for i, lab in enumerate(label):
numeratorName = histoName + "_Ghosts"
denominatorName = histoName + "_Total"
numerator = findRootObjByName(tf[lab], numeratorName)
denominator = findRootObjByName(tf[lab], denominatorName)
# numerator = tf[lab].Get(numeratorName)
# denominator = tf[lab].Get(denominatorName)
print("Numerator = " + numeratorName)
print("Denominator = " + denominatorName)
teff = TEfficiency(numerator, denominator)
teff.SetStatisticOption(7)
ghost[lab] = teff.CreateGraph()
print(lab)
ghost[lab].SetName(lab)
return ghost
def PrCheckerEfficiency(
filename,
outfile,
label,
trackers,
savepdf,
compare,
compare_cuts,
plot_electrons,
plot_electrons_only,
):
from utils.LHCbStyle import setLHCbStyle, set_style
from utils.ConfigHistos import (
efficiencyHistoDict,
ghostHistoDict,
categoriesDict,
getCuts,
)
from utils.CompareConfigHistos import getCompare, getCompColors
from utils.Legend import place_legend
setLHCbStyle()
markers = get_markers()
colors = get_colors()
styles = get_fillstyles()
tf = {}
tf = get_files(tf, filename, label)
outputfile = TFile(outfile, "recreate")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.05)
efficiencyHistoDict = efficiencyHistoDict()
efficiencyHistos = getEfficiencyHistoNames()
ghostHistos = getGhostHistoNames()
ghostHistoDict = ghostHistoDict()
categories = categoriesDict()
cuts = getCuts()
compareDict = getCompare()
compareCuts = getCompCuts(compare_cuts)
compareColors = getCompColors()
trackers = getTrackers(trackers)
folders = getOriginFolders()
for tracker in trackers:
outputfile.cd()
trackerDir = outputfile.mkdir(tracker)
trackerDir.cd()
for cut in cuts[tracker]:
cutDir = trackerDir.mkdir(cut)
cutDir.cd()
folder = folders[tracker]["folder"]
print(folder)
histoBaseName = "Track/" + folder + tracker + "/" + cut + "_"
# calculate efficiency
for histo in efficiencyHistos:
canvastitle = (
"efficiency_" + histo + ", " + categories[tracker][cut]["title"]
)
# get efficiency for not electrons category
histoName = histoBaseName + "" + efficiencyHistoDict[histo]["variable"]
print("not electrons: " + histoName)
eff = {}
hist_den = {}
eff, hist_den = get_eff(eff, hist_den, tf, histoName, label, histo)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
histoNameElec = (
"Track/"
+ folder
+ tracker
+ "/"
+ categories[tracker][cut]["Electrons"]
)
histoName_e = (
histoNameElec + "_" + efficiencyHistoDict[histo]["variable"]
)
print("electrons: " + histoName_e)
eff_elec = {}
hist_elec = {}
eff_elec, hist_elec = get_eff(
eff_elec,
hist_elec,
tf,
histoName_e,
label,
histo,
)
name = "efficiency_" + histo
canvas = TCanvas(name, canvastitle)
canvas.SetRightMargin(0.1)
mg = TMultiGraph()
for i, lab in enumerate(label):
if not plot_electrons_only:
mg.Add(eff[lab])
set_style(eff[lab], colors[i], markers[i], styles[i])
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
mg.Add(eff_elec[lab])
set_style(eff_elec[lab], colors[i], markers[i + 2], styles[i])
mg.Draw("AP")
mg.GetYaxis().SetRangeUser(0, 1.05)
xtitle = efficiencyHistoDict[histo]["xTitle"]
unit_l = xtitle.split("[")
if "]" in unit_l[-1]:
unit = unit_l[-1].replace("]", "")
else:
unit = ""
print(unit)
mg.GetXaxis().SetTitle(xtitle)
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitle(
"Efficiency of Long Tracks",
) # (" + str(round(hist_den[label[0]].GetBinWidth(1), 2)) + f"{unit})"+"^{-1}")
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
mygray = 18
myblue = kBlue - 9
for i, lab in enumerate(label):
rightmax = 1.05 * hist_den[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
hist_den[lab].Scale(scale)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
rightmax = 1.05 * hist_elec[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
hist_elec[lab].Scale(scale)
if i == 0:
if not plot_electrons_only:
set_style(hist_den[lab], mygray, markers[i], styles[i])
gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
hist_den[lab].Draw("HIST PLC SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
set_style(hist_elec[lab], myblue, markers[i], styles[i])
hist_elec[lab].SetFillColorAlpha(myblue, 0.35)
hist_elec[lab].Draw("HIST PLC SAME")
# else:
# print(
# "No distribution plotted for other labels.",
# "Can be added by uncommenting the code below this print statement.",
# )
# set_style(hist_den[lab], mygray, markers[i], styles[i])
# gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
# hist_den[lab].Draw("HIST PLC SAME")
if histo == "p":
pos = [0.53, 0.4, 1.01, 0.71]
elif histo == "pt":
pos = [0.5, 0.4, 0.98, 0.71]
elif histo == "phi":
pos = [0.3, 0.3, 0.9, 0.6]
else:
pos = [0.4, 0.37, 0.88, 0.68]
legend = place_legend(
canvas, *pos, header="LHCb Simulation", option="LPE"
)
for le in legend.GetListOfPrimitives():
if "distribution" in le.GetLabel():
le.SetOption("LF")
legend.SetTextFont(132)
legend.SetTextSize(0.04)
legend.Draw()
for lab in label:
if not plot_electrons_only:
eff[lab].Draw("P SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
eff_elec[lab].Draw("P SAME")
cutName = categories[tracker][cut]["title"]
latex.DrawLatex(legend.GetX1() + 0.01, legend.GetY1() - 0.05, cutName)
low = 0
high = 1.05
gPad.Update()
axis = TGaxis(
gPad.GetUxmax(),
gPad.GetUymin(),
gPad.GetUxmax(),
gPad.GetUymax(),
low,
high,
510,
"+U",
)
axis.SetTitleFont(132)
axis.SetTitleSize(0.06)
axis.SetTitleOffset(0.55)
axis.SetTitle(
"# Tracks " + get_nicer_var_string(histo) + " distribution [a.u.]",
)
axis.SetLabelSize(0)
axis.Draw()
canvas.RedrawAxis()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
if not plot_electrons_only:
canvasName = tracker + "_" + cut + "_" + histo + "." + ftype
else:
canvasName = (
tracker + "Electrons_" + cut + "_" + histo + "." + ftype
)
canvas.SaveAs("checks/" + canvasName)
# canvas.SetRightMargin(0.05)
canvas.Write()
# calculate ghost rate
histoBaseName = "Track/" + folder + tracker + "/"
for histo in ghostHistos[tracker]:
trackerDir.cd()
title = "ghost_rate_vs_" + histo
gPad.SetTicks()
histoName = histoBaseName + ghostHistoDict[histo]["variable"]
ghost = {}
hist_den = {}
ghost = get_ghost(ghost, hist_den, tf, histoName, label)
canvas = TCanvas(title, title)
mg = TMultiGraph()
for i, lab in enumerate(label):
mg.Add(ghost[lab])
set_style(ghost[lab], colors[i], markers[i], styles[i])
xtitle = ghostHistoDict[histo]["xTitle"]
mg.GetXaxis().SetTitle(xtitle)
mg.GetYaxis().SetTitle("Fraction of fake tracks")
mg.Draw("ap")
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
# for lab in label:
# ghost[lab].Draw("P SAME")
if histo == "p":
pos = [0.53, 0.4, 1.00, 0.71]
elif histo == "pt":
pos = [0.5, 0.4, 0.98, 0.71]
elif histo == "eta":
pos = [0.35, 0.6, 0.85, 0.9]
elif histo == "phi":
pos = [0.3, 0.3, 0.9, 0.6]
else:
pos = [0.4, 0.37, 0.80, 0.68]
legend = place_legend(canvas, *pos, header="LHCb Simulation", option="LPE")
legend.SetTextFont(132)
legend.SetTextSize(0.04)
legend.Draw()
# if histo != "nPV":
# latex.DrawLatex(0.7, 0.85, "LHCb simulation")
# else:
# latex.DrawLatex(0.2, 0.85, "LHCb simulation")
# mg.GetYaxis().SetRangeUser(0, 0.4)
if histo == "eta":
mg.GetYaxis().SetRangeUser(0, 0.4)
# track_name = names[tracker] + " tracks"
# latex.DrawLatex(0.7, 0.75, track_name)
# canvas.PlaceLegend()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
canvas.SaveAs(
"checks/" + tracker + "ghost_rate_" + histo + "." + ftype,
)
canvas.Write()
#
# Compare electron efficiencies of different trackers
#
plot_electrons_only = True
if compare:
print("\nCompare Efficiencies: ")
outputfile.cd()
compareDir = outputfile.mkdir("CompareEff")
compareDir.cd()
for jcut in compareCuts: # [long, long_fromB, long_fromB_P>5GeV]
for histo in efficiencyHistos: # [p, pt, phi, eta, nPV]
canvastitle = "efficiency_" + histo + "_" + jcut
name = "efficiency_" + histo + "_" + jcut
canvas = TCanvas(name, canvastitle)
canvas.SetRightMargin(0.1)
mg = TMultiGraph()
dist_eff = {}
dist_hist_den = {}
dist_eff_elec = {}
dist_hist_elec = {}
First = True
dist_tracker = ""
markeritr = 0
for tracker in trackers: # [BestLong, Forward, Match, Seed]
cut = compareDict[jcut][tracker]
folder = folders[tracker]["folder"]
print(folder)
jcolor = compareColors[tracker]
histoName = (
"Track/"
+ folder
+ tracker
+ "/"
+ cut
+ "_"
+ ""
+ efficiencyHistoDict[histo]["variable"]
)
print("not electrons: " + histoName)
eff = {}
hist_den = {}
eff, hist_den = get_eff(eff, hist_den, tf, histoName, label, histo)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
histoNameElec = (
"Track/"
+ folder
+ tracker
+ "/"
+ categories[tracker][cut]["Electrons"]
)
histoName_e = (
histoNameElec + "_" + efficiencyHistoDict[histo]["variable"]
)
print("electrons: " + histoName_e)
eff_elec = {}
hist_elec = {}
eff_elec, hist_elec = get_eff(
eff_elec,
hist_elec,
tf,
histoName_e,
label,
histo,
)
if First:
dist_eff_elec = eff_elec
dist_hist_elec = hist_elec
if First:
dist_tracker = tracker
dist_eff = eff
dist_hist_den = hist_den
First = False
for i, lab in enumerate(label):
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
mg.Add(eff_elec[lab])
set_style(
eff_elec[lab],
colors[jcolor + i],
markers[i + markeritr],
styles[i],
)
markeritr = markeritr + 1
# set_style(
# eff_elec[lab], colors[jcolor], markers[i], styles[i]
# )
markeritr = 0
mg.Draw("AP")
mg.GetYaxis().SetRangeUser(0, 1.05)
xtitle = efficiencyHistoDict[histo]["xTitle"]
unit_l = xtitle.split("[")
if "]" in unit_l[-1]:
unit = unit_l[-1].replace("]", "")
else:
unit = ""
print(unit)
mg.GetXaxis().SetTitle(xtitle)
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitle(
"Efficiency of Long Tracks",
) # (" + str(round(hist_den[label[0]].GetBinWidth(1), 2)) + f"{unit})"+"^{-1}")
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
mygray = 16
myblue = kBlue - 7
dist_cut = compareDict[jcut][dist_tracker]
for i, lab in enumerate(label):
rightmax = 1.05 * dist_hist_den[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
dist_hist_den[lab].Scale(scale)
if (
categories[dist_tracker][dist_cut]["plotElectrons"]
and plot_electrons
):
rightmax = 1.05 * dist_hist_elec[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
dist_hist_elec[lab].Scale(scale)
if i == len(label) - 1:
if not plot_electrons_only:
set_style(dist_hist_den[lab], mygray, markers[i], styles[i])
# gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
dist_hist_den[lab].SetFillColorAlpha(mygray, 0.5)
dist_hist_den[lab].Draw("HIST PLC SAME")
if (
categories[dist_tracker][dist_cut]["plotElectrons"]
and plot_electrons
):
set_style(
dist_hist_elec[lab], mygray, markers[i], styles[i]
)
# gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
# dist_hist_elec[lab].SetFillColor(myblue)
dist_hist_elec[lab].SetFillColorAlpha(myblue, 0.5)
dist_hist_elec[lab].Draw("HIST PLC SAME")
# else:
# print(
# "No distribution plotted for other labels.",
# "Can be added by uncommenting the code below this print statement.",
# )
# set_style(dist_hist_den[lab], mygray, markers[i], styles[i])
# gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
# dist_hist_den[lab].Draw("HIST PLC SAME")
if histo == "p":
pos = [0.53, 0.4, 1.01, 0.71]
elif histo == "pt":
pos = [0.5, 0.4, 0.98, 0.71]
elif histo == "phi":
pos = [0.3, 0.3, 0.9, 0.6]
else:
pos = [0.4, 0.37, 0.88, 0.68]
legend = place_legend(
canvas, *pos, header="LHCb Simulation", option="LPE"
)
for le in legend.GetListOfPrimitives():
if "distribution" in le.GetLabel():
le.SetOption("LF")
legend.SetTextFont(132)
legend.SetTextSize(0.04)
legend.Draw()
for lab in label:
if not plot_electrons_only:
dist_eff[lab].Draw("P SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
dist_eff_elec[lab].Draw("P SAME")
cutName = categories[tracker][cut]["title"]
latex.DrawLatex(legend.GetX1() + 0.01, legend.GetY1() - 0.05, cutName)
low = 0
high = 1.05
gPad.Update()
axis = TGaxis(
gPad.GetUxmax(),
gPad.GetUymin(),
gPad.GetUxmax(),
gPad.GetUymax(),
low,
high,
510,
"+U",
)
axis.SetTitleFont(132)
axis.SetTitleSize(0.06)
axis.SetTitleOffset(0.55)
axis.SetTitle(
"# Tracks " + get_nicer_var_string(histo) + " distribution [a.u.]",
)
axis.SetLabelSize(0)
axis.Draw()
canvas.RedrawAxis()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
if not plot_electrons_only:
canvasName = (
"Compare_"
+ tracker
+ "_"
+ cut
+ "_"
+ histo
+ "."
+ ftype
)
else:
canvasName = (
"Compare_"
+ tracker
+ "Electrons_"
+ cut
+ "_"
+ histo
+ "."
+ ftype
)
canvas.SaveAs("checks/" + canvasName)
# canvas.SetRightMargin(0.05)
canvas.Write()
outputfile.Write()
outputfile.Close()
if __name__ == "__main__":
parser = argument_parser()
args = parser.parse_args()
PrCheckerEfficiency(**vars(args))

520
scripts/MyPrCheckerEfficiency.py Normal file
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@ -0,0 +1,520 @@
# flake8: noqa
"""
Takes data from Recent_get_resolution_and_eff_data.py and calculates efficiencies
python scripts/MyPrCheckerEfficiency.py --filename data/resolutions_and_effs_B_normal_weights.root data/resolutions_and_effs_B_electron_weights.root
--outfile data_results/PrCheckerNormalElectron.root --label normal electron --trackers Match BestLong
python scripts/MyPrCheckerEfficiency.py --filename data/resolutions_and_effs_D_electron_weights.root --outfile data_results/PrCheckerDElectron.root
"""
import argparse
from ROOT import TMultiGraph, TLatex, TCanvas, TFile, TGaxis
from ROOT import (
kGreen,
kBlue,
kBlack,
kAzure,
kOrange,
kMagenta,
kCyan,
kGray,
kViolet,
kTeal,
)
from ROOT import gROOT, gStyle, gPad
from ROOT import TEfficiency
from array import array
gROOT.SetBatch(True)
from utils.components import unique_name_ext_re, findRootObjByName
def getEfficiencyHistoNames():
return ["p", "pt", "phi", "eta", "nPV"]
def getTrackers(trackers):
return trackers
# data/resolutions_and_effs_Bd2KstEE_MDmaster.root:Track/...
def getOriginFolders():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
# evtl anpassen wenn die folders anders heissen
basedict["Velo"]["folder"] = "VeloTrackChecker/"
basedict["Upstream"]["folder"] = "UpstreamTrackChecker/"
basedict["Forward"]["folder"] = "ForwardTrackChecker" + unique_name_ext_re() + "/"
basedict["Match"]["folder"] = "MatchTrackChecker" + unique_name_ext_re() + "/"
basedict["BestLong"]["folder"] = "BestLongTrackChecker" + unique_name_ext_re() + "/"
basedict["Seed"]["folder"] = "SeedTrackChecker" + unique_name_ext_re() + "/"
# basedict["Forward"]["folder"] = "ForwardTrackChecker_7a0dbfa7/"
# basedict["Match"]["folder"] = "MatchTrackChecker_29e3152a/"
# basedict["BestLong"]["folder"] = "BestLongTrackChecker_4ddacce1/"
# basedict["Seed"]["folder"] = "SeedTrackChecker_1b1d5575/"
return basedict
def getTrackNames():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
basedict["Velo"] = "Velo"
basedict["Upstream"] = "VeloUT"
basedict["Forward"] = "Forward"
basedict["Match"] = "Match"
basedict["BestLong"] = "BestLong"
basedict["Seed"] = "Seed"
return basedict
def get_colors():
# [kBlack, kGreen + 3, kAzure, kMagenta + 2, kOrange, kCyan + 2]
return [kBlack, kAzure, kGreen + 3, kMagenta + 2, kOrange, kCyan + 2]
def get_elec_colors():
# [kBlack, kGreen + 3, kAzure, kMagenta + 2, kOrange, kCyan + 2]
return [kGray + 2, kBlue - 7, kGreen + 1, kViolet, kOrange - 3, kTeal - 1]
def get_markers():
# [20, 24, 21, 22, 23, 25]
return [20, 21, 24, 25, 22, 23, 26, 32]
def get_fillstyles():
return [1003, 3001, 3002, 3325, 3144, 3244, 3444]
def getGhostHistoNames():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
basedict["Velo"] = ["eta", "nPV"]
basedict["Upstream"] = ["eta", "p", "pt", "nPV"]
basedict["Forward"] = ["eta", "p", "pt", "nPV"]
basedict["Match"] = ["eta", "p", "pt", "nPV"]
basedict["BestLong"] = ["eta", "p", "pt", "nPV"]
basedict["Seed"] = ["eta", "p", "pt", "nPV"]
return basedict
def argument_parser():
parser = argparse.ArgumentParser(description="location of the tuple file")
parser.add_argument(
"--filename",
type=str,
default=["data/resolutions_and_effs_B.root"],
nargs="+",
help="input files, including path",
)
parser.add_argument(
"--outfile",
type=str,
default="data_results/efficiency_plots.root",
help="output file",
)
parser.add_argument(
"--trackers",
type=str,
nargs="+",
default=["Forward", "Match", "BestLong", "Seed"], # ---
help="Trackers to plot.",
)
parser.add_argument(
"--label",
nargs="+",
default=["EffChecker"],
help="label for files",
)
parser.add_argument(
"--savepdf",
action="store_true",
help="save plots in pdf format",
)
parser.add_argument(
"--plot-electrons",
action="store_true",
default=True,
help="plot electrons",
)
parser.add_argument(
"--plot-electrons-only",
action="store_true",
help="plot only electrons",
)
return parser
def get_files(tf, filename, label):
for i, f in enumerate(filename):
tf[label[i]] = TFile(f, "read")
return tf
def get_nicer_var_string(var: str):
nice_vars = dict(pt="p_{T}", eta="#eta", phi="#phi")
try:
return nice_vars[var]
except KeyError:
return var
def get_eff(eff, hist, tf, histoName, label, var):
eff = {}
hist = {}
var = get_nicer_var_string(var)
for i, lab in enumerate(label):
numeratorName = histoName + "_reconstructed"
numerator = findRootObjByName(tf[lab], numeratorName)
denominatorName = histoName + "_reconstructible"
denominator = findRootObjByName(tf[lab], denominatorName)
if numerator.GetEntries() == 0 or denominator.GetEntries() == 0:
continue
teff = TEfficiency(numerator, denominator)
teff.SetStatisticOption(7)
eff[lab] = teff.CreateGraph()
eff[lab].SetName(lab)
eff[lab].SetTitle(lab + " not e^{-}")
if histoName.find("strange") != -1:
eff[lab].SetTitle(lab + " from stranges")
if histoName.find("electron") != -1:
eff[lab].SetTitle(lab + " e^{-}")
hist[lab] = denominator.Clone()
hist[lab].SetName("h_numerator_notElectrons")
hist[lab].SetTitle(var + " distribution, not e^{-}")
if histoName.find("strange") != -1:
hist[lab].SetTitle(var + " distribution, stranges")
if histoName.find("electron") != -1:
hist[lab].SetTitle(var + " distribution, e^{-}")
return eff, hist
def get_ghost(eff, hist, tf, histoName, label):
ghost = {}
for i, lab in enumerate(label):
numeratorName = histoName + "_Ghosts"
denominatorName = histoName + "_Total"
numerator = findRootObjByName(tf[lab], numeratorName)
denominator = findRootObjByName(tf[lab], denominatorName)
print("Numerator = " + numeratorName.replace(unique_name_ext_re(), ""))
print("Denominator = " + denominatorName.replace(unique_name_ext_re(), ""))
teff = TEfficiency(numerator, denominator)
teff.SetStatisticOption(7)
ghost[lab] = teff.CreateGraph()
print(lab)
ghost[lab].SetName(lab)
return ghost
def PrCheckerEfficiency(
filename,
outfile,
label,
trackers,
savepdf,
plot_electrons,
plot_electrons_only,
):
from utils.LHCbStyle import setLHCbStyle, set_style
from utils.ConfigHistos import (
efficiencyHistoDict,
ghostHistoDict,
categoriesDict,
getCuts,
)
from utils.Legend import place_legend
# from utils.components import unique_name_ext_re, findRootObjByName
setLHCbStyle()
markers = get_markers()
colors = get_colors()
elec_colors = get_elec_colors()
styles = get_fillstyles()
tf = {}
tf = get_files(tf, filename, label)
outputfile = TFile(outfile, "recreate")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.05)
efficiencyHistoDict = efficiencyHistoDict()
efficiencyHistos = getEfficiencyHistoNames()
ghostHistos = getGhostHistoNames()
ghostHistoDict = ghostHistoDict()
categories = categoriesDict()
cuts = getCuts()
trackers = getTrackers(trackers)
folders = getOriginFolders()
# names = getTrackNames()
for tracker in trackers:
outputfile.cd()
trackerDir = outputfile.mkdir(tracker)
trackerDir.cd()
for cut in cuts[tracker]:
cutDir = trackerDir.mkdir(cut)
cutDir.cd()
folder = folders[tracker]["folder"]
print("folder: " + folder.replace(unique_name_ext_re(), ""))
histoBaseName = "Track/" + folder + tracker + "/" + cut + "_"
# calculate efficiency
for histo in efficiencyHistos:
canvastitle = (
"efficiency_" + histo + ", " + categories[tracker][cut]["title"]
)
# get efficiency for not electrons category
histoName = histoBaseName + "" + efficiencyHistoDict[histo]["variable"]
print("not electrons: " + histoName.replace(unique_name_ext_re(), ""))
eff = {}
hist_den = {}
eff, hist_den = get_eff(eff, hist_den, tf, histoName, label, histo)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
histoNameElec = (
"Track/"
+ folder
+ tracker
+ "/"
+ categories[tracker][cut]["Electrons"]
)
histoName_e = (
histoNameElec + "_" + efficiencyHistoDict[histo]["variable"]
)
print("electrons: " + histoName_e.replace(unique_name_ext_re(), ""))
eff_elec = {}
hist_elec = {}
eff_elec, hist_elec = get_eff(
eff_elec,
hist_elec,
tf,
histoName_e,
label,
histo,
)
name = "efficiency_" + histo
canvas = TCanvas(name, canvastitle)
canvas.SetRightMargin(0.1)
mg = TMultiGraph()
for i, lab in enumerate(label):
if not plot_electrons_only:
mg.Add(eff[lab])
set_style(eff[lab], colors[i], markers[i], styles[i])
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
mg.Add(eff_elec[lab])
set_style(eff_elec[lab], elec_colors[i], markers[i], styles[i])
mg.Draw("AP")
mg.GetYaxis().SetRangeUser(0, 1.05)
xtitle = efficiencyHistoDict[histo]["xTitle"]
unit_l = xtitle.split("[")
if "]" in unit_l[-1]:
unit = unit_l[-1].replace("]", "")
else:
unit = "a.u."
