From 34ffa387bd20166d81dc6eb09f1a15eaf304dac3 Mon Sep 17 00:00:00 2001 From: t7phy Date: Sun, 24 Mar 2024 01:58:21 +0100 Subject: [PATCH 1/5] add y labels --- .../new_commondata/ATLAS_1JET_13TEV_DIF/metadata.yaml | 2 ++ .../new_commondata/ATLAS_2JET_13TEV_DIF/metadata.yaml | 3 ++- .../ATLAS_TTBAR_13TEV_LJ_DIF/metadata.yaml | 8 ++++++++ .../new_commondata/ATLAS_TTBAR_13TEV_TOT/metadata.yaml | 1 + .../new_commondata/ATLAS_TTBAR_7TEV_TOT/metadata.yaml | 1 + .../ATLAS_TTBAR_8TEV_2L_DIF/metadata.yaml | 4 ++++ .../ATLAS_TTBAR_8TEV_LJ_DIF/metadata.yaml | 8 ++++++++ .../new_commondata/ATLAS_TTBAR_8TEV_TOT/metadata.yaml | 1 + .../new_commondata/CMS_1JET_13TEV_DIF/metadata.yaml | 2 ++ .../CMS_TTBAR_13TEV_2L_DIF/metadata.yaml | 8 ++++++++ .../CMS_TTBAR_13TEV_LJ_DIF/metadata.yaml | 10 ++++++++++ .../new_commondata/CMS_TTBAR_13TEV_TOT/metadata.yaml | 1 + .../new_commondata/CMS_TTBAR_5TEV_TOT/metadata.yaml | 1 + .../new_commondata/CMS_TTBAR_7TEV_TOT/metadata.yaml | 1 + .../new_commondata/CMS_TTBAR_8TEV_2L_DIF/metadata.yaml | 3 +++ .../new_commondata/CMS_TTBAR_8TEV_LJ_DIF/metadata.yaml | 4 ++++ .../new_commondata/CMS_TTBAR_8TEV_TOT/metadata.yaml | 1 + .../H1_1JET_319GEV_290PB-1_DIF/metadata.yaml | 4 ++++ .../H1_1JET_319GEV_351PB-1_DIF/metadata.yaml | 2 ++ .../H1_2JET_319GEV_290PB-1_DIF/metadata.yaml | 2 ++ .../H1_2JET_319GEV_351PB-1_DIF/metadata.yaml | 2 ++ .../ZEUS_1JET_300GEV_38P6PB-1_DIF/metadata.yaml | 1 + .../ZEUS_1JET_319GEV_82PB-1_DIF/metadata.yaml | 1 + .../ZEUS_2JET_319GEV_374PB-1_DIF/metadata.yaml | 1 + 24 files changed, 71 insertions(+), 1 deletion(-) diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/metadata.yaml index 79636a0ce3..39154e9809 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/metadata.yaml @@ -38,6 +38,7 @@ implemented_observables: x_scale: log dataset_label: 'ATLAS Jet 13 TeV: $\frac{d^2\sigma}{dp_T d|y|}$' plot_x: pT + y_label: '$\frac{d^2\sigma}{dp_T d|y|}$ ($\frac{pb}{GeV}$)' figure_by: - y theory: @@ -70,6 +71,7 @@ implemented_observables: x_scale: log dataset_label: 'ATLAS Jet 13 TeV: $\frac{d^2\sigma}{dp_T d|y|}$' plot_x: pT + y_label: '$\frac{d^2\sigma}{dp_T d|y|}$ ($\frac{pb}{GeV}$)' figure_by: - y theory: diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/metadata.yaml index 4f9be5e49b..916e47fadf 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/metadata.yaml @@ -35,8 +35,9 @@ implemented_observables: plotting: kinematics_override: identity x_scale: log - dataset_label: 'ATLAS DiJet 13 TeV: $\frac{d^2\sigma}{dm_{jj} d|y|}$' + dataset_label: 'ATLAS DiJet 13 TeV: $\frac{d^2\sigma}{dm_{jj} d|y^*|}$' plot_x: m_jj + y_label: '$\frac{d^2\sigma}{dm_{jj} d|y^*|}$ ($\frac{pb}{GeV}$)' figure_by: - ystar theory: diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/metadata.yaml index 182f07e9cb..20e1dd72e9 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/metadata.yaml @@ -37,6 +37,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: m_ttBar + y_label: '$\frac{d\sigma}{dm_{t\bar{t}}}$ ($\frac{pb}{GeV}$)' theory: FK_tables: - - ATLAS_TTBAR_13TEV_LJ_DIF_MTTBAR @@ -62,6 +63,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: m_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dm_{t\bar{t}}}$ ($\frac{1}{GeV}$)' theory: FK_tables: - - ATLAS_TTBAR_13TEV_LJ_DIF_MTTBAR @@ -88,6 +90,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: pT_t + y_label: '$\frac{d\sigma}{dpT_t}$ ($\frac{pb}{GeV}$)' theory: FK_tables: - - ATLAS_TTBAR_13TEV_LJ_DIF_PTT @@ -113,6 +116,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: pT_t + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dpT_t}$ ($\frac{1}{GeV}$)' theory: FK_tables: - - ATLAS_TTBAR_13TEV_LJ_DIF_PTT @@ -139,6 +143,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_t + y_label: '$\frac{d\sigma}{d|y_{t}|}$ ($pb$)' theory: FK_tables: - - ATLAS_TTBAR_13TEV_LJ_DIF_YT @@ -164,6 +169,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_t + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{d|y_{t}|}$' theory: FK_tables: - - ATLAS_TTBAR_13TEV_LJ_DIF_YT @@ -190,6 +196,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{d\sigma}{d|y_{t\bar{t}}|}$ ($pb$)' theory: FK_tables: - - ATLAS_TTBAR_13TEV_LJ_DIF_YTTBAR @@ -215,6 +222,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{d|y_{t\bar{t}}}$' theory: FK_tables: - - ATLAS_TTBAR_13TEV_LJ_DIF_YTTBAR diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_TOT/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_TOT/metadata.yaml index 05541e1279..cdc6fdfdce 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_TOT/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_TOT/metadata.yaml @@ -46,6 +46,7 @@ implemented_observables: dataset_label: ATLAS 13 TeV $\sigma_{t\bar{t}}$ kinematics_override: identity plot_x: sqrts + y_label: '$\sigma_{t\bar{t}}$ ($pb$)' theory: FK_tables: - - ATLAS_TTBAR_13TEV_TOT_X-SEC diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_7TEV_TOT/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_7TEV_TOT/metadata.yaml index 3176653125..fdbe4a9c85 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_7TEV_TOT/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_7TEV_TOT/metadata.yaml @@ -46,6 +46,7 @@ implemented_observables: dataset_label: ATLAS 7 TeV $\sigma_{t\bar{t}}$ kinematics_override: identity plot_x: sqrts + y_label: '$\sigma_{t\bar{t}}$ ($pb$)' theory: FK_tables: - - ATLAS_TTBAR_7TEV_TOT_X-SEC diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/metadata.yaml index 25dcc010c8..f3bd2b2bf1 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/metadata.yaml @@ -48,6 +48,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: m_ttBar + y_label: '$\frac{d\sigma}{dm_{t\bar{t}}}$ ($\frac{pb}{GeV}$)' theory: FK_tables: - - ATLAS_TTBAR_8TEV_2L_DIF_MTTBAR @@ -89,6 +90,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: m_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dm_{t\bar{t}}}$ ($\frac{1}{GeV}$)' theory: FK_tables: - - ATLAS_TTBAR_8TEV_2L_DIF_MTTBAR @@ -131,6 +133,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{d\sigma}{d|y_{t\bar{t}}|}$ ($pb$)' theory: FK_tables: - - ATLAS_TTBAR_8TEV_2L_DIF_YTTBAR @@ -173,6 +176,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{d|y_{t\bar{t}}|}$' theory: FK_tables: - - ATLAS_TTBAR_8TEV_2L_DIF_YTTBAR diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/metadata.yaml index a5d123c0e6..50faded449 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/metadata.yaml @@ -49,6 +49,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: m_ttBar + y_label: '$\frac{d\sigma}{dm_{t\bar{t}}}$ ($\frac{pb}{GeV}$)' theory: FK_tables: - - ATLAS_TTBAR_8TEV_LJ_DIF_MTTBAR @@ -90,6 +91,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: m_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dm_{t\bar{t}}}$ ($\frac{1}{GeV}$)' theory: FK_tables: - - ATLAS_TTBAR_8TEV_LJ_DIF_MTTBAR @@ -132,6 +134,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: pT_t + y_label: '$\frac{d\sigma}{dpT_t}$ ($\frac{pb}{GeV}$)' theory: FK_tables: - - ATLAS_TTBAR_8TEV_LJ_DIF_PTT @@ -174,6 +177,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: pT_t + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dpT_t}$ ($\frac{1}{GeV}$)' theory: FK_tables: - - ATLAS_TTBAR_8TEV_LJ_DIF_PTT @@ -216,6 +220,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_t + y_label: '$\frac{d\sigma}{d|y_{t}|}$ ($pb$)' theory: FK_tables: - - ATLAS_TTBAR_8TEV_LJ_DIF_YT @@ -258,6 +263,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_t + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{d|y_{t}|}$' theory: FK_tables: - - ATLAS_TTBAR_8TEV_LJ_DIF_YT @@ -311,6 +317,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{d\sigma}{d|y_{t\bar{t}}|}$ ($pb$)' theory: FK_tables: - - ATLAS_TTBAR_8TEV_LJ_DIF_YTTBAR @@ -353,6 +360,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{d|y_{t\bar{t}}}$' theory: FK_tables: - - ATLAS_TTBAR_8TEV_LJ_DIF_YTTBAR diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_TOT/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_TOT/metadata.yaml index d29a7de91e..9537b8a7c0 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_TOT/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_TOT/metadata.yaml @@ -46,6 +46,7 @@ implemented_observables: dataset_label: ATLAS 8 TeV $\sigma_{t\bar{t}}$ kinematics_override: identity plot_x: sqrts + y_label: '$\sigma_{t\bar{t}}$ (pb)' theory: FK_tables: - - ATLAS_TTBAR_8TEV_TOT_X-SEC diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_13TEV_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_13TEV_DIF/metadata.yaml index cf7e31b51c..90ddfd65b9 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_13TEV_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_13TEV_DIF/metadata.yaml @@ -37,6 +37,7 @@ implemented_observables: dataset_label: 'CMS Jet 13 TeV R = 0.4: $\frac{d^2\sigma}{dp_T d|y|}$' plot_x: pT x_scale: log + y_label: '$\frac{d^2\sigma}{dp_T d|y|}$ ($\frac{pb}{GeV}$)' figure_by: - y theory: @@ -67,6 +68,7 @@ implemented_observables: dataset_label: 'CMS Jet 13 TeV R = 0.7: $\frac{d^2\sigma}{dp_T d|y|}$' plot_x: pT x_scale: log + y_label: '$\frac{d^2\sigma}{dp_T d|y|}$ ($\frac{pb}{GeV}$)' figure_by: - y theory: diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/metadata.yaml index 109f56c210..cb918f99b2 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/metadata.yaml @@ -49,6 +49,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: pT_t + y_label: '$\frac{d\sigma}{dpT_{t}}$ ($\frac{pb}{GeV}$)' theory: FK_tables: - - CMS_TTBAR_13TEV_2L_DIF_PTT @@ -91,6 +92,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: pT_t + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dpT_{t}}$ ($\frac{1}{GeV}$)' theory: FK_tables: - - CMS_TTBAR_13TEV_2L_DIF_PTT @@ -133,6 +135,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: m_ttBar + y_label: '$\frac{d\sigma}{dm_{t\bar{t}}}$ ($\frac{pb}{GeV}$)' theory: FK_tables: - - CMS_TTBAR_13TEV_2L_DIF_MTTBAR @@ -175,6 +178,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: m_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dm_{t\bar{t}}}$ ($\frac{1}{GeV}$)' theory: FK_tables: - - CMS_TTBAR_13TEV_2L_DIF_MTTBAR @@ -217,6 +221,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_t + y_label: '$\frac{d\sigma}{dy_{t}}$ ($pb$)' theory: FK_tables: - - CMS_TTBAR_13TEV_2L_DIF_YT @@ -265,6 +270,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_t + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dy_{t}}$' theory: FK_tables: - - CMS_TTBAR_13TEV_2L_DIF_YT @@ -307,6 +313,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{d\sigma}{dy_{t\bar{t}}}$ ($pb$)' theory: FK_tables: - - CMS_TTBAR_13TEV_2L_DIF_YTTBAR @@ -348,6 +355,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dy_{t\bar{t}}}$' theory: FK_tables: - - CMS_TTBAR_13TEV_2L_DIF_YTTBAR diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/metadata.yaml index 77eb024b63..8ccf38a40c 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/metadata.yaml @@ -37,6 +37,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: m_ttBar + y_label: '$\frac{d\sigma}{dm_{t\bar{t}}}$ ($\frac{pb}{GeV}$)' theory: FK_tables: - - CMS_TTBAR_13TEV_LJ_DIF_MTTBAR @@ -63,6 +64,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: m_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dm_{t\bar{t}}}$ ($\frac{1}{GeV}$)' theory: FK_tables: - - CMS_TTBAR_13TEV_LJ_DIF_MTTBAR @@ -89,6 +91,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{d\sigma}{d|y_{t\bar{t}}|}$ ($pb$)' theory: FK_tables: - - CMS_TTBAR_13TEV_LJ_DIF_YTTBAR @@ -115,6 +118,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{d|y_{t\bar{t}}|}$' theory: FK_tables: - - CMS_TTBAR_13TEV_LJ_DIF_YTTBAR @@ -142,6 +146,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{d^2\sigma}{dm_{t\bar{t}}d|y_{t\bar{t}}|}$ ($\frac{pb}{GeV}$)' figure_by: - m_ttBar theory: @@ -171,6 +176,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_ttBar + y_label: '$\frac{1}{\sigma}\frac{d^2\sigma}{dm_{t\bar{t}}d|y_{t\bar{t}}|}$ ($\frac{1}{GeV}$)' figure_by: - m_ttBar theory: @@ -199,6 +205,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: pT_t + y_label: '$\frac{d\sigma}{dpT_t}$ ($\frac{pb}{GeV}$)' theory: FK_tables: - - CMS_TTBAR_13TEV_LJ_DIF_PTT @@ -225,6 +232,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: pT_t + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dpT_t}$ ($\frac{1}{GeV}$)' theory: FK_tables: - - CMS_TTBAR_13TEV_LJ_DIF_PTT @@ -251,6 +259,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_t + y_label: '$\frac{d\sigma}{d|y_t|}$ ($pb$)' theory: FK_tables: - - CMS_TTBAR_13TEV_LJ_DIF_YT @@ -277,6 +286,7 @@ implemented_observables: kinematics_override: identity x_scale: log plot_x: y_t + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{d|y_t|}$' theory: FK_tables: - - CMS_TTBAR_13TEV_LJ_DIF_YT diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_TOT/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_TOT/metadata.yaml index 4fffb1aa05..5df9eaa1f8 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_TOT/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_TOT/metadata.yaml @@ -46,6 +46,7 @@ implemented_observables: dataset_label: CMS 13 TeV $\sigma_{t\bar{t}}$ kinematics_override: identity plot_x: sqrts + y_label: '$\sigma_{t\bar{t}}$ ($pb$)' theory: FK_tables: - - CMS_TTBAR_13TEV_TOT_X-SEC diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_5TEV_TOT/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_5TEV_TOT/metadata.yaml index 31c5a992a6..3346c8d6e2 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_5TEV_TOT/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_5TEV_TOT/metadata.yaml @@ -46,6 +46,7 @@ implemented_observables: kinematics_override: identity dataset_label: CMS 5 TeV $\sigma_{t\bar{t}}$ plot_x: sqrts + y_label: '$\sigma_{t\bar{t}}$ ($pb$)' theory: FK_tables: - - CMS_TTBAR_5TEV_TOT_X-SEC diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_7TEV_TOT/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_7TEV_TOT/metadata.yaml index 6aeb39f436..b1f27523db 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_7TEV_TOT/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_7TEV_TOT/metadata.yaml @@ -46,6 +46,7 @@ implemented_observables: dataset_label: CMS 7 TeV $\sigma_{t\bar{t}}$ kinematics_override: identity plot_x: sqrts + y_label: '$\sigma_{t\bar{t}}$ ($pb$)' theory: FK_tables: - - CMS_TTBAR_7TEV_TOT_X-SEC diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/metadata.yaml index a25049f1c2..8ccc833e2c 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/metadata.yaml @@ -52,6 +52,7 @@ implemented_observables: dataset_label: 'CMS TTB 8 TeV: $\frac{1}{\sigma}\frac{d^2\sigma}{d|y_{t}|dpT_{t}}$' kinematics_override: identity plot_x: pT_t + y_label: '$\frac{1}{\sigma}\frac{d^2\sigma}{d|y_{t}|dpT_{t}}$ ($\frac{1}{GeV}$)' figure_by: - y_t theory: @@ -99,6 +100,7 @@ implemented_observables: dataset_label: 'CMS TTB 8 TeV: $\frac{1}{\sigma}\frac{d^2\sigma}{d|y_{t}|dm_{t\bar{t}}}$' kinematics_override: identity plot_x: y_t + y_label: '$\frac{1}{\sigma}\frac{d^2\sigma}{d|y_{t}|dm_{t\bar{t}}}$ ($\frac{1}{GeV}$)' figure_by: - m_ttBar theory: @@ -157,6 +159,7 @@ implemented_observables: dataset_label: 'CMS TTB 8 TeV: $\frac{1}{\sigma}\frac{d^2\sigma}{dm_{t\bar{t}}d|y_{t\bar{t}}|}$' kinematics_override: identity plot_x: y_ttBar + y_label: '$\frac{1}{\sigma}\frac{d^2\sigma}{dm_{t\bar{t}}d|y_{t\bar{t}}|}$ ($\frac{1}{GeV}$)' figure_by: - m_ttBar theory: diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/metadata.yaml index a5a5cadaca..f74658fa8a 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/metadata.yaml @@ -48,6 +48,7 @@ implemented_observables: kinematics_override: identity dataset_label: 'CMS 8 TeV $t\bar{t}$: $\frac{1}{\sigma}\frac{d\sigma}{dpT_t}$' plot_x: pT_t + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dpT_t}$ ($\frac{1}{GeV}$)' theory: FK_tables: - - CMS_TTBAR_8TEV_LJ_DIF_PTT @@ -89,6 +90,7 @@ implemented_observables: kinematics_override: identity dataset_label: 'CMS 8 TeV $t\bar{t}$: $\frac{1}{\sigma}\frac{d\sigma}{dy_{t}}$' plot_x: y_t + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dy_{t}}$' theory: FK_tables: - - CMS_TTBAR_8TEV_LJ_DIF_YT @@ -130,6 +132,7 @@ implemented_observables: kinematics_override: identity dataset_label: 'CMS 8 TeV $t\bar{t}$: $\frac{1}{\sigma}\frac{d\sigma}{dy_{t\bar{t}}}$' plot_x: y_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dy_{t\bar{t}}}$' theory: FK_tables: - - CMS_TTBAR_8TEV_LJ_DIF_YTTBAR @@ -179,6 +182,7 @@ implemented_observables: kinematics_override: identity dataset_label: 'CMS 8 TeV $t\bar{t}$: $\frac{1}{\sigma}\frac{d\sigma}{dm_{t\bar{t}}}$' plot_x: m_ttBar + y_label: '$\frac{1}{\sigma}\frac{d\sigma}{dm_{t\bar{t}}}$ ($\frac{1}{GeV}$)' theory: FK_tables: - - CMS_TTBAR_8TEV_LJ_DIF_MTTBAR diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_TOT/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_TOT/metadata.yaml index bcdceed7d3..2ea7e80275 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_TOT/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_TOT/metadata.yaml @@ -46,6 +46,7 @@ implemented_observables: dataset_label: CMS 8 TeV $\sigma_{t\bar{t}}$ kinematics_override: identity plot_x: sqrts + y_label: '$\sigma_{t\bar{t}}$ ($pb$)' theory: FK_tables: - - CMS_TTBAR_8TEV_TOT_X-SEC diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/metadata.yaml index a9d036b70b..d3e518ce3d 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/metadata.yaml @@ -36,6 +36,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$H1\ Jet\ 290\ pb^{-1}:\ \frac{d^2\sigma}{dq^{2}dpT}$' plot_x: pT + y_label: '$\frac{d^2\sigma}{dq^{2}dpT}$ ($pb/GeV^3$)' x_scale: log figure_by: - Q2 @@ -70,6 +71,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$H1\ Jet\ 290\ pb^{-1}:\ \frac{1}{\sigma}\frac{d^2\sigma}{dq^{2}dpT}$' plot_x: pT + y_label: '$\frac{1}{\sigma}\frac{d^2\sigma}{dq^{2}dpT}$ ($1/GeV^3$)' x_scale: log figure_by: - Q2 @@ -93,6 +95,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$H1\ Jet\ 290\ pb^{-1}\ high\ Q2:\ \frac{d^2\sigma}{dq^{2}dpT}$' plot_x: pT + y_label: '$\frac{d^2\sigma}{dq^{2}dpT}$ ($pb/GeV^3$)' x_scale: log figure_by: - Q2 @@ -116,6 +119,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$H1\ Jet\ 290\ pb^{-1}\ high\ Q2:\ \frac{1}{\sigma}\frac{d^2\sigma}{dq^{2}dpT}$' plot_x: pT + y_label: '$\frac{1}{\sigma}\frac{d^2\sigma}{dq^{2}dpT}$ ($1/GeV^3$)' x_scale: log figure_by: - Q2 diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/metadata.yaml index c9f0ae07c2..d104756007 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/metadata.yaml @@ -36,6 +36,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$H1\ Jet\ 351\ pb^{-1}:\ \frac{d^2\sigma}{dq^{2}dpT}$' plot_x: pT + y_label: '$\frac{d^2\sigma}{dq^{2}dpT}$ ($pb/GeV^3$)' x_scale: log figure_by: - Q2 @@ -68,6 +69,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$H1\ Jet\ 351\ pb^{-1}:\ \frac{1}{\sigma}\frac{d^2\sigma}{dq^{2}dpT}$' plot_x: pT + y_label: '$\frac{1}{\sigma}\frac{d^2\sigma}{dq^{2}dpT}$ ($1/GeV^3$)' x_scale: log figure_by: - Q2 diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/metadata.yaml index 5c72a7473d..d4524615b6 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/metadata.yaml @@ -37,6 +37,7 @@ implemented_observables: dataset_label: '$H1\ DiJet\ 290\ pb^{-1}:\ \frac{d^2\sigma}{dq^{2}d\langle pT \rangle}$' plot_x: pT x_scale: log + y_label: '$\frac{d^2\sigma}{dq^{2}d\langle pT \rangle}$ ($pb/GeV^3$)' figure_by: - Q2 theory: @@ -70,6 +71,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$H1\ DiJet\ 290\ pb^{-1}:\ \frac{1}{\sigma}\frac{d^2\sigma}{dq^{2}d\langle pT \rangle}$' plot_x: pT + y_label: '$\frac{1}{\sigma}\frac{d^2\sigma}{dq^{2}d\langle pT \rangle}$ ($1/GeV^3$)' x_scale: log figure_by: - Q2 diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/metadata.yaml index cbddbcaf58..aa3f2ec2c4 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/metadata.yaml @@ -36,6 +36,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$H1\ DiJet\ 351\ pb^{-1}:\ \frac{d^2\sigma}{dq^{2}dpT}$' plot_x: pT + y_label: '$\frac{d^2\sigma}{dq^{2}dpT}$ ($pb/GeV^3$)' x_scale: log figure_by: - Q2 @@ -68,6 +69,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$H1\ Jet\ 351\ pb^{-1}:\ \frac{1}{\sigma}\frac{d^2\sigma}{dq^{2}dpT}$' plot_x: pT + y_label: '$\frac{1}{\sigma}\frac{d^2\sigma}{dq^{2}dpT}$ ($1/GeV^3$)' x_scale: log figure_by: - Q2 diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/metadata.yaml index 0b237b1beb..ebe5804927 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/metadata.yaml @@ -36,6 +36,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$ZEUS\ Jet\ 38.6\ pb^{-1}:\ \frac{d^2\sigma}{dE_{T}dq^2}$' plot_x: ET + y_label: '$\frac{d^2\sigma}{dE_{T}dq^2}$ ($pb/GeV^3$)' x_scale: log figure_by: - Q2 diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/metadata.yaml index 33fbfc7e53..89bae76b2c 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/metadata.yaml @@ -36,6 +36,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$ZEUS\ Jet\ 82\ pb^{-1}:\ \frac{d^2\sigma}{dE_Tdq^2}$' plot_x: ET + y_label: '$\frac{d^2\sigma}{dE_Tdq^2}$ ($pb/GeV^3$)' x_scale: log figure_by: - Q2 diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/metadata.yaml b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/metadata.yaml index 34c49cb16a..05bde3f06a 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/metadata.