print(unit)
mg.GetXaxis().SetTitle(xtitle)
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitle(
"Efficiency of Long Tracks",
) # (" + str(round(hist_den[label[0]].GetBinWidth(1), 2)) + f"{unit})"+"^{-1}")
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
mygray = 18
myblue = kBlue - 9
for i, lab in enumerate(label):
rightmax = 1.05 * hist_den[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
hist_den[lab].Scale(scale)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
rightmax = 1.05 * hist_elec[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
hist_elec[lab].Scale(scale)
if i == 0:
if not plot_electrons_only:
set_style(hist_den[lab], mygray, markers[i], styles[i])
gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
hist_den[lab].Draw("HIST PLC SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
set_style(hist_elec[lab], myblue, markers[i], styles[i])
hist_elec[lab].SetFillColorAlpha(myblue, 0.35)
hist_elec[lab].Draw("HIST PLC SAME")
# else:
# print(
# "No distribution plotted for other labels.",
# "Can be added by uncommenting the code below this print statement.",
# )
# set_style(hist_den[lab], mygray, markers[i], styles[i])
# gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
# hist_den[lab].Draw("HIST PLC SAME")
if histo == "p":
pos = [0.5, 0.3, 1.0, 0.5] # [0.53, 0.4, 1.01, 0.71]
elif histo == "pt":
pos = [0.5, 0.3, 0.99, 0.5] # [0.5, 0.4, 0.98, 0.71]
elif histo == "phi":
pos = [0.4, 0.3, 0.9, 0.5]
elif histo == "eta":
pos = [0.5, 0.25, 1.0, 0.45]
else:
pos = [0.35, 0.25, 0.85, 0.45]
legend = place_legend(
canvas, *pos, header="LHCb Simulation", option="LPE"
)
for le in legend.GetListOfPrimitives():
if "distribution" in le.GetLabel():
le.SetOption("LF")
legend.SetTextFont(132)
legend.SetTextSize(0.04)
legend.Draw()
for lab in label:
if not plot_electrons_only:
eff[lab].Draw("P SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
eff_elec[lab].Draw("P SAME")
cutName = categories[tracker][cut]["title"]
latex.DrawLatex(legend.GetX1() + 0.01, legend.GetY1() - 0.05, cutName)
low = 0
high = 1.05
gPad.Update()
axis = TGaxis(
gPad.GetUxmax(),
gPad.GetUymin(),
gPad.GetUxmax(),
gPad.GetUymax(),
low,
high,
510,
"+U",
)
axis.SetTitleFont(132)
axis.SetTitleSize(0.06)
axis.SetTitleOffset(0.55)
axis.SetTitle(
"# Tracks " + get_nicer_var_string(histo) + " distribution [a.u.]",
)
axis.SetLabelSize(0)
axis.Draw()
canvas.RedrawAxis()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
if not plot_electrons_only:
canvasName = tracker + "_" + cut + "_" + histo + "." + ftype
else:
canvasName = (
tracker + "Electrons_" + cut + "_" + histo + "." + ftype
)
canvas.SaveAs("checks/" + canvasName)
# canvas.SetRightMargin(0.05)
canvas.Write()
# calculate ghost rate
print("\ncalculate ghost rate: ")
histoBaseName = "Track/" + folder + tracker + "/"
for histo in ghostHistos[tracker]:
trackerDir.cd()
title = "ghost_rate_vs_" + histo
gPad.SetTicks()
histoName = histoBaseName + ghostHistoDict[histo]["variable"]
ghost = {}
hist_den = {}
ghost = get_ghost(ghost, hist_den, tf, histoName, label)
canvas = TCanvas(title, title)
mg = TMultiGraph()
for i, lab in enumerate(label):
mg.Add(ghost[lab])
set_style(ghost[lab], colors[i], markers[2 * i], styles[i])
xtitle = ghostHistoDict[histo]["xTitle"]
mg.GetXaxis().SetTitle(xtitle)
mg.GetYaxis().SetTitle("Fraction of fake tracks")
mg.Draw("ap")
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
# for lab in label:
# ghost[lab].Draw("P SAME")
if histo == "p":
pos = [0.53, 0.4, 1.00, 0.71]
elif histo == "pt":
pos = [0.5, 0.4, 0.98, 0.71]
elif histo == "eta":
pos = [0.35, 0.6, 0.85, 0.9]
elif histo == "phi":
pos = [0.3, 0.3, 0.9, 0.6]
else:
pos = [0.4, 0.37, 0.80, 0.68]
legend = place_legend(canvas, *pos, header="LHCb Simulation", option="LPE")
legend.SetTextFont(132)
legend.SetTextSize(0.04)
legend.Draw()
# if histo != "nPV":
# latex.DrawLatex(0.7, 0.85, "LHCb simulation")
# else:
# latex.DrawLatex(0.2, 0.85, "LHCb simulation")
# mg.GetYaxis().SetRangeUser(0, 0.4)
if histo == "eta":
mg.GetYaxis().SetRangeUser(0, 0.4)
# track_name = names[tracker] + " tracks"
# latex.DrawLatex(0.7, 0.75, track_name)
# canvas.PlaceLegend()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
canvas.SaveAs(
"checks/" + tracker + "ghost_rate_" + histo + "." + ftype,
)
canvas.Write()
outputfile.Write()
outputfile.Close()
if __name__ == "__main__":
parser = argument_parser()
args = parser.parse_args()
PrCheckerEfficiency(**vars(args))

500
scripts/ResidualPrCheckerEfficiency.py Normal file
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@ -0,0 +1,500 @@
# flake8: noqa
#
# Takes data from Recent_get_resolution_and_eff_data.py and calculates efficiencies
#
# python scripts/ResidualPrCheckerEfficiency.py
# --filename data/resolutions_and_effs_B.root
# --trackers Match --outfile data/compare_effs.root
#
import argparse
from ROOT import TMultiGraph, TLatex, TCanvas, TFile, TGaxis
from ROOT import kGreen, kBlue, kBlack, kAzure, kOrange, kMagenta, kCyan
from ROOT import gROOT, gStyle, gPad
from ROOT import TEfficiency
from array import array
gROOT.SetBatch(True)
from utils.components import unique_name_ext_re, findRootObjByName
def getEfficiencyHistoNames():
return ["p", "pt", "phi", "eta", "nPV"]
def getTrackers(trackers):