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/metadata.yaml @@ -36,6 +36,7 @@ implemented_observables: kinematics_override: identity dataset_label: '$ZEUS\ DiJet\ 374\ pb^{-1}:\ \frac{d^2\sigma}{dE_Tdq^2}$' plot_x: ET + y_label: '$\frac{d^2\sigma}{dE_Tdq^2}$ ($pb/GeV^3$)' x_scale: log figure_by: - Q2 From a527a02b32a25ca41891fe15893f00189917d365 Mon Sep 17 00:00:00 2001 From: t7phy Date: Sun, 24 Mar 2024 15:59:24 +0100 Subject: [PATCH 2/5] remove commas --- .../ATLAS_TTBAR_8TEV_LJ_DIF/filter.py | 10 + .../uncertainties_dSig_dmttBar.yaml | 880 ++++++++-------- .../uncertainties_dSig_dmttBar_norm.yaml | 880 ++++++++-------- .../uncertainties_dSig_dpTt.yaml | 990 +++++++++--------- .../uncertainties_dSig_dpTt_norm.yaml | 990 +++++++++--------- .../uncertainties_dSig_dyt.yaml | 660 ++++++------ .../uncertainties_dSig_dyt_norm.yaml | 660 ++++++------ .../uncertainties_dSig_dyttBar.yaml | 660 ++++++------ .../uncertainties_dSig_dyttBar_norm.yaml | 660 ++++++------ 9 files changed, 3200 insertions(+), 3190 deletions(-) diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py index 8d9e67f405..e0f9b2b344 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py @@ -1,5 +1,7 @@ import artunc import yaml +import re +from pathlib import Path # use #1693 from validphys.commondata_utils import percentage_to_absolute as pta @@ -444,4 +446,12 @@ def processData(): with open('uncertainties_dSig_dyttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_norm_yaml, file, sort_keys=False) +def remove_commas(): + pattern = "uncertainties*.yaml" + reg = re.compile(fr'({"sys,"})') + for file in Path(".").glob(pattern): + new_text = reg.sub("syst_", file.read_text()) + file.write_text(new_text) + processData() +remove_commas() \ No newline at end of file diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dmttBar.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dmttBar.yaml index 3ced30bf62..8a3b1d8f23 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dmttBar.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dmttBar.yaml @@ -99,223 +99,223 @@ definitions: definition: artificial uncertainty 25 treatment: ADD type: ATLAS8TEVTTB151104716unc25 - sys,singletop-xsec: + syst_singletop-xsec: definition: '' treatment: MULT type: CORR - sys,wjet-scale: + syst_wjet-scale: definition: '' treatment: MULT type: CORR - sys,laltrealcr-mujet-fake: + syst_laltrealcr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,eta-jes: + syst_eta-jes: definition: '' treatment: MULT type: CORR - sys,statNP3-jes: + syst_statNP3-jes: definition: '' treatment: MULT type: CORR - sys,laltrealcr-ejet-fake: + syst_laltrealcr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,pileoffmu-jes: + syst_pileoffmu-jes: definition: '' treatment: MULT type: CORR - sys,lstat-ejet-fake: + syst_lstat-ejet-fake: definition: '' treatment: MULT type: CORR - sys,lstat-mujet-fake: + syst_lstat-mujet-fake: definition: '' treatment: MULT type: CORR - sys,etmsoft-scale: + syst_etmsoft-scale: definition: '' treatment: MULT type: CORR - sys,hardscat-model: + syst_hardscat-model: definition: '' treatment: MULT type: CORR - sys,statNP2-jes: + syst_statNP2-jes: definition: '' treatment: MULT type: CORR - sys,elen-scale: + syst_elen-scale: definition: '' treatment: MULT type: CORR - sys,punch-jes: + syst_punch-jes: definition: '' treatment: MULT type: CORR - sys,pileoffnpv-jes: + syst_pileoffnpv-jes: definition: '' treatment: MULT type: CORR - sys,lrec-eff: + syst_lrec-eff: definition: '' treatment: MULT type: CORR - sys,pileoffpt-jes: + syst_pileoffpt-jes: definition: '' treatment: MULT type: CORR - sys,jeten-res: + syst_jeten-res: definition: '' treatment: MULT type: CORR - sys,lighttag-eff: + syst_lighttag-eff: definition: '' treatment: MULT type: CORR - sys,laltfakecr-ejet-fake: + syst_laltfakecr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,laltpar-mujet-fake: + syst_laltpar-mujet-fake: definition: '' treatment: MULT type: CORR - sys,jetrec-eff: + syst_jetrec-eff: definition: '' treatment: MULT type: CORR - sys,c/tautag-eff: + syst_c/tautag-eff: definition: '' treatment: MULT type: CORR - sys,dibos-xsec: + syst_dibos-xsec: definition: '' treatment: MULT type: CORR - sys,elen-res: + syst_elen-res: definition: '' treatment: MULT type: CORR - sys,flavcomp-jes: + syst_flavcomp-jes: definition: '' treatment: MULT type: CORR - sys,detNP2-jes: + syst_detNP2-jes: definition: '' treatment: MULT type: CORR - sys,detNP3-jes: + syst_detNP3-jes: definition: '' treatment: MULT type: CORR - sys,jetvxfrac: + syst_jetvxfrac: definition: '' treatment: MULT type: CORR - sys,ltrig-eff: + syst_ltrig-eff: definition: '' treatment: MULT type: CORR - sys,btag-jes: + syst_btag-jes: definition: '' treatment: MULT type: CORR - sys,mup-scale: + syst_mup-scale: definition: '' treatment: MULT type: CORR - sys,singlephpt-jes: + syst_singlephpt-jes: definition: '' treatment: MULT type: CORR - sys,etmsoft-res: + syst_etmsoft-res: definition: '' treatment: MULT type: CORR - sys,detNP1-jes: + syst_detNP1-jes: definition: '' treatment: MULT type: CORR - sys,laltpar-ejet-fake: + syst_laltpar-ejet-fake: definition: '' treatment: MULT type: CORR - sys,statNP1-jes: + syst_statNP1-jes: definition: '' treatment: MULT type: CORR - sys,muid-res: + syst_muid-res: definition: '' treatment: MULT type: CORR - sys,pdf: + syst_pdf: definition: '' treatment: MULT type: CORR - sys,isr-fsr: + syst_isr-fsr: definition: '' treatment: MULT type: CORR - sys,zjet-xsec: + syst_zjet-xsec: definition: '' treatment: MULT type: CORR - sys,ps-model: + syst_ps-model: definition: '' treatment: MULT type: CORR - sys,flavres-jes: + syst_flavres-jes: definition: '' treatment: MULT type: CORR - sys,laltfakecr-mujet-fake: + syst_laltfakecr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,mums-res: + syst_mums-res: definition: '' treatment: MULT type: CORR - sys,mod-NP2-jes: + syst_mod-NP2-jes: definition: '' treatment: MULT type: CORR - sys,lid-eff: + syst_lid-eff: definition: '' treatment: MULT type: CORR - sys,mixNP2-jes: + syst_mixNP2-jes: definition: '' treatment: MULT type: CORR - sys,mixNP1-jes: + syst_mixNP1-jes: definition: '' treatment: MULT type: CORR - sys,btag-eff: + syst_btag-eff: definition: '' treatment: MULT type: CORR - sys,pileoffrho-jes: + syst_pileoffrho-jes: definition: '' treatment: MULT type: CORR - sys,modNP4-jes: + syst_modNP4-jes: definition: '' treatment: MULT type: CORR - sys,mcstat: + syst_mcstat: definition: '' treatment: MULT type: CORR - sys,modNP3-jes: + syst_modNP3-jes: definition: '' treatment: MULT type: CORR - sys,mod-NP1-jes: + syst_mod-NP1-jes: definition: '' treatment: MULT type: CORR @@ -349,61 +349,61 @@ bins: ArtUnc_23: -2.818960235063438e-07 ArtUnc_24: -1.3402002488404512e-05 ArtUnc_25: 3.066060133088487e-08 - sys,singletop-xsec: 0.001985853202469115 - sys,wjet-scale: 0.0028016694 - sys,laltrealcr-mujet-fake: 0.0013780568999999998 - sys,eta-jes: 0.0031121719164628947 - sys,statNP3-jes: 0.006144570143307182 - sys,laltrealcr-ejet-fake: 0.0005125005 - sys,pileoffmu-jes: 0.0012212703590408595 - sys,lstat-ejet-fake: 0.0031001456063758314 - sys,lstat-mujet-fake: 9.322216418357815e-05 - sys,etmsoft-scale: 0.0010458427552118565 - sys,hardscat-model: 0.0281419719 - sys,statNP2-jes: 0.0020540473951530134 - sys,elen-scale: 0.0014325022526560324 - sys,punch-jes: 0.00016266602845966334 - sys,pileoffnpv-jes: 0.00540447406685704 - sys,lrec-eff: 0.0025169469000000002 - sys,pileoffpt-jes: 0.00047435144482603557 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.004612532620655021 - sys,laltfakecr-ejet-fake: 0.0028016694 - sys,laltpar-mujet-fake: 0.0015375015 - sys,jetrec-eff: 0.0011161122 - sys,c/tautag-eff: 0.010301263197843632 - sys,dibos-xsec: 0.0008427786 - sys,elen-res: 0.000532606142942672 - sys,flavcomp-jes: 0.017935955525707002 - sys,detNP2-jes: 0.0022949905178165047 - sys,detNP3-jes: 0.0008976302982968657 - sys,jetvxfrac: 0.012011154239860187 - sys,ltrig-eff: 0.0145322364 - sys,btag-jes: 0.006439675226820709 - sys,mup-scale: 5.917846032696356e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0009271292117890958 - sys,detNP1-jes: 0.009385130828112494 - sys,laltpar-ejet-fake: 0.0029269473 - sys,statNP1-jes: 0.010802374651818243 - sys,muid-res: 3.41667e-05 - sys,pdf: 0.0007744452000000001 - sys,isr-fsr: 0.08135707328286182 - sys,zjet-xsec: 0.0101588988 - sys,ps-model: 0.0482319915 - sys,flavres-jes: 0.0069108854953368 - sys,laltfakecr-mujet-fake: 0.0030977808000000003 - sys,mums-res: 1.13889e-05 - sys,mod-NP2-jes: 0.001687047486704576 - sys,lid-eff: 0.0145322364 - sys,mixNP2-jes: 0.0043571548476910105 - sys,mixNP1-jes: 0.0058425279005426154 - sys,btag-eff: 0.04697782613980783 - sys,pileoffrho-jes: 0.009585806155941368 - sys,modNP4-jes: 0.0014083147367417944 - sys,mcstat: 0.0020841686999999998 - sys,modNP3-jes: 0.007626097499542145 - sys,mod-NP1-jes: 0.009206055482993553 + syst_singletop-xsec: 0.001985853202469115 + syst_wjet-scale: 0.0028016694 + syst_laltrealcr-mujet-fake: 0.0013780568999999998 + syst_eta-jes: 0.0031121719164628947 + syst_statNP3-jes: 0.006144570143307182 + syst_laltrealcr-ejet-fake: 0.0005125005 + syst_pileoffmu-jes: 0.0012212703590408595 + syst_lstat-ejet-fake: 0.0031001456063758314 + syst_lstat-mujet-fake: 9.322216418357815e-05 + syst_etmsoft-scale: 0.0010458427552118565 + syst_hardscat-model: 0.0281419719 + syst_statNP2-jes: 0.0020540473951530134 + syst_elen-scale: 0.0014325022526560324 + syst_punch-jes: 0.00016266602845966334 + syst_pileoffnpv-jes: 0.00540447406685704 + syst_lrec-eff: 0.0025169469000000002 + syst_pileoffpt-jes: 0.00047435144482603557 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.004612532620655021 + syst_laltfakecr-ejet-fake: 0.0028016694 + syst_laltpar-mujet-fake: 0.0015375015 + syst_jetrec-eff: 0.0011161122 + syst_c/tautag-eff: 0.010301263197843632 + syst_dibos-xsec: 0.0008427786 + syst_elen-res: 0.000532606142942672 + syst_flavcomp-jes: 0.017935955525707002 + syst_detNP2-jes: 0.0022949905178165047 + syst_detNP3-jes: 0.0008976302982968657 + syst_jetvxfrac: 0.012011154239860187 + syst_ltrig-eff: 0.0145322364 + syst_btag-jes: 0.006439675226820709 + syst_mup-scale: 5.917846032696356e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0009271292117890958 + syst_detNP1-jes: 0.009385130828112494 + syst_laltpar-ejet-fake: 0.0029269473 + syst_statNP1-jes: 0.010802374651818243 + syst_muid-res: 3.41667e-05 + syst_pdf: 0.0007744452000000001 + syst_isr-fsr: 0.08135707328286182 + syst_zjet-xsec: 0.0101588988 + syst_ps-model: 0.0482319915 + syst_flavres-jes: 0.0069108854953368 + syst_laltfakecr-mujet-fake: 0.0030977808000000003 + syst_mums-res: 1.13889e-05 + syst_mod-NP2-jes: 0.001687047486704576 + syst_lid-eff: 0.0145322364 + syst_mixNP2-jes: 0.0043571548476910105 + syst_mixNP1-jes: 0.0058425279005426154 + syst_btag-eff: 0.04697782613980783 + syst_pileoffrho-jes: 0.009585806155941368 + syst_modNP4-jes: 0.0014083147367417944 + syst_mcstat: 0.0020841686999999998 + syst_modNP3-jes: 0.007626097499542145 + syst_mod-NP1-jes: 0.009206055482993553 lumi: 0.031888919999999994 - ArtUnc_1: -0.0012905946416980907 ArtUnc_2: 0.00195850666049491 @@ -430,61 +430,61 @@ bins: ArtUnc_23: -5.200845697995414e-07 ArtUnc_24: -2.6503562639675078e-05 ArtUnc_25: 1.4456399499308546e-07 - sys,singletop-xsec: 0.0021121989370325132 - sys,wjet-scale: 0.0024530880000000004 - sys,laltrealcr-mujet-fake: 3.34512e-05 - sys,eta-jes: 0.001331656097829706 - sys,statNP3-jes: 0.0012773293012785076 - sys,laltrealcr-ejet-fake: 0.0006913247999999999 - sys,pileoffmu-jes: 0.001717234003668364 - sys,lstat-ejet-fake: 0.0029721202431139426 - sys,lstat-mujet-fake: 0.0001708414495447753 - sys,etmsoft-scale: 0.00038432502800374584 - sys,hardscat-model: 0.08273596800000001 - sys,statNP2-jes: 0.0014607875150774257 - sys,elen-scale: 0.0006276744280532703 - sys,punch-jes: 0.00015690003566500553 - sys,pileoffnpv-jes: 0.007784001519103983 - sys,lrec-eff: 0.0025534416000000007 - sys,pileoffpt-jes: 0.00018490846531189425 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.004493611200000001 - sys,laltfakecr-ejet-fake: 0.0029994576000000003 - sys,laltpar-mujet-fake: 0.0017283119999999999 - sys,jetrec-eff: 0.0008139792 - sys,c/tautag-eff: 0.009806779969527628 - sys,dibos-xsec: 0.0007582272000000002 - sys,elen-res: 0.0004927354063972266 - sys,flavcomp-jes: 0.023728976015436023 - sys,detNP2-jes: 0.0029102971215255124 - sys,detNP3-jes: 0.00011094507918713656 - sys,jetvxfrac: 0.010249763736563 - sys,ltrig-eff: 0.0140829552 - sys,btag-jes: 0.0032870567873569574 - sys,mup-scale: 0.00016340187416110012 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.00042488730514375216 - sys,detNP1-jes: 0.003017017818720785 - sys,laltpar-ejet-fake: 0.0026537951999999997 - sys,statNP1-jes: 0.012450742300569027 - sys,muid-res: 5.5752000000000004e-05 - sys,pdf: 0.0027876000000000003 - sys,isr-fsr: 0.09087099513130321 - sys,zjet-xsec: 0.009031824 - sys,ps-model: 0.08736338400000002 - sys,flavres-jes: 0.012809989888524198 - sys,laltfakecr-mujet-fake: 0.0022746816 - sys,mums-res: 6.69024e-05 - sys,mod-NP2-jes: 0.0016617089126358564 - sys,lid-eff: 0.014462068799999998 - sys,mixNP2-jes: 0.005190996237702541 - sys,mixNP1-jes: 0.00011587835594829607 - sys,btag-eff: 0.04515762602817681 - sys,pileoffrho-jes: 0.01951030307033234 - sys,modNP4-jes: 0.0005911815474706226 - sys,mcstat: 0.0013157472 - sys,modNP3-jes: 0.0035737814780818595 - sys,mod-NP1-jes: 0.025799139055823234 + syst_singletop-xsec: 0.0021121989370325132 + syst_wjet-scale: 0.0024530880000000004 + syst_laltrealcr-mujet-fake: 3.34512e-05 + syst_eta-jes: 0.001331656097829706 + syst_statNP3-jes: 0.0012773293012785076 + syst_laltrealcr-ejet-fake: 0.0006913247999999999 + syst_pileoffmu-jes: 0.001717234003668364 + syst_lstat-ejet-fake: 0.0029721202431139426 + syst_lstat-mujet-fake: 0.0001708414495447753 + syst_etmsoft-scale: 0.00038432502800374584 + syst_hardscat-model: 0.08273596800000001 + syst_statNP2-jes: 0.0014607875150774257 + syst_elen-scale: 0.0006276744280532703 + syst_punch-jes: 0.00015690003566500553 + syst_pileoffnpv-jes: 0.007784001519103983 + syst_lrec-eff: 0.0025534416000000007 + syst_pileoffpt-jes: 0.00018490846531189425 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.004493611200000001 + syst_laltfakecr-ejet-fake: 0.0029994576000000003 + syst_laltpar-mujet-fake: 0.0017283119999999999 + syst_jetrec-eff: 0.0008139792 + syst_c/tautag-eff: 0.009806779969527628 + syst_dibos-xsec: 0.0007582272000000002 + syst_elen-res: 0.0004927354063972266 + syst_flavcomp-jes: 0.023728976015436023 + syst_detNP2-jes: 0.0029102971215255124 + syst_detNP3-jes: 0.00011094507918713656 + syst_jetvxfrac: 0.010249763736563 + syst_ltrig-eff: 0.0140829552 + syst_btag-jes: 0.0032870567873569574 + syst_mup-scale: 0.00016340187416110012 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.00042488730514375216 + syst_detNP1-jes: 0.003017017818720785 + syst_laltpar-ejet-fake: 0.0026537951999999997 + syst_statNP1-jes: 0.012450742300569027 + syst_muid-res: 5.5752000000000004e-05 + syst_pdf: 0.0027876000000000003 + syst_isr-fsr: 0.09087099513130321 + syst_zjet-xsec: 0.009031824 + syst_ps-model: 0.08736338400000002 + syst_flavres-jes: 0.012809989888524198 + syst_laltfakecr-mujet-fake: 0.0022746816 + syst_mums-res: 6.69024e-05 + syst_mod-NP2-jes: 0.0016617089126358564 + syst_lid-eff: 0.014462068799999998 + syst_mixNP2-jes: 0.005190996237702541 + syst_mixNP1-jes: 0.00011587835594829607 + syst_btag-eff: 0.04515762602817681 + syst_pileoffrho-jes: 0.01951030307033234 + syst_modNP4-jes: 0.0005911815474706226 + syst_mcstat: 0.0013157472 + syst_modNP3-jes: 0.0035737814780818595 + syst_mod-NP1-jes: 0.025799139055823234 lumi: 0.03122112 - ArtUnc_1: -0.0008405524694533638 ArtUnc_2: 0.001262207909758043 @@ -511,61 +511,61 @@ bins: ArtUnc_23: -7.906891664589237e-07 ArtUnc_24: -3.496476250249044e-05 ArtUnc_25: 1.0521007822504992e-07 - sys,singletop-xsec: 0.0015164392735770606 - sys,wjet-scale: 0.0019121207399999999 - sys,laltrealcr-mujet-fake: 0.00152562825 - sys,eta-jes: 0.003974461051988882 - sys,statNP3-jes: 0.002105634478417693 - sys,laltrealcr-ejet-fake: 0.00049498161 - sys,pileoffmu-jes: 0.0015112611323920603 - sys,lstat-ejet-fake: 0.0021824694717015323 - sys,lstat-mujet-fake: 0.0001702922302920209 - sys,etmsoft-scale: 0.0002136417622700311 - sys,hardscat-model: 0.04342277028 - sys,statNP2-jes: 0.00042814346709458997 - sys,elen-scale: 0.0010610400509141233 - sys,punch-jes: 2.2488606554740115e-05 - sys,pileoffnpv-jes: 0.005851022962700957 - sys,lrec-eff: 0.0016544590800000002 - sys,pileoffpt-jes: 0.00016442008441988225 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.0030376953600000003 - sys,laltfakecr-ejet-fake: 0.0023121743700000004 - sys,laltpar-mujet-fake: 0.0014849448300000001 - sys,jetrec-eff: 0.00028478394 - sys,c/tautag-eff: 0.005936390971198999 - sys,dibos-xsec: 0.00036615078 - sys,elen-res: 0.00019581647536345506 - sys,flavcomp-jes: 0.016461370928122557 - sys,detNP2-jes: 0.0017571098502898474 - sys,detNP3-jes: 0.00045113726444880514 - sys,jetvxfrac: 0.0045105763896864486 - sys,ltrig-eff: 0.00848249307 - sys,btag-jes: 0.007514248354648857 - sys,mup-scale: 0.00016283959194092224 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0002055251055248528 - sys,detNP1-jes: 0.002671819918907742 - sys,laltpar-ejet-fake: 0.0018578761800000002 - sys,statNP1-jes: 0.007732995582282289 - sys,muid-res: 2.034171e-05 - sys,pdf: 0.0029088645299999998 - sys,isr-fsr: 0.059051258400565056 - sys,zjet-xsec: 0.005505822840000001 - sys,ps-model: 0.03490637436 - sys,flavres-jes: 0.010745277659748358 - sys,laltfakecr-mujet-fake: 0.00107133006 - sys,mums-res: 6.78057e-06 - sys,mod-NP2-jes: 0.000749636403298607 - sys,lid-eff: 0.00899781639 - sys,mixNP2-jes: 0.0033080617938022025 - sys,mixNP1-jes: 0.003844882145307815 - sys,btag-eff: 0.027365352957069534 - sys,pileoffrho-jes: 0.01675433157793271 - sys,modNP4-jes: 0.0002746549371811468 - sys,mcstat: 0.0009153769500000001 - sys,modNP3-jes: 0.0005156800686200909 - sys,mod-NP1-jes: 0.024129805879404644 + syst_singletop-xsec: 0.0015164392735770606 + syst_wjet-scale: 0.0019121207399999999 + syst_laltrealcr-mujet-fake: 0.00152562825 + syst_eta-jes: 0.003974461051988882 + syst_statNP3-jes: 0.002105634478417693 + syst_laltrealcr-ejet-fake: 0.00049498161 + syst_pileoffmu-jes: 0.0015112611323920603 + syst_lstat-ejet-fake: 0.0021824694717015323 + syst_lstat-mujet-fake: 0.0001702922302920209 + syst_etmsoft-scale: 0.0002136417622700311 + syst_hardscat-model: 0.04342277028 + syst_statNP2-jes: 0.00042814346709458997 + syst_elen-scale: 0.0010610400509141233 + syst_punch-jes: 2.2488606554740115e-05 + syst_pileoffnpv-jes: 0.005851022962700957 + syst_lrec-eff: 0.0016544590800000002 + syst_pileoffpt-jes: 0.00016442008441988225 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.0030376953600000003 + syst_laltfakecr-ejet-fake: 0.0023121743700000004 + syst_laltpar-mujet-fake: 0.0014849448300000001 + syst_jetrec-eff: 0.00028478394 + syst_c/tautag-eff: 0.005936390971198999 + syst_dibos-xsec: 0.00036615078 + syst_elen-res: 0.00019581647536345506 + syst_flavcomp-jes: 0.016461370928122557 + syst_detNP2-jes: 0.0017571098502898474 + syst_detNP3-jes: 0.00045113726444880514 + syst_jetvxfrac: 0.0045105763896864486 + syst_ltrig-eff: 0.00848249307 + syst_btag-jes: 0.007514248354648857 + syst_mup-scale: 0.00016283959194092224 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0002055251055248528 + syst_detNP1-jes: 0.002671819918907742 + syst_laltpar-ejet-fake: 0.0018578761800000002 + syst_statNP1-jes: 0.007732995582282289 + syst_muid-res: 2.034171e-05 + syst_pdf: 0.0029088645299999998 + syst_isr-fsr: 0.059051258400565056 + syst_zjet-xsec: 0.005505822840000001 + syst_ps-model: 0.03490637436 + syst_flavres-jes: 0.010745277659748358 + syst_laltfakecr-mujet-fake: 0.00107133006 + syst_mums-res: 6.78057e-06 + syst_mod-NP2-jes: 0.000749636403298607 + syst_lid-eff: 0.00899781639 + syst_mixNP2-jes: 0.0033080617938022025 + syst_mixNP1-jes: 0.003844882145307815 + syst_btag-eff: 0.027365352957069534 + syst_pileoffrho-jes: 0.01675433157793271 + syst_modNP4-jes: 0.0002746549371811468 + syst_mcstat: 0.0009153769500000001 + syst_modNP3-jes: 0.0005156800686200909 + syst_mod-NP1-jes: 0.024129805879404644 lumi: 0.018985596 - ArtUnc_1: -0.0004937730060837897 ArtUnc_2: 0.0006904068593025251 @@ -592,61 +592,61 @@ bins: ArtUnc_23: -1.1101161772269996e-06 ArtUnc_24: -6.467036350749013e-05 ArtUnc_25: 3.1546531786239094e-07 - sys,singletop-xsec: 0.001001256442078433 - sys,wjet-scale: 0.001553667 - sys,laltrealcr-mujet-fake: 0.00153617075 - sys,eta-jes: 0.003163788403254801 - sys,statNP3-jes: 0.0017531312345170247 - sys,laltrealcr-ejet-fake: 0.000223952 - sys,pileoffmu-jes: 0.0009285812241590325 - sys,lstat-ejet-fake: 0.001414692722974984 - sys,lstat-mujet-fake: 0.00011515669697932248 - sys,etmsoft-scale: 0.00018898379878490082 - sys,hardscat-model: 0.018143611249999997 - sys,statNP2-jes: 3.636527273031236e-05 - sys,elen-scale: 0.0008564570749657696 - sys,punch-jes: 1.4427777360442599e-05 - sys,pileoffnpv-jes: 0.00281596531740639 - sys,lrec-eff: 0.0009098050000000001 - sys,pileoffpt-jes: 6.0279596294450046e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.0019175890000000001 - sys,laltfakecr-ejet-fake: 0.00143819175 - sys,laltpar-mujet-fake: 0.001091766 - sys,jetrec-eff: 3.849175e-05 - sys,c/tautag-eff: 0.0032210605753665083 - sys,dibos-xsec: 0.0002589445 - sys,elen-res: 6.863794707683116e-05 - sys,flavcomp-jes: 0.00781704313738006 - sys,detNP2-jes: 0.0006632071207063163 - sys,detNP3-jes: 0.00037559522258751173 - sys,jetvxfrac: 0.0013333749670189365 - sys,ltrig-eff: 0.00438456025 - sys,btag-jes: 