return trackers
# data/resolutions_and_effs_B.root:Track/...
def getOriginFolders():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
basedict["Velo"]["folder"] = "VeloTrackChecker/"
basedict["Upstream"]["folder"] = "UpstreamTrackChecker/"
basedict["Forward"]["folder"] = "ForwardTrackChecker" + unique_name_ext_re() + "/"
basedict["Match"]["folder"] = "MatchTrackChecker" + unique_name_ext_re() + "/"
basedict["BestLong"]["folder"] = "BestLongTrackChecker" + unique_name_ext_re() + "/"
basedict["Seed"]["folder"] = "SeedTrackChecker" + unique_name_ext_re() + "/"
# basedict["Forward"]["folder"] = "ForwardTrackChecker_7a0dbfa7/"
# basedict["Match"]["folder"] = "MatchTrackChecker_29e3152a/"
# basedict["ResidualMatch"]["folder"] = "ResidualMatchTrackChecker_955c7a21/"
# basedict["BestLong"]["folder"] = "BestLongTrackChecker_c163325d/"
# basedict["Seed"]["folder"] = "SeedTrackChecker_1b1d5575/"
return basedict
def getTrackNames():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
basedict["Velo"] = "Velo"
basedict["Upstream"] = "VeloUT"
basedict["Forward"] = "Forward"
basedict["Match"] = "Match"
basedict["BestLong"] = "BestLong"
basedict["Seed"] = "Seed"
return basedict
def get_colors():
return [kBlack, kGreen + 3, kAzure, kMagenta + 2, kOrange, kCyan + 2]
def get_markers():
return [20, 24, 21, 22, 23, 25]
def get_fillstyles():
return [1003, 3001, 3002, 3325, 3144, 3244, 3444]
def getGhostHistoNames():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"Match": {},
"BestLong": {},
"Seed": {},
}
basedict["Velo"] = ["eta", "nPV"]
basedict["Upstream"] = ["eta", "p", "pt", "nPV"]
basedict["Forward"] = ["eta", "p", "pt", "nPV"]
basedict["Match"] = ["eta", "p", "pt", "nPV"]
basedict["BestLong"] = ["eta", "p", "pt", "nPV"]
basedict["Seed"] = ["eta", "p", "pt", "nPV"]
return basedict
def argument_parser():
parser = argparse.ArgumentParser(description="location of the tuple file")
parser.add_argument(
"--filename",
type=str,
default=["data/resolutions_and_effs_Bd2KstEE_MDmaster.root"],
nargs="+",
help="input files, including path",
)
parser.add_argument(
"--outfile",
type=str,
default="data/efficiency_plots.root",
help="output file",
)
parser.add_argument(
"--trackers",
type=str,
nargs="+",
default=["Forward", "Match", "BestLong", "Seed"], # ---
help="Trackers to plot.",
)
parser.add_argument(
"--label",
nargs="+",
default=["EffChecker"],
help="label for files",
)
parser.add_argument(
"--savepdf",
action="store_true",
help="save plots in pdf format",
)
parser.add_argument(
"--plot-electrons",
action="store_true",
default=True,
help="plot electrons",
)
parser.add_argument(
"--plot-electrons-only",
action="store_true",
help="plot only electrons",
)
return parser
def get_files(tf, filename, label):
for i, f in enumerate(filename):
tf[label[i]] = TFile(f, "read")
return tf
def get_nicer_var_string(var: str):
nice_vars = dict(pt="p_{T}", eta="#eta", phi="#phi")
try:
return nice_vars[var]
except KeyError:
return var
def get_eff(eff, hist, tf, histoName, label, var):
eff = {}
hist = {}
var = get_nicer_var_string(var)
for i, lab in enumerate(label):
numeratorName = histoName + "_reconstructed"
numerator = findRootObjByName(tf[lab], numeratorName)
# numerator = tf[lab].Get(numeratorName)
denominatorName = histoName + "_reconstructible"
denominator = findRootObjByName(tf[lab], denominatorName)
# denominator = tf[lab].Get(denominatorName)
if numerator.GetEntries() == 0 or denominator.GetEntries() == 0:
continue
teff = TEfficiency(numerator, denominator)
teff.SetStatisticOption(7)
eff[lab] = teff.CreateGraph()
eff[lab].SetName(lab)
eff[lab].SetTitle(lab + " not e^{-}")
if histoName.find("strange") != -1:
eff[lab].SetTitle(lab + " from stranges")
if histoName.find("electron") != -1:
eff[lab].SetTitle(lab + " e^{-}")
hist[lab] = denominator.Clone()
hist[lab].SetName("h_numerator_notElectrons")
hist[lab].SetTitle(var + " distribution, not e^{-}")
if histoName.find("strange") != -1:
hist[lab].SetTitle(var + " distribution, stranges")
if histoName.find("electron") != -1:
hist[lab].SetTitle(var + " distribution, e^{-}")
return eff, hist
def get_ghost(eff, hist, tf, histoName, label):
ghost = {}
for i, lab in enumerate(label):
numeratorName = histoName + "_Ghosts"
denominatorName = histoName + "_Total"
numerator = findRootObjByName(tf[lab], numeratorName)
denominator = findRootObjByName(tf[lab], denominatorName)
# numerator = tf[lab].Get(numeratorName)
# denominator = tf[lab].Get(denominatorName)
print("Numerator = " + numeratorName)
print("Denominator = " + denominatorName)
teff = TEfficiency(numerator, denominator)
teff.SetStatisticOption(7)
ghost[lab] = teff.CreateGraph()
print(lab)
ghost[lab].SetName(lab)
return ghost
def PrCheckerEfficiency(
filename,
outfile,
label,
trackers,
savepdf,
plot_electrons,
plot_electrons_only,
):
from utils.LHCbStyle import setLHCbStyle, set_style
from utils.ConfigHistos import (
efficiencyHistoDict,
ghostHistoDict,
categoriesDict,
getCuts,
)
from utils.Legend import place_legend
setLHCbStyle()
markers = get_markers()
colors = get_colors()
styles = get_fillstyles()
tf = {}
tf = get_files(tf, filename, label)
outputfile = TFile(outfile, "recreate")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.05)
efficiencyHistoDict = efficiencyHistoDict()
efficiencyHistos = getEfficiencyHistoNames()
ghostHistos = getGhostHistoNames()
ghostHistoDict = ghostHistoDict()
categories = categoriesDict()
cuts = getCuts()
trackers = getTrackers(trackers)
folders = getOriginFolders()
# names = getTrackNames()
for tracker in trackers:
outputfile.cd()
trackerDir = outputfile.mkdir(tracker)
trackerDir.cd()
for cut in cuts[tracker]:
cutDir = trackerDir.mkdir(cut)
cutDir.cd()
folder = folders[tracker]["folder"]
print(folder)
histoBaseName = "Track/" + folder + tracker + "/" + cut + "_"
# calculate efficiency
for histo in efficiencyHistos:
canvastitle = (
"efficiency_" + histo + ", " + categories[tracker][cut]["title"]
)
# get efficiency for not electrons category
histoName = histoBaseName + "" + efficiencyHistoDict[histo]["variable"]
print("not electrons: " + histoName)
eff = {}
hist_den = {}
eff, hist_den = get_eff(eff, hist_den, tf, histoName, label, histo)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
histoNameElec = (
"Track/"
+ folder
+ tracker
+ "/"
+ categories[tracker][cut]["Electrons"]
)
histoName_e = (
histoNameElec + "_" + efficiencyHistoDict[histo]["variable"]
)
print("electrons: " + histoName_e)
eff_elec = {}
hist_elec = {}
eff_elec, hist_elec = get_eff(
eff_elec,
hist_elec,
tf,
histoName_e,
label,
histo,
)
name = "efficiency_" + histo
canvas = TCanvas(name, canvastitle)
canvas.SetRightMargin(0.1)
mg = TMultiGraph()
for i, lab in enumerate(label):
if not plot_electrons_only:
mg.Add(eff[lab])
set_style(eff[lab], colors[i], markers[i], styles[i])
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
mg.Add(eff_elec[lab])
set_style(
eff_elec[lab], colors[i + 2], markers[i + 1], styles[i]
)
mg.Draw("AP")
mg.GetYaxis().SetRangeUser(0, 1.05)
xtitle = efficiencyHistoDict[histo]["xTitle"]
unit_l = xtitle.split("[")
if "]" in unit_l[-1]:
unit = unit_l[-1].replace("]", "")
else:
unit = ""
print(unit)
mg.GetXaxis().SetTitle(xtitle)
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitle(
"Efficiency of Long Tracks",
) # (" + str(round(hist_den[label[0]].GetBinWidth(1), 2)) + f"{unit})"+"^{-1}")
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
mygray = 18
myblue = kBlue - 9
for i, lab in enumerate(label):
rightmax = 1.05 * hist_den[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
hist_den[lab].Scale(scale)
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
rightmax = 1.05 * hist_elec[lab].GetMaximum()
scale = gPad.GetUymax() / rightmax
hist_elec[lab].Scale(scale)
if i == 0:
if not plot_electrons_only:
set_style(hist_den[lab], mygray, markers[i], styles[i])
gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
hist_den[lab].Draw("HIST PLC SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
set_style(hist_elec[lab], myblue, markers[i], styles[i])
hist_elec[lab].SetFillColorAlpha(myblue, 0.35)
hist_elec[lab].Draw("HIST PLC SAME")
# else:
# print(
# "No distribution plotted for other labels.",
# "Can be added by uncommenting the code below this print statement.",
# )
# set_style(hist_den[lab], mygray, markers[i], styles[i])
# gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1]))
# hist_den[lab].Draw("HIST PLC SAME")
if histo == "p":
pos = [0.53, 0.4, 1.01, 0.71]
elif histo == "pt":
pos = [0.5, 0.4, 0.98, 0.71]
elif histo == "phi":
pos = [0.3, 0.3, 0.9, 0.6]
else:
pos = [0.4, 0.37, 0.88, 0.68]
legend = place_legend(
canvas, *pos, header="LHCb Simulation", option="LPE"
)
for le in legend.GetListOfPrimitives():
if "distribution" in le.GetLabel():
le.SetOption("LF")
legend.SetTextFont(132)
legend.SetTextSize(0.04)
legend.Draw()
for lab in label:
if not plot_electrons_only:
eff[lab].Draw("P SAME")
if categories[tracker][cut]["plotElectrons"] and plot_electrons:
eff_elec[lab].Draw("P SAME")
cutName = categories[tracker][cut]["title"]
latex.DrawLatex(legend.GetX1() + 0.01, legend.GetY1() - 0.05, cutName)
low = 0
high = 1.05