0.004841673313166965 - sys,mup-scale: 9.111498713919133e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0002606177879005719 - sys,detNP1-jes: 0.003074171783944669 - sys,laltpar-ejet-fake: 0.001133757 - sys,statNP1-jes: 0.0032474637813263096 - sys,muid-res: 3.49925e-05 - sys,pdf: 0.002281511 - sys,isr-fsr: 0.026680677012998696 - sys,zjet-xsec: 0.0032822965 - sys,ps-model: 0.010595729 - sys,flavres-jes: 0.00579647435403974 - sys,laltfakecr-mujet-fake: 0.000545883 - sys,mums-res: 1.049775e-05 - sys,mod-NP2-jes: 0.00013789886737592338 - sys,lid-eff: 0.0047239875 - sys,mixNP2-jes: 0.001479941795030229 - sys,mixNP1-jes: 0.003149328888053155 - sys,btag-eff: 0.014607210789635037 - sys,pileoffrho-jes: 0.008662940756033978 - sys,modNP4-jes: 0.0004314307050970787 - sys,mcstat: 0.000615868 - sys,modNP3-jes: 0.001050719372723338 - sys,mod-NP1-jes: 0.012614838837887544 + syst_singletop-xsec: 0.001001256442078433 + syst_wjet-scale: 0.001553667 + syst_laltrealcr-mujet-fake: 0.00153617075 + syst_eta-jes: 0.003163788403254801 + syst_statNP3-jes: 0.0017531312345170247 + syst_laltrealcr-ejet-fake: 0.000223952 + syst_pileoffmu-jes: 0.0009285812241590325 + syst_lstat-ejet-fake: 0.001414692722974984 + syst_lstat-mujet-fake: 0.00011515669697932248 + syst_etmsoft-scale: 0.00018898379878490082 + syst_hardscat-model: 0.018143611249999997 + syst_statNP2-jes: 3.636527273031236e-05 + syst_elen-scale: 0.0008564570749657696 + syst_punch-jes: 1.4427777360442599e-05 + syst_pileoffnpv-jes: 0.00281596531740639 + syst_lrec-eff: 0.0009098050000000001 + syst_pileoffpt-jes: 6.0279596294450046e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.0019175890000000001 + syst_laltfakecr-ejet-fake: 0.00143819175 + syst_laltpar-mujet-fake: 0.001091766 + syst_jetrec-eff: 3.849175e-05 + syst_c/tautag-eff: 0.0032210605753665083 + syst_dibos-xsec: 0.0002589445 + syst_elen-res: 6.863794707683116e-05 + syst_flavcomp-jes: 0.00781704313738006 + syst_detNP2-jes: 0.0006632071207063163 + syst_detNP3-jes: 0.00037559522258751173 + syst_jetvxfrac: 0.0013333749670189365 + syst_ltrig-eff: 0.00438456025 + syst_btag-jes: 0.004841673313166965 + syst_mup-scale: 9.111498713919133e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0002606177879005719 + syst_detNP1-jes: 0.003074171783944669 + syst_laltpar-ejet-fake: 0.001133757 + syst_statNP1-jes: 0.0032474637813263096 + syst_muid-res: 3.49925e-05 + syst_pdf: 0.002281511 + syst_isr-fsr: 0.026680677012998696 + syst_zjet-xsec: 0.0032822965 + syst_ps-model: 0.010595729 + syst_flavres-jes: 0.00579647435403974 + syst_laltfakecr-mujet-fake: 0.000545883 + syst_mums-res: 1.049775e-05 + syst_mod-NP2-jes: 0.00013789886737592338 + syst_lid-eff: 0.0047239875 + syst_mixNP2-jes: 0.001479941795030229 + syst_mixNP1-jes: 0.003149328888053155 + syst_btag-eff: 0.014607210789635037 + syst_pileoffrho-jes: 0.008662940756033978 + syst_modNP4-jes: 0.0004314307050970787 + syst_mcstat: 0.000615868 + syst_modNP3-jes: 0.001050719372723338 + syst_mod-NP1-jes: 0.012614838837887544 lumi: 0.0097979 - ArtUnc_1: -0.0002493212268697025 ArtUnc_2: 0.00031738270738695436 @@ -673,61 +673,61 @@ bins: ArtUnc_23: -2.085216144560725e-06 ArtUnc_24: -6.962425298289801e-05 ArtUnc_25: 2.688649367078048e-07 - sys,singletop-xsec: 0.0006087905802584397 - sys,wjet-scale: 0.0009594790200000002 - sys,laltrealcr-mujet-fake: 0.0010050285600000002 - sys,eta-jes: 0.0016544768399999999 - sys,statNP3-jes: 0.0007846523237665167 - sys,laltrealcr-ejet-fake: 3.085614000000001e-05 - sys,pileoffmu-jes: 0.0003798371790109972 - sys,lstat-ejet-fake: 0.0006515127125998731 - sys,lstat-mujet-fake: 0.00011834117631208635 - sys,etmsoft-scale: 7.888043371203787e-05 - sys,hardscat-model: 0.00423022986 - sys,statNP2-jes: 5.9358426344531744e-05 - sys,elen-scale: 0.00042349631412488494 - sys,punch-jes: 2.7360909932681333e-05 - sys,pileoffnpv-jes: 0.0008660709262689549 - sys,lrec-eff: 0.0004099458600000001 - sys,pileoffpt-jes: 4.816993147453191e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.0010373561212160428 - sys,laltfakecr-ejet-fake: 0.00063622422 - sys,laltpar-mujet-fake: 0.0005789199600000001 - sys,jetrec-eff: 1.616274e-05 - sys,c/tautag-eff: 0.0014759523956893524 - sys,dibos-xsec: 0.00015721938000000003 - sys,elen-res: 1.9087286501949408e-05 - sys,flavcomp-jes: 0.0025264633700673865 - sys,detNP2-jes: 0.00011387858970781251 - sys,detNP3-jes: 0.00020248523476063804 - sys,jetvxfrac: 0.0001702533331185196 - sys,ltrig-eff: 0.00186753114 - sys,btag-jes: 0.0020583899836826764 - sys,mup-scale: 3.776946972784633e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.00011325123324489981 - sys,detNP1-jes: 0.0018765398987247457 - sys,laltpar-ejet-fake: 0.0005010449400000001 - sys,statNP1-jes: 0.0008822062237104856 - sys,muid-res: 7.346700000000001e-06 - sys,pdf: 0.0012944885400000001 - sys,isr-fsr: 0.007856073715313033 - sys,zjet-xsec: 0.0016427221200000003 - sys,ps-model: 0.00046137276000000004 - sys,flavres-jes: 0.0021838815545408203 - sys,laltfakecr-mujet-fake: 0.00028064394000000006 - sys,mums-res: 8.816040000000001e-06 - sys,mod-NP2-jes: 7.493634e-05 - sys,lid-eff: 0.0020085877800000004 - sys,mixNP2-jes: 0.00045945723775007877 - sys,mixNP1-jes: 0.001637587669885057 - sys,btag-eff: 0.006673737548801164 - sys,pileoffrho-jes: 0.0030279838262722474 - sys,modNP4-jes: 0.0003130797735023386 - sys,mcstat: 0.00035704962 - sys,modNP3-jes: 0.000593032522867949 - sys,mod-NP1-jes: 0.004485907954312339 + syst_singletop-xsec: 0.0006087905802584397 + syst_wjet-scale: 0.0009594790200000002 + syst_laltrealcr-mujet-fake: 0.0010050285600000002 + syst_eta-jes: 0.0016544768399999999 + syst_statNP3-jes: 0.0007846523237665167 + syst_laltrealcr-ejet-fake: 3.085614000000001e-05 + syst_pileoffmu-jes: 0.0003798371790109972 + syst_lstat-ejet-fake: 0.0006515127125998731 + syst_lstat-mujet-fake: 0.00011834117631208635 + syst_etmsoft-scale: 7.888043371203787e-05 + syst_hardscat-model: 0.00423022986 + syst_statNP2-jes: 5.9358426344531744e-05 + syst_elen-scale: 0.00042349631412488494 + syst_punch-jes: 2.7360909932681333e-05 + syst_pileoffnpv-jes: 0.0008660709262689549 + syst_lrec-eff: 0.0004099458600000001 + syst_pileoffpt-jes: 4.816993147453191e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.0010373561212160428 + syst_laltfakecr-ejet-fake: 0.00063622422 + syst_laltpar-mujet-fake: 0.0005789199600000001 + syst_jetrec-eff: 1.616274e-05 + syst_c/tautag-eff: 0.0014759523956893524 + syst_dibos-xsec: 0.00015721938000000003 + syst_elen-res: 1.9087286501949408e-05 + syst_flavcomp-jes: 0.0025264633700673865 + syst_detNP2-jes: 0.00011387858970781251 + syst_detNP3-jes: 0.00020248523476063804 + syst_jetvxfrac: 0.0001702533331185196 + syst_ltrig-eff: 0.00186753114 + syst_btag-jes: 0.0020583899836826764 + syst_mup-scale: 3.776946972784633e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.00011325123324489981 + syst_detNP1-jes: 0.0018765398987247457 + syst_laltpar-ejet-fake: 0.0005010449400000001 + syst_statNP1-jes: 0.0008822062237104856 + syst_muid-res: 7.346700000000001e-06 + syst_pdf: 0.0012944885400000001 + syst_isr-fsr: 0.007856073715313033 + syst_zjet-xsec: 0.0016427221200000003 + syst_ps-model: 0.00046137276000000004 + syst_flavres-jes: 0.0021838815545408203 + syst_laltfakecr-mujet-fake: 0.00028064394000000006 + syst_mums-res: 8.816040000000001e-06 + syst_mod-NP2-jes: 7.493634e-05 + syst_lid-eff: 0.0020085877800000004 + syst_mixNP2-jes: 0.00045945723775007877 + syst_mixNP1-jes: 0.001637587669885057 + syst_btag-eff: 0.006673737548801164 + syst_pileoffrho-jes: 0.0030279838262722474 + syst_modNP4-jes: 0.0003130797735023386 + syst_mcstat: 0.00035704962 + syst_modNP3-jes: 0.000593032522867949 + syst_mod-NP1-jes: 0.004485907954312339 lumi: 0.004114152 - ArtUnc_1: -7.781313940744525e-05 ArtUnc_2: 9.68337718024727e-05 @@ -754,61 +754,61 @@ bins: ArtUnc_23: -3.8473253346644865e-06 ArtUnc_24: -0.0003458705296630889 ArtUnc_25: 1.625391026367454e-06 - sys,singletop-xsec: 0.00023573251096217223 - sys,wjet-scale: 0.00040489353400000006 - sys,laltrealcr-mujet-fake: 0.00036484508100000004 - sys,eta-jes: 0.0005208714608431341 - sys,statNP3-jes: 0.00019334584340657056 - sys,laltrealcr-ejet-fake: 1.2124761000000001e-05 - sys,pileoffmu-jes: 0.00010272311745247793 - sys,lstat-ejet-fake: 0.0004244687360135444 - sys,lstat-mujet-fake: 7.477522710883277e-05 - sys,etmsoft-scale: 1.957336263550497e-05 - sys,hardscat-model: 0.000886577221 - sys,statNP2-jes: 3.6273939959178454e-05 - sys,elen-scale: 0.00016293721942341302 - sys,punch-jes: 2.2803546002243338e-05 - sys,pileoffnpv-jes: 0.0001406092666117504 - sys,lrec-eff: 0.000112062185 - sys,pileoffpt-jes: 2.5726406107320838e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.0003613554600564703 - sys,laltfakecr-ejet-fake: 0.000185913002 - sys,laltpar-mujet-fake: 0.00019289392500000002 - sys,jetrec-eff: 8.083174000000001e-06 - sys,c/tautag-eff: 0.0004366751817858577 - sys,dibos-xsec: 6.613506000000001e-05 - sys,elen-res: 4.45469438095174e-06 - sys,flavcomp-jes: 0.00035734486129548496 - sys,detNP2-jes: 1.9535392934113285e-05 - sys,detNP3-jes: 6.470817357831323e-05 - sys,jetvxfrac: 8.754302768454748e-05 - sys,ltrig-eff: 0.00048021401900000005 - sys,btag-jes: 0.00047492566439833687 - sys,mup-scale: 1.418605553892719e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 1.1437237388479834e-05 - sys,detNP1-jes: 0.0006722039112522525 - sys,laltpar-ejet-fake: 0.00020354901800000004 - sys,statNP1-jes: 6.584969617186225e-05 - sys,muid-res: 2.571919e-06 - sys,pdf: 0.0005897042850000001 - sys,isr-fsr: 0.001122627882991657 - sys,zjet-xsec: 0.000571700852 - sys,ps-model: 0.001213945768 - sys,flavres-jes: 0.0004602221328600295 - sys,laltfakecr-mujet-fake: 0.00010140709200000003 - sys,mums-res: 3.306753e-06 - sys,mod-NP2-jes: 6.802177782516035e-05 - sys,lid-eff: 0.000509239962 - sys,mixNP2-jes: 4.1369914230618635e-05 - sys,mixNP1-jes: 0.0004917877231247617 - sys,btag-eff: 0.0018955987988868365 - sys,pileoffrho-jes: 0.0005397684619472364 - sys,modNP4-jes: 0.00011708718570695269 - sys,mcstat: 0.00014733421700000002 - sys,modNP3-jes: 0.00017391005569015916 - sys,mod-NP1-jes: 0.0008500735819737558 + syst_singletop-xsec: 0.00023573251096217223 + syst_wjet-scale: 0.00040489353400000006 + syst_laltrealcr-mujet-fake: 0.00036484508100000004 + syst_eta-jes: 0.0005208714608431341 + syst_statNP3-jes: 0.00019334584340657056 + syst_laltrealcr-ejet-fake: 1.2124761000000001e-05 + syst_pileoffmu-jes: 0.00010272311745247793 + syst_lstat-ejet-fake: 0.0004244687360135444 + syst_lstat-mujet-fake: 7.477522710883277e-05 + syst_etmsoft-scale: 1.957336263550497e-05 + syst_hardscat-model: 0.000886577221 + syst_statNP2-jes: 3.6273939959178454e-05 + syst_elen-scale: 0.00016293721942341302 + syst_punch-jes: 2.2803546002243338e-05 + syst_pileoffnpv-jes: 0.0001406092666117504 + syst_lrec-eff: 0.000112062185 + syst_pileoffpt-jes: 2.5726406107320838e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.0003613554600564703 + syst_laltfakecr-ejet-fake: 0.000185913002 + syst_laltpar-mujet-fake: 0.00019289392500000002 + syst_jetrec-eff: 8.083174000000001e-06 + syst_c/tautag-eff: 0.0004366751817858577 + syst_dibos-xsec: 6.613506000000001e-05 + syst_elen-res: 4.45469438095174e-06 + syst_flavcomp-jes: 0.00035734486129548496 + syst_detNP2-jes: 1.9535392934113285e-05 + syst_detNP3-jes: 6.470817357831323e-05 + syst_jetvxfrac: 8.754302768454748e-05 + syst_ltrig-eff: 0.00048021401900000005 + syst_btag-jes: 0.00047492566439833687 + syst_mup-scale: 1.418605553892719e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 1.1437237388479834e-05 + syst_detNP1-jes: 0.0006722039112522525 + syst_laltpar-ejet-fake: 0.00020354901800000004 + syst_statNP1-jes: 6.584969617186225e-05 + syst_muid-res: 2.571919e-06 + syst_pdf: 0.0005897042850000001 + syst_isr-fsr: 0.001122627882991657 + syst_zjet-xsec: 0.000571700852 + syst_ps-model: 0.001213945768 + syst_flavres-jes: 0.0004602221328600295 + syst_laltfakecr-mujet-fake: 0.00010140709200000003 + syst_mums-res: 3.306753e-06 + syst_mod-NP2-jes: 6.802177782516035e-05 + syst_lid-eff: 0.000509239962 + syst_mixNP2-jes: 4.1369914230618635e-05 + syst_mixNP1-jes: 0.0004917877231247617 + syst_btag-eff: 0.0018955987988868365 + syst_pileoffrho-jes: 0.0005397684619472364 + syst_modNP4-jes: 0.00011708718570695269 + syst_mcstat: 0.00014733421700000002 + syst_modNP3-jes: 0.00017391005569015916 + syst_mod-NP1-jes: 0.0008500735819737558 lumi: 0.0010287676 - ArtUnc_1: -7.970745554019968e-06 ArtUnc_2: 8.013227272200023e-06 @@ -835,59 +835,59 @@ bins: ArtUnc_23: -1.8360824163312212e-05 ArtUnc_24: 3.8846761148986064e-05 ArtUnc_25: -3.938635222660408e-07 - sys,singletop-xsec: 5.005728244933783e-05 - sys,wjet-scale: 9.629627677231602e-05 - sys,laltrealcr-mujet-fake: 7.512676290000001e-05 - sys,eta-jes: 8.012668278616041e-05 - sys,statNP3-jes: 1.9722125706267354e-05 - sys,laltrealcr-ejet-fake: 7.5431979e-06 - sys,pileoffmu-jes: 1.632246728096107e-05 - sys,lstat-ejet-fake: 0.00015021206246552545 - sys,lstat-mujet-fake: 2.0730624005423196e-05 - sys,etmsoft-scale: 2.5214045550703997e-06 - sys,hardscat-model: 0.0010011960126 - sys,statNP2-jes: 9.154899684358554e-06 - sys,elen-scale: 2.5607684986569582e-05 - sys,punch-jes: 6.63482460952425e-06 - sys,pileoffnpv-jes: 7.279201249110801e-06 - sys,lrec-eff: 1.5217202699999999e-05 - sys,pileoffpt-jes: 5.425421065893756e-06 - sys,jeten-res: 0.0 - sys,lighttag-eff: 6.344137646720941e-05 - sys,laltfakecr-ejet-fake: 4.0506536699999996e-05 - sys,laltpar-mujet-fake: 2.5289333999999998e-05 - sys,jetrec-eff: 2.3981265e-06 - sys,c/tautag-eff: 6.161008075795404e-05 - sys,dibos-xsec: 1.13802003e-05 - sys,elen-res: 1.0518913674894285e-06 - sys,flavcomp-jes: 1.0864237319548224e-05 - sys,detNP2-jes: 1.1275473847366157e-05 - sys,detNP3-jes: 1.0780156148421065e-05 - sys,jetvxfrac: 2.4707648324075146e-05 - sys,ltrig-eff: 6.0781606199999994e-05 - sys,btag-jes: 5.517975289189525e-05 - sys,mup-scale: 1.4307601611297427e-06 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 4.8333695313190696e-06 - sys,detNP1-jes: 0.0001237150452802432 - sys,laltpar-ejet-fake: 3.5623079099999995e-05 - sys,statNP1-jes: 1.7497674157327238e-05 - sys,muid-res: 1.6568874e-06 - sys,pdf: 0.00014148946349999998 - sys,isr-fsr: 0.00030218619234146775 - sys,zjet-xsec: 0.0001249205895 - sys,ps-model: 0.00037615704210000004 - sys,flavres-jes: 4.344423858555926e-05 - sys,laltfakecr-