gPad.Update()
axis = TGaxis(
gPad.GetUxmax(),
gPad.GetUymin(),
gPad.GetUxmax(),
gPad.GetUymax(),
low,
high,
510,
"+U",
)
axis.SetTitleFont(132)
axis.SetTitleSize(0.06)
axis.SetTitleOffset(0.55)
axis.SetTitle(
"# Tracks " + get_nicer_var_string(histo) + " distribution [a.u.]",
)
axis.SetLabelSize(0)
axis.Draw()
canvas.RedrawAxis()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
if not plot_electrons_only:
canvasName = tracker + "_" + cut + "_" + histo + "." + ftype
else:
canvasName = (
tracker + "Electrons_" + cut + "_" + histo + "." + ftype
)
canvas.SaveAs("checks/" + canvasName)
# canvas.SetRightMargin(0.05)
canvas.Write()
# calculate ghost rate
histoBaseName = "Track/" + folder + tracker + "/"
for histo in ghostHistos[tracker]:
trackerDir.cd()
title = "ghost_rate_vs_" + histo
gPad.SetTicks()
histoName = histoBaseName + ghostHistoDict[histo]["variable"]
ghost = {}
hist_den = {}
ghost = get_ghost(ghost, hist_den, tf, histoName, label)
canvas = TCanvas(title, title)
mg = TMultiGraph()
for i, lab in enumerate(label):
mg.Add(ghost[lab])
set_style(ghost[lab], colors[i], markers[i], styles[i])
xtitle = ghostHistoDict[histo]["xTitle"]
mg.GetXaxis().SetTitle(xtitle)
mg.GetYaxis().SetTitle("Fraction of fake tracks")
mg.Draw("ap")
mg.GetXaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleSize(0.06)
mg.GetYaxis().SetTitleOffset(1.1)
mg.GetXaxis().SetRangeUser(*efficiencyHistoDict[histo]["range"])
mg.GetXaxis().SetNdivisions(10, 5, 0)
# for lab in label:
# ghost[lab].Draw("P SAME")
if histo == "p":
pos = [0.53, 0.4, 1.00, 0.71]
elif histo == "pt":
pos = [0.5, 0.4, 0.98, 0.71]
elif histo == "eta":
pos = [0.35, 0.6, 0.85, 0.9]
elif histo == "phi":
pos = [0.3, 0.3, 0.9, 0.6]
else:
pos = [0.4, 0.37, 0.80, 0.68]
legend = place_legend(canvas, *pos, header="LHCb Simulation", option="LPE")
legend.SetTextFont(132)
legend.SetTextSize(0.04)
legend.Draw()
# if histo != "nPV":
# latex.DrawLatex(0.7, 0.85, "LHCb simulation")
# else:
# latex.DrawLatex(0.2, 0.85, "LHCb simulation")
# mg.GetYaxis().SetRangeUser(0, 0.4)
if histo == "eta":
mg.GetYaxis().SetRangeUser(0, 0.4)
# track_name = names[tracker] + " tracks"
# latex.DrawLatex(0.7, 0.75, track_name)
# canvas.PlaceLegend()
if savepdf:
filestypes = ["pdf"] # , "png", "eps", "C", "ps", "tex"]
for ftype in filestypes:
canvas.SaveAs(
"checks/" + tracker + "ghost_rate_" + histo + "." + ftype,
)
canvas.Write()
outputfile.Write()
outputfile.Close()
if __name__ == "__main__":
parser = argument_parser()
args = parser.parse_args()
PrCheckerEfficiency(**vars(args))

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###############################################################################
# (c) Copyright 2019 CERN for the benefit of the LHCb Collaboration #
# #
# This software is distributed under the terms of the GNU General Public #
# Licence version 3 (GPL Version 3), copied verbatim in the file "COPYING". #
# #
# In applying this licence, CERN does not waive the privileges and immunities #
# granted to it by virtue of its status as an Intergovernmental Organization #
# or submit itself to any jurisdiction. #
###############################################################################
#
# flake8: noqa
from collections import defaultdict
def getCompare():
basedict = {
"long": {},
"long_fromB": {},
"long_fromB_P>5GeV": {},
}
basedict["long"]["BestLong"] = "01_long"
basedict["long"]["Seed"] = "02_long"
basedict["long"]["Match"] = "01_long"
basedict["long"]["ResidualMatch"] = "01_long"
basedict["long"]["Forward"] = "01_long"
basedict["long_fromB"]["BestLong"] = "05_long_fromB"
basedict["long_fromB"]["Seed"] = "04_long_fromB"
basedict["long_fromB"]["Match"] = "05_long_fromB"
basedict["long_fromB"]["ResidualMatch"] = "05_long_fromB"
basedict["long_fromB"]["Forward"] = "05_long_fromB"
basedict["long_fromB_P>5GeV"]["BestLong"] = "06_long_fromB_P>5GeV"
basedict["long_fromB_P>5GeV"]["Seed"] = "05_long_fromB_P>5GeV"
basedict["long_fromB_P>5GeV"]["Match"] = "06_long_fromB_P>5GeV"
basedict["long_fromB_P>5GeV"]["ResidualMatch"] = "06_long_fromB_P>5GeV"
basedict["long_fromB_P>5GeV"]["Forward"] = "06_long_fromB_P>5GeV"
return basedict
def getCompColors():
basedict = {
"Forward": {},
"Match": {},
"Seed": {},
"BestLong": {},
}
basedict["Forward"] = 0
basedict["Match"] = 4
basedict["Seed"] = 6
basedict["BestLong"] = 1
return basedict

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from collections import defaultdict
def efficiencyHistoDict():
basedict = {
"eta": {},
"p": {},
"pt": {},
"phi": {},
"nPV": {},
"docaz": {},
"z": {},
}
basedict["eta"]["xTitle"] = "#eta"
basedict["eta"]["variable"] = "Eta"
basedict["eta"]["range"] = [2, 5]
basedict["p"]["xTitle"] = "p [MeV]"
basedict["p"]["variable"] = "P"
basedict["p"]["range"] = [0, 50001]
basedict["pt"]["xTitle"] = "p_{T} [MeV]"
basedict["pt"]["variable"] = "Pt"
basedict["pt"]["range"] = [0, 5001]
basedict["phi"]["xTitle"] = "#phi [rad]"
basedict["phi"]["variable"] = "Phi"
basedict["phi"]["range"] = [-3.15, 3.15]
basedict["nPV"]["xTitle"] = "# of PVs"
basedict["nPV"]["variable"] = "nPV"
basedict["nPV"]["range"] = [0, 15]
basedict["docaz"]["xTitle"] = "docaz [mm]"
basedict["docaz"]["variable"] = "docaz"
basedict["docaz"]["range"] = [0, 10]
basedict["z"]["xTitle"] = "PV z coordinate [mm]"
basedict["z"]["variable"] = "z"
basedict["z"]["range"] = [-200, 200]
return basedict
def ghostHistoDict():
basedict = {
"eta": {},
"nPV": {},
"pt": {},
"p": {},
}
basedict["eta"]["xTitle"] = "#eta"
basedict["eta"]["variable"] = "Eta"
basedict["eta"]["range"] = [1.9, 5.1]
basedict["nPV"]["xTitle"] = "# of PVs"
basedict["nPV"]["variable"] = "nPV"
basedict["nPV"]["range"] = [0, 16.5]
basedict["pt"]["xTitle"] = "p_{T} [MeV]"
basedict["pt"]["variable"] = "Pt"
basedict["pt"]["range"] = [0, 5000]
basedict["p"]["xTitle"] = "p [MeV]"
basedict["p"]["variable"] = "P"
basedict["p"]["range"] = [0, 50000]
return basedict
def getCuts():
basedict = {
"Velo": {},
"Upstream": {},
"Forward": {},
"MuonMatch": {},
"Match": {},
"ResidualMatch": {},
"Seed": {},
"Downstream": {},
"BestLong": {},
"BestDownstream": {},
"LongGhostFiltered": {},
"DownstreamGhostFiltered": {},
}
basedict["Velo"] = [
"01_velo",
"02_long",
"03_long_P>5GeV",
"04_long_strange",
"05_long_strange_P>5GeV",
"06_long_fromB",
"07_long_fromB_P>5GeV",
"11_long_fromB_P>3GeV_Pt>0.5GeV",
"12_UT_long_fromB_P>3GeV_Pt>0.5GeV",
]
basedict["Upstream"] = [
"01_velo",
"02_velo+UT",
"03_velo+UT_P>5GeV",
"07_long",
"08_long_P>5GeV",
"09_long_fromB",
"10_long_fromB_P>5GeV",
"14_long_fromB_P>3GeV_Pt>0.5GeV",
"15_UT_long_fromB_P>3GeV_Pt>0.5GeV",
]
basedict["Forward"] = [
"01_long",
"02_long_P>5GeV",
"03_long_strange",
"04_long_strange_P>5GeV",
"05_long_fromB",
"06_long_fromB_P>5GeV",
"10_long_fromB_P>3GeV_Pt>0.5GeV",
"11_UT_long_fromB_P>3GeV_Pt>0.5GeV",
]
basedict["MuonMatch"] = ["01_long", "02_long_muon", "04_long_pion"]
basedict["Match"] = [
"01_long",
"02_long_P>5GeV",
"03_long_strange",
"04_long_strange_P>5GeV",
"05_long_fromB",
"06_long_fromB_P>5GeV",
"10_long_fromB_P>3GeV_Pt>0.5GeV",
"11_UT_long_fromB_P>3GeV_Pt>0.5GeV",
]
basedict["ResidualMatch"] = basedict["Match"]
basedict["Seed"] = [
"01_hasT",
"02_long",
"03_long_P>5GeV",
"04_long_fromB",
"05_long_fromB_P>5GeV",
# "08_noVelo+UT+T_strange",
# "09_noVelo+UT+T_strange_P>5GeV",
# "12_noVelo+UT+T_SfromDB_P>5GeV",
]
basedict["Downstream"] = [
"01_UT+T",
"05_noVelo+UT+T_strange",
"06_noVelo+UT+T_strange_P>5GeV",
"13_noVelo+UT+T_SfromDB",
"14_noVelo+UT+T_SfromDB_P>5GeV",
]
basedict["BestLong"] = [
"01_long",
"02_long_P>5GeV",
"03_long_strange",
"04_long_strange_P>5GeV",
"05_long_fromB",
"06_long_fromB_P>5GeV",
"10_long_fromB_P>3GeV_Pt>0.5GeV",
]
basedict["BestDownstream"] = [
"01_UT+T",
"05_noVelo+UT+T_strange",
"06_noVelo+UT+T_strange_P>5GeV",
"13_noVelo+UT+T_SfromDB",
"14_noVelo+UT+T_SfromDB_P>5GeV",
]
basedict["LongGhostFiltered"] = [
"01_long",
"02_long_P>5GeV",
"03_long_strange",
"04_long_strange_P>5GeV",
"05_long_fromB",
"06_long_fromB_P>5GeV",
"10_long_fromB_P>3GeV_Pt>0.5GeV",
]
basedict["DownstreamGhostFiltered"] = [
"01_UT+T",
"05_noVelo+UT+T_strange",
"06_noVelo+UT+T_strange_P>5GeV",
"13_noVelo+UT+T_SfromDB",
"14_noVelo+UT+T_SfromDB_P>5GeV",
]
return basedict
def categoriesDict():
basedict = defaultdict(lambda: defaultdict(dict))
# VELO
basedict["Velo"]["01_velo"]["title"] = "Velo, 2 <#eta< 5"
basedict["Velo"]["02_long"]["title"] = "Long, 2 <#eta< 5"
basedict["Velo"]["03_long_P>5GeV"]["title"] = "Long, p>5GeV, 2<#eta< 5"
basedict["Velo"]["04_long_strange"]["title"] = "Long, from Strange, 2 <#eta < 5"
basedict["Velo"]["05_long_strange_P>5GeV"][
"title"
] = "Long, from Strange, p>5GeV, 2 <#eta < 5"
basedict["Velo"]["06_long_fromB"]["title"] = "Long from B, 2<#eta<5"
basedict["Velo"]["07_long_fromB_P>5GeV"]["title"] = "Long from B, p>5GeV, 2<#eta<5"
basedict["Velo"]["11_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from B, p>3GeV, pt>0.5GeV, 2<#eta<5"
basedict["Velo"]["11_long_strange_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from strange, p>3GeV, pt>0.5GeV, 2<#eta<5"
basedict["Velo"]["12_UT_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "UT Long, from B, p>3GeV, pt>0.5GeV, 2<#eta<5"
basedict["Velo"]["01_velo"]["plotElectrons"] = False
basedict["Velo"]["02_long"]["plotElectrons"] = True
basedict["Velo"]["03_long_P>5GeV"]["plotElectrons"] = False
basedict["Velo"]["04_long_strange"]["plotElectrons"] = False
basedict["Velo"]["05_long_strange_P>5GeV"]["plotElectrons"] = False
basedict["Velo"]["06_long_fromB"]["plotElectrons"] = True
basedict["Velo"]["07_long_fromB_P>5GeV"]["plotElectrons"] = True
basedict["Velo"]["11_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["Velo"]["11_long_strange_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["Velo"]["12_UT_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["Velo"]["02_long"]["Electrons"] = "08_long_electrons"
basedict["Velo"]["06_long_fromB"]["Electrons"] = "09_long_fromB_electrons"
basedict["Velo"]["07_long_fromB_P>5GeV"][
"Electrons"
] = "10_long_fromB_electrons_P>5GeV"
basedict["Velo"]["11_long_fromB_P>3GeV_Pt>0.5GeV"][
"Electrons"
] = "11_long_fromB_electrons_P>3GeV_Pt>0.5GeV"
# UPSTREAM
basedict["Upstream"]["01_velo"]["title"] = "Velo, 2 <#eta < 5"
basedict["Upstream"]["02_velo+UT"]["title"] = "VeloUT, 2 <#eta < 5"
basedict["Upstream"]["03_velo+UT_P>5GeV"]["title"] = "VeloUT, p>5GeV, 2 <#eta < 5"
basedict["Upstream"]["07_long"]["title"] = "Long, 2 <#eta < 5"
basedict["Upstream"]["08_long_P>5GeV"]["title"] = "Long, p>5GeV, 2 <#eta < 5"
basedict["Upstream"]["09_long_fromB"]["title"] = "Long from B, 2 <#eta < 5"
basedict["Upstream"]["10_long_fromB_P>5GeV"][
"title"
] = "Long from B, p>5GeV, 2 <#eta < 5"
basedict["Upstream"]["14_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long, from B, p>3GeV, pt>0.5GeV"
basedict["Upstream"]["14_long_strange_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long, from strange, p>3GeV, pt>0.5GeV"
basedict["Upstream"]["15_UT_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long, from B, p>3GeV, pt>0.5GeV"
basedict["Upstream"]["01_velo"]["plotElectrons"] = False
basedict["Upstream"]["02_velo+UT"]["plotElectrons"] = False
basedict["Upstream"]["03_velo+UT_P>5GeV"]["plotElectrons"] = False
basedict["Upstream"]["07_long"]["plotElectrons"] = True
basedict["Upstream"]["08_long_P>5GeV"]["plotElectrons"] = False
basedict["Upstream"]["09_long_fromB"]["plotElectrons"] = True
basedict["Upstream"]["10_long_fromB_P>5GeV"]["plotElectrons"] = True
basedict["Upstream"]["14_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["Upstream"]["14_long_strange_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["Upstream"]["15_UT_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["Upstream"]["07_long"]["Electrons"] = "11_long_electrons"
basedict["Upstream"]["09_long_fromB"]["Electrons"] = "12_long_fromB_electrons"
basedict["Upstream"]["10_long_fromB_P>5GeV"][
"Electrons"
] = "13_long_fromB_electrons_P>5GeV"
basedict["Upstream"]["14_long_fromB_P>3GeV_Pt>0.5GeV"][
"Electrons"
] = "14_long_fromB_electrons_P>3GeV_Pt>0.5GeV"
# FORwARD
basedict["Forward"]["01_long"]["Electrons"] = "07_long_electrons"
basedict["Forward"]["05_long_fromB"]["Electrons"] = "08_long_fromB_electrons"
basedict["Forward"]["06_long_fromB_P>5GeV"][
"Electrons"
] = "09_long_fromB_electrons_P>5GeV"
basedict["Forward"]["10_long_fromB_P>3GeV_Pt>0.5GeV"][
"Electrons"
] = "10_long_fromB_electrons_P>3GeV_Pt>0.5GeV"
basedict["Forward"]["01_long"]["title"] = "Long, 2 <#eta < 5"
basedict["Forward"]["02_long_P>5GeV"]["title"] = "Long, p>5GeV, 2 <#eta < 5"
basedict["Forward"]["03_long_strange"]["title"] = "Long, from strange, 2 <#eta < 5"
basedict["Forward"]["04_long_strange_P>5GeV"][
"title"
] = "Long, from strange, p>5GeV, 2 <#eta < 5"
basedict["Forward"]["05_long_fromB"]["title"] = "Long from B, 2 <#eta < 5"
basedict["Forward"]["06_long_fromB_P>5GeV"][
"title"
] = "Long from B, p>5GeV 2 <#eta < 5"
basedict["Forward"]["10_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from B, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["Forward"]["10_long_strange_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from strange, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["Forward"]["11_UT_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "UT Long from B, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["Forward"]["01_long"]["plotElectrons"] = True
basedict["Forward"]["02_long_P>5GeV"]["plotElectrons"] = False
basedict["Forward"]["03_long_strange"]["plotElectrons"] = False
basedict["Forward"]["04_long_strange_P>5GeV"]["plotElectrons"] = False
basedict["Forward"]["05_long_fromB"]["plotElectrons"] = True
basedict["Forward"]["06_long_fromB_P>5GeV"]["plotElectrons"] = True
basedict["Forward"]["10_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["Forward"]["11_UT_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
# MUONMATCH
basedict["MuonMatch"]["01_long"]["title"] = "Long, forward track, 2 <#eta< 5"
basedict["MuonMatch"]["02_long_muon"][
"title"
] = "Long, #mu, forward track, 2 <#eta< 5"
basedict["MuonMatch"]["04_long_pion"][
"title"
] = "Long, #pi, forward track, 2 <#eta< 5"
# MATCH
basedict["Match"]["01_long"]["Electrons"] = "07_long_electrons"
basedict["Match"]["05_long_fromB"]["Electrons"] = "08_long_fromB_electrons"
basedict["Match"]["06_long_fromB_P>5GeV"][
"Electrons"
] = "09_long_fromB_electrons_P>5GeV"
basedict["Match"]["10_long_fromB_P>3GeV_Pt>0.5GeV"][
"Electrons"
] = "10_long_fromB_electrons_P>3GeV_Pt>0.5GeV"
basedict["Match"]["01_long"]["title"] = "Long, 2 <#eta < 5"
basedict["Match"]["02_long_P>5GeV"]["title"] = "Long, p>5GeV, 2 <#eta < 5"
basedict["Match"]["03_long_strange"]["title"] = "Long, from strange, 2 <#eta < 5"
basedict["Match"]["04_long_strange_P>5GeV"][
"title"
] = "Long, from strange, p>5GeV, 2 <#eta < 5"
basedict["Match"]["05_long_fromB"]["title"] = "Long from B, 2 <#eta < 5"
basedict["Match"]["06_long_fromB_P>5GeV"][
"title"
] = "Long from B, p>5GeV 2 <#eta < 5"
basedict["Match"]["10_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from B, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["Match"]["10_long_strange_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from strange, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["Match"]["11_UT_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "UT Long from B, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["Match"]["01_long"]["plotElectrons"] = True
basedict["Match"]["02_long_P>5GeV"]["plotElectrons"] = False
basedict["Match"]["03_long_strange"]["plotElectrons"] = False
basedict["Match"]["04_long_strange_P>5GeV"]["plotElectrons"] = False
basedict["Match"]["05_long_fromB"]["plotElectrons"] = True
basedict["Match"]["06_long_fromB_P>5GeV"]["plotElectrons"] = True
basedict["Match"]["10_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["Match"]["10_long_strange_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["Match"]["11_UT_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
# ResidualMATCH
basedict["ResidualMatch"]["01_long"]["Electrons"] = "07_long_electrons"
basedict["ResidualMatch"]["05_long_fromB"]["Electrons"] = "08_long_fromB_electrons"
basedict["ResidualMatch"]["06_long_fromB_P>5GeV"][
"Electrons"
] = "09_long_fromB_electrons_P>5GeV"
basedict["ResidualMatch"]["10_long_fromB_P>3GeV_Pt>0.5GeV"][
"Electrons"
] = "10_long_fromB_electrons_P>3GeV_Pt>0.5GeV"
basedict["ResidualMatch"]["01_long"]["title"] = "Long, 2 <#eta < 5"
basedict["ResidualMatch"]["02_long_P>5GeV"]["title"] = "Long, p>5GeV, 2 <#eta < 5"
basedict["ResidualMatch"]["03_long_strange"][
"title"
] = "Long, from strange, 2 <#eta < 5"
basedict["ResidualMatch"]["04_long_strange_P>5GeV"][
"title"
] = "Long, from strange, p>5GeV, 2 <#eta < 5"
basedict["ResidualMatch"]["05_long_fromB"]["title"] = "Long from B, 2 <#eta < 5"
basedict["ResidualMatch"]["06_long_fromB_P>5GeV"][
"title"
] = "Long from B, p>5GeV 2 <#eta < 5"
basedict["ResidualMatch"]["10_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from B, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["ResidualMatch"]["10_long_strange_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from strange, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["ResidualMatch"]["11_UT_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "UT Long from B, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["ResidualMatch"]["01_long"]["plotElectrons"] = True
basedict["ResidualMatch"]["02_long_P>5GeV"]["plotElectrons"] = False
basedict["ResidualMatch"]["03_long_strange"]["plotElectrons"] = False
basedict["ResidualMatch"]["04_long_strange_P>5GeV"]["plotElectrons"] = False
basedict["ResidualMatch"]["05_long_fromB"]["plotElectrons"] = True
basedict["ResidualMatch"]["06_long_fromB_P>5GeV"]["plotElectrons"] = True
basedict["ResidualMatch"]["10_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["ResidualMatch"]["10_long_strange_P>3GeV_Pt>0.5GeV"][
"plotElectrons"
] = False
basedict["ResidualMatch"]["11_UT_long_fromB_P>3GeV_Pt>0.5GeV"][
"plotElectrons"
] = False
# SEED
basedict["Seed"]["01_hasT"]["Electrons"] = "13_hasT_electrons"
basedict["Seed"]["02_long"]["Electrons"] = "14_long_electrons"
basedict["Seed"]["03_long_P>5GeV"]["Electrons"] = "16_long_electrons_P>5GeV"
basedict["Seed"]["04_long_fromB"]["Electrons"] = "15_long_fromB_electrons"
basedict["Seed"]["05_long_fromB_P>5GeV"][
"Electrons"
] = "17_long_fromB_electrons_P>5GeV"
basedict["Seed"]["01_hasT"]["title"] = "T, 2 <#eta < 5"
basedict["Seed"]["02_long"]["title"] = "Long, 2 <#eta < 5"
basedict["Seed"]["03_long_P>5GeV"]["title"] = "Long, p>5GeV, 2 <#eta < 5"
basedict["Seed"]["04_long_fromB"]["title"] = "Long, from B, 2 <#eta < 5"
basedict["Seed"]["05_long_fromB_P>5GeV"][
"title"
] = "Long from B, p>5GeV, 2 <#eta < 5"
basedict["Seed"]["08_noVelo+UT+T_strange"][
"title"
] = "Down from strange, 2 <#eta < 5"
basedict["Seed"]["09_noVelo+UT+T_strange_P>5GeV"][
"title"
] = "Down from strange, p>5GeV, 2 <#eta < 5"
basedict["Seed"]["12_noVelo+UT+T_SfromDB_P>5GeV"][
"title"
] = "Down from strange from B/D, p>5GeV, 2 <#eta < 5"
basedict["Seed"]["01_hasT"]["plotElectrons"] = False
basedict["Seed"]["02_long"]["plotElectrons"] = True
basedict["Seed"]["03_long_P>5GeV"]["plotElectrons"] = False
basedict["Seed"]["04_long_fromB"]["plotElectrons"] = True
basedict["Seed"]["05_long_fromB_P>5GeV"]["plotElectrons"] = True
##################################
basedict["Seed"]["08_noVelo+UT+T_strange"]["plotElectrons"] = False
basedict["Seed"]["09_noVelo+UT+T_strange_P>5GeV"]["plotElectrons"] = False
basedict["Seed"]["12_noVelo+UT+T_SfromDB_P>5GeV"]["plotElectrons"] = False
# DOWNSTREAM
basedict["Downstream"]["01_UT+T"]["title"] = "UT+T, 2 <#eta < 5"
basedict["Downstream"]["05_noVelo+UT+T_strange"][
"title"
] = "Down from strange, 2 <#eta < 5"
basedict["Downstream"]["06_noVelo+UT+T_strange_P>5GeV"][
"title"
] = "Down from strange, p>5GeV, 2 <#eta < 5"
basedict["Downstream"]["13_noVelo+UT+T_SfromDB"][
"title"
] = "Down from strange from B/D, 2 <#eta < 5"
basedict["Downstream"]["14_noVelo+UT+T_SfromDB_P>5GeV"][
"title"
] = "Down from strange from B/D, p>5GeV, 2 <#eta < 5"
basedict["Downstream"]["01_UT+T"]["plotElectrons"] = False
basedict["Downstream"]["05_noVelo+UT+T_strange"]["plotElectrons"] = False
basedict["Downstream"]["06_noVelo+UT+T_strange_P>5GeV"]["plotElectrons"] = False
basedict["Downstream"]["13_noVelo+UT+T_SfromDB"]["plotElectrons"] = False
basedict["Downstream"]["14_noVelo+UT+T_SfromDB_P>5GeV"]["plotElectrons"] = False
# BESTLONG
basedict["BestLong"]["01_long"]["title"] = "Long, 2 <#eta < 5"
basedict["BestLong"]["02_long_P>5GeV"]["title"] = "Long, p>5GeV, 2 <#eta < 5"
basedict["BestLong"]["03_long_strange"]["title"] = "Long, from strange, 2 <#eta < 5"
basedict["BestLong"]["04_long_strange_P>5GeV"][
"title"
] = "Long, from strange, p>5GeV, 2 <#eta < 5"
basedict["BestLong"]["05_long_fromB"]["title"] = "Long from B, 2 <#eta < 5"
basedict["BestLong"]["06_long_fromB_P>5GeV"][
"title"
] = "Long from B, p>5GeV 2 <#eta < 5"
basedict["BestLong"]["10_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from B, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["BestLong"]["10_long_strange_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from strange, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["BestLong"]["01_long"]["plotElectrons"] = True
basedict["BestLong"]["02_long_P>5GeV"]["plotElectrons"] = False
basedict["BestLong"]["03_long_strange"]["plotElectrons"] = False
basedict["BestLong"]["04_long_strange_P>5GeV"]["plotElectrons"] = False
basedict["BestLong"]["05_long_fromB"]["plotElectrons"] = True
basedict["BestLong"]["06_long_fromB_P>5GeV"]["plotElectrons"] = True
basedict["BestLong"]["10_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["BestLong"]["10_long_strange_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False
basedict["BestLong"]["01_long"]["Electrons"] = "07_long_electrons"
basedict["BestLong"]["05_long_fromB"]["Electrons"] = "08_long_fromB_electrons"
basedict["BestLong"]["06_long_fromB_P>5GeV"][
"Electrons"
] = "09_long_fromB_electrons_P>5GeV"
basedict["BestLong"]["10_long_fromB_P>3GeV_Pt>0.5GeV"][
"Electrons"
] = "10_long_fromB_electrons_P>3GeV_Pt>0.5GeV"
#
# BESTDOWNSTREAM
basedict["BestDownstream"]["01_UT+T"]["title"] = "UT+T, 2 <#eta < 5"
basedict["BestDownstream"]["05_noVelo+UT+T_strange"][
"title"
] = "Down from strange, 2 <#eta < 5"
basedict["BestDownstream"]["06_noVelo+UT+T_strange_P>5GeV"][
"title"
] = "Down from strange, p>5GeV, 2 <#eta < 5"
basedict["BestDownstream"]["13_noVelo+UT+T_SfromDB"][
"title"
] = "Down from strange from B/D, 2 <#eta < 5"
basedict["BestDownstream"]["14_noVelo+UT+T_SfromDB_P>5GeV"][
"title"
] = "Down from strange from B/D, p>5GeV, 2 <#eta < 5"
basedict["BestDownstream"]["01_UT+T"]["plotElectrons"] = False
basedict["BestDownstream"]["05_noVelo+UT+T_strange"]["plotElectrons"] = False
basedict["BestDownstream"]["06_noVelo+UT+T_strange_P>5GeV"]["plotElectrons"] = False
basedict["BestDownstream"]["13_noVelo+UT+T_SfromDB"]["plotElectrons"] = False
basedict["BestDownstream"]["14_noVelo+UT+T_SfromDB_P>5GeV"]["plotElectrons"] = False
#
# LONGGHOSTFILTERED
basedict["LongGhostFiltered"]["01_long"]["title"] = "Long, 2 <#eta < 5"
basedict["LongGhostFiltered"]["02_long_P>5GeV"][
"title"
] = "Long, p>5GeV, 2 <#eta < 5"
basedict["LongGhostFiltered"]["03_long_strange"][
"title"
] = "Long, from strange, 2 <#eta < 5"
basedict["LongGhostFiltered"]["04_long_strange_P>5GeV"][
"title"
] = "Long, from strange, p>5GeV, 2 <#eta < 5"
basedict["LongGhostFiltered"]["05_long_fromB"]["title"] = "Long from B, 2 <#eta < 5"
basedict["LongGhostFiltered"]["06_long_fromB_P>5GeV"][
"title"
] = "Long from B, p>5GeV 2 <#eta < 5"
basedict["LongGhostFiltered"]["10_long_fromB_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from B, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["LongGhostFiltered"]["10_long_strange_P>3GeV_Pt>0.5GeV"][
"title"
] = "Long from strange, p>3GeV, pt>0.5GeV, 2 <#eta < 5"
basedict["LongGhostFiltered"]["01_long"]["plotElectrons"] = True
basedict["LongGhostFiltered"]["02_long_P>5GeV"]["plotElectrons"] = False
basedict["LongGhostFiltered"]["03_long_strange"]["plotElectrons"] = False
basedict["LongGhostFiltered"]["04_long_strange_P>5GeV"]["plotElectrons"] = False
basedict["LongGhostFiltered"]["05_long_fromB"]["plotElectrons"] = True
basedict["LongGhostFiltered"]["06_long_fromB_P>5GeV"]["plotElectrons"] = True
basedict["LongGhostFiltered"]["10_long_fromB_P>3GeV_Pt>0.5GeV"][
"plotElectrons"
] = False
basedict["LongGhostFiltered"]["10_long_strange_P>3GeV_Pt>0.5GeV"][
"plotElectrons"
] = False
basedict["LongGhostFiltered"]["01_long"]["Electrons"] = "07_long_electrons"
basedict["LongGhostFiltered"]["05_long_fromB"][
"Electrons"
] = "08_long_fromB_electrons"
basedict["LongGhostFiltered"]["06_long_fromB_P>5GeV"][
"Electrons"
] = "09_long_fromB_electrons_P>5GeV"
basedict["LongGhostFiltered"]["10_long_fromB_P>3GeV_Pt>0.5GeV"][
"Electrons"
] = "10_long_fromB_electrons_P>3GeV_Pt>0.5GeV"
# DOWNSTREAMGHOSTFILTERED
basedict["DownstreamGhostFiltered"]["01_UT+T"]["title"] = "UT+T, 2 <#eta < 5"
basedict["DownstreamGhostFiltered"]["05_noVelo+UT+T_strange"][
"title"
] = "Down from strange, 2 <#eta < 5"
basedict["DownstreamGhostFiltered"]["06_noVelo+UT+T_strange_P>5GeV"][
"title"
] = "Down from strange, p>5GeV, 2 <#eta < 5"
basedict["DownstreamGhostFiltered"]["13_noVelo+UT+T_SfromDB"][
"title"
] = "Down from strange from B/D, 2 <#eta < 5"
basedict["DownstreamGhostFiltered"]["14_noVelo+UT+T_SfromDB_P>5GeV"][
"title"
] = "Down from strange from B/D, p>5GeV, 2 <#eta < 5"
basedict["DownstreamGhostFiltered"]["01_UT+T"]["plotElectrons"] = False
basedict["DownstreamGhostFiltered"]["05_noVelo+UT+T_strange"][
"plotElectrons"
] = False
basedict["DownstreamGhostFiltered"]["06_noVelo+UT+T_strange_P>5GeV"][
"plotElectrons"
] = False
basedict["DownstreamGhostFiltered"]["13_noVelo+UT+T_SfromDB"][
"plotElectrons"
] = False
basedict["DownstreamGhostFiltered"]["14_noVelo+UT+T_SfromDB_P>5GeV"][
"plotElectrons"
] = False
return basedict

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from ROOT import gROOT, TH1, TH1F, TH1D, TEfficiency
from ROOT import TStyle
def set_style(graph, color, marker, style):
graph.SetLineColor(color)
graph.SetMarkerColor(color)
graph.SetMarkerSize(1.0)
graph.SetMarkerStyle(marker)
if (
type(graph) == TH1F
or type(graph) == TH1
or type(graph) == TH1D
or type(graph) == TEfficiency
):
graph.SetFillColor(color)
# graph.SetFillColorAlpha(color, 0.5)
graph.SetFillStyle(style)
graph.SetLineWidth(2)
if style == 0:
graph.SetFillColor(0)
graph.SetStats(False)
graph.GetYaxis().SetTitleOffset(1.0)
if type(graph) != TEfficiency:
graph.GetYaxis().SetTitleOffset(0.85)
graph.GetYaxis().SetTitleSize(0.06)
graph.GetYaxis().SetLabelSize(0.06)
graph.GetXaxis().SetTitleSize(0.06)
graph.GetXaxis().SetLabelSize(0.06)
graph.GetXaxis().SetTitleFont(132)
graph.GetXaxis().SetLabelFont(132)
graph.GetYaxis().SetTitleFont(132)
graph.GetYaxis().SetLabelFont(132)
return graph
def setLHCbStyle():
global lhcbStyle
lhcbFont = 132
lhcbTSize = 0.05
lhcbWidth = 2
lhcbStyle = TStyle("lhcbStyle", "LHCb plots style")
lhcbStyle.SetFillColor(1)
lhcbStyle.SetFillStyle(1001) # solid
lhcbStyle.SetFrameFillColor(0)
lhcbStyle.SetFrameBorderMode(0)
lhcbStyle.SetPadBorderMode(0)
lhcbStyle.SetPadColor(0)
lhcbStyle.SetCanvasBorderMode(0)
lhcbStyle.SetCanvasColor(0)
lhcbStyle.SetStatColor(0)
lhcbStyle.SetLegendBorderSize(0)
lhcbStyle.SetLegendFont(132)
# use large fonts
lhcbStyle.SetTextFont(lhcbFont)
lhcbStyle.SetTitleFont(lhcbFont)
lhcbStyle.SetTextSize(lhcbTSize)
lhcbStyle.SetLabelFont(lhcbFont, "x")
lhcbStyle.SetLabelFont(lhcbFont, "y")
lhcbStyle.SetLabelFont(lhcbFont, "z")
lhcbStyle.SetLabelSize(lhcbTSize, "x")
lhcbStyle.SetLabelSize(lhcbTSize, "y")
lhcbStyle.SetLabelSize(lhcbTSize, "z")
lhcbStyle.SetTitleFont(lhcbFont)
lhcbStyle.SetTitleFont(lhcbFont, "x")
lhcbStyle.SetTitleFont(lhcbFont, "y")
lhcbStyle.SetTitleFont(lhcbFont, "z")
lhcbStyle.SetTitleSize(1.2 * lhcbTSize, "x")
lhcbStyle.SetTitleSize(1.2 * lhcbTSize, "y")
lhcbStyle.SetTitleSize(1.2 * lhcbTSize, "z")
# set the paper & margin sizes
lhcbStyle.SetPaperSize(20, 26)
lhcbStyle.SetPadTopMargin(0.05)
lhcbStyle.SetPadRightMargin(0.05) # increase for colz plots
lhcbStyle.SetPadBottomMargin(0.16)
lhcbStyle.SetPadLeftMargin(0.14)
# use medium bold lines and thick markers
lhcbStyle.SetLineWidth(lhcbWidth)
lhcbStyle.SetFrameLineWidth(lhcbWidth)
lhcbStyle.SetHistLineWidth(lhcbWidth)
lhcbStyle.SetFuncWidth(lhcbWidth)
lhcbStyle.SetGridWidth(lhcbWidth)
lhcbStyle.SetLineStyleString(2, "[12 12]")
# postscript dashes
lhcbStyle.SetMarkerStyle(20)
lhcbStyle.SetMarkerSize(1.0)
# label offsets
lhcbStyle.SetLabelOffset(0.010, "X")
lhcbStyle.SetLabelOffset(0.010, "Y")
# by default, do not display histogram decorations:
lhcbStyle.SetOptStat(0)
# lhcbStyle.SetOptStat("emr") # show only nent -e , mean - m , rms -r
# full opts at http:#root.cern.ch/root/html/TStyle.html#TStyle:SetOptStat
lhcbStyle.SetStatFormat("6.3g") # specified as c printf options
lhcbStyle.SetOptTitle(0)
lhcbStyle.SetOptFit(0)
# lhcbStyle.SetOptFit(1011) # order is probability, Chi2, errors, parameters
# titles
lhcbStyle.SetTitleOffset(0.95, "X")
lhcbStyle.SetTitleOffset(0.85, "Y")
lhcbStyle.SetTitleOffset(1.2, "Z")
lhcbStyle.SetTitleFillColor(0)
lhcbStyle.SetTitleStyle(0)
lhcbStyle.SetTitleBorderSize(0)
lhcbStyle.SetTitleFont(lhcbFont, "title")
lhcbStyle.SetTitleX(0.0)
lhcbStyle.SetTitleY(1.0)
lhcbStyle.SetTitleW(1.0)
lhcbStyle.SetTitleH(0.05)
# look of the statistics box:
lhcbStyle.SetStatBorderSize(0)
lhcbStyle.SetStatFont(lhcbFont)
lhcbStyle.SetStatFontSize(0.05)
lhcbStyle.SetStatX(0.9)
lhcbStyle.SetStatY(0.9)
lhcbStyle.SetStatW(0.25)
lhcbStyle.SetStatH(0.15)
# put tick marks on top and RHS of plots
lhcbStyle.SetPadTickX(1)
lhcbStyle.SetPadTickY(1)
# histogram divisions: only 5 in x to avoid label overlaps
lhcbStyle.SetNdivisions(505, "x")
lhcbStyle.SetNdivisions(510, "y")
gROOT.SetStyle("lhcbStyle")
return

156
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from __future__ import division
import ROOT
from ROOT import gStyle
# Some convenience function to easily iterate over the parts of the collections
# Needed if importing this script from another script in case TMultiGraphs are used
# ROOT.SetMemoryPolicy(ROOT.kMemoryStrict)
# Start a bit right of the Yaxis and above the Xaxis to not overlap with the ticks
start, stop = 0.28, 0.52
x_width, y_width = 0.4, 0.2
PLACES = [
(start, stop - y_width, start + x_width, stop), # top left opt
(start, start, start + x_width, start + y_width), # bottom left opt
(stop - x_width, stop - y_width, stop, stop), # top right opt
(stop - x_width, start, stop, start + y_width), # bottom right opt
(stop - x_width, 0.5 - y_width / 2, stop, 0.5 + y_width / 2), # right
(start, 0.5 - y_width / 2, start + x_width, 0.5 + y_width / 2),
] # left
# Needed if importing this script from another script in case TMultiGraphs are used
# ROOT.SetMemoryPolicy(ROOT.kMemoryStrict)
def transform_to_user(canvas, x1, y1, x2, y2):
"""
Transforms from Pad coordinates to User coordinates.
This can probably be replaced by using the built-in conversion commands.
"""
xstart = canvas.GetX1()
xlength = canvas.GetX2() - xstart
xlow = xlength * x1 + xstart
xhigh = xlength * x2 + xstart
if canvas.GetLogx():
xlow = 10**xlow
xhigh = 10**xhigh
ystart = canvas.GetY1()
ylength = canvas.GetY2() - ystart
ylow = ylength * y1 + ystart
yhigh = ylength * y2 + ystart
if canvas.GetLogy():
ylow = 10**ylow
yhigh = 10**yhigh
return xlow, ylow, xhigh, yhigh
def overlap_h(hist, x1, y1, x2, y2):
xlow = hist.FindFixBin(x1)
xhigh = hist.FindFixBin(x2)
for i in range(xlow, xhigh + 1):
val = hist.GetBinContent(i)
# Values
if y1 <= val <= y2:
return True
# Errors
if val + hist.GetBinErrorUp(i) > y1 and val - hist.GetBinErrorLow(i) < y2:
return True
return False
def overlap_rect(rect1, rect2):
"""Do the two rectangles overlap?"""
if rect1[0] > rect2[2] or rect1[2] < rect2[0]:
return False
if rect1[1] > rect2[3] or rect1[3] < rect2[1]:
return False
return True
def to_list(pointer):
"""turns pointer to array into list after checking if pointer is nullptr"""
if len(pointer) == 0:
return []
return list(pointer)
def overlap_g(graph, x1, y1, x2, y2):
x_values = to_list(graph.GetX())
y_values = to_list(graph.GetY())
x_err = to_list(graph.GetEX()) or [0] * len(x_values)
y_err = to_list(graph.GetEY()) or [0] * len(y_values)
for x, ex, y, ey in zip(x_values, x_err, y_values, y_err):
# Could maybe be less conservative
if overlap_rect((x1, y1, x2, y2), (x - ex, y - ey, x + ex, y + ey)):
# print "Overlap with graph", graph.GetName(), "at point", (x, y)
return True
return False
def place_legend(
canvas,
x1=None,
y1=None,
x2=None,
y2=None,
header="LHCb Simulation",
option="lpe",
):
gStyle.SetFillStyle(0)
gStyle.SetTextSize(0.06)
# If position is specified, use that
if all(x is not None for x in (x1, x2, y1, y2)):
return canvas.BuildLegend(x1, y1, x2, y2, header, option)
# Make sure all objects are correctly registered
canvas.Update()
# Build a list of objects to check for overlaps
objects = []
for x in canvas.GetListOfPrimitives():
if isinstance(x, ROOT.TH1) or isinstance(x, ROOT.TGraph):
objects.append(x)
elif isinstance(x, ROOT.THStack) or isinstance(x, ROOT.TMultiGraph):
objects.extend(x)
for place in PLACES:
place_user = canvas.PadtoU(*place)
# Make sure there are no overlaps
if any(obj.Overlap(*place_user) for obj in objects):
continue
return canvas.BuildLegend(
place[0],
place[1],
place[2],
place[3],
header,
option,
)
# As a fallback, use the default values, taken from TCanvas::BuildLegend
return canvas.BuildLegend(0.4, 0.37, 0.88, 0.68, header, option)
# Monkey patch ROOT objects to make it all work
ROOT.THStack.__iter__ = lambda self: iter(self.GetHists())
ROOT.TMultiGraph.__iter__ = lambda self: iter(self.GetListOfGraphs())
ROOT.TH1.Overlap = overlap_h
ROOT.TGraph.Overlap = overlap_g
ROOT.TPad.PadtoU = transform_to_user
ROOT.TPad.PlaceLegend = place_legend
def set_legend(legend, gr, title, colors, label):
legend.SetTextSize(0.05)
legend.SetFillColor(0)
legend.SetShadowColor(0)
legend.SetBorderSize(0)
legend.SetTextFont(132)
for idx, lab in enumerate(label):
legend.AddEntry(gr[lab], title[lab], "lep").SetTextColor(colors[idx])
return legend

48
scripts/utils/components.py Normal file
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# flake8: noqaq
import inspect
import re
import importlib
import warnings
from collections import OrderedDict
from functools import lru_cache
from html import escape as html_escape
# String that separates a name from its unique ID
_UNIQUE_SEPARATOR = "_"
_IDENTITY_LENGTH = 8
_IDENTITY_TABLE = {}
# If a property ends with this key, it is a pseudo-property which exists to
# hold the data dependencies of another property value
_DATA_DEPENDENCY_KEY_SUFFIX = "_PyConfDataDependencies"
_FLOW_GRAPH_NODE_COLOUR = "aliceblue"
_FLOW_GRAPH_INPUT_COLOUR = "deepskyblue1"
_FLOW_GRAPH_OUTPUT_COLOUR = "coral1"
def unique_name_ext_re():
return "(?:%s[1234567890abcdef]{%s})?" % (_UNIQUE_SEPARATOR, _IDENTITY_LENGTH)
def findRootObjByName(rootFile, name):
"""
Finds the object with given name in the given root file,
where name is a regular expression or a set of them separated by '/' in case directories are used
"""
curObj = rootFile
for n in name.split("/"):
matches = [
k.GetName() for k in curObj.GetListOfKeys() if re.fullmatch(n, k.GetName())
]
if len(matches) > 1:
raise Exception(
"Collision of names in Root : found several objects with name matching %s inside %s : %s"
% (n, curObj.GetName(), matches)
)
if len(matches) == 0:
raise Exception(
"Failed to find object with name %s inside %s" % (n, curObj.GetName())
)
curObj = curObj.Get(matches[0])
return curObj

16
setup.sh Executable file
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@ -0,0 +1,16 @@
#!/bin/bash
if [ ! -f "env/miniconda.sh" ]; then
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O env/miniconda.sh
fi
if [ ! -d "env/tuner_env" ]; then
bash env/miniconda.sh -b -p env/tuner_env &&
(
source env/tuner_env/bin/activate
conda install -y -c conda-forge mamba
conda env create -f env/environment.yaml -n tuner
conda activate tuner
conda env config vars set PATH="/cvmfs/sft.cern.ch/lcg/external/texlive/latest/bin/x86_64-linux:${PATH}"
)
fi

186
test/ghost_data_new_vars.ipynb Normal file
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408
test/ghost_data_test.ipynb Normal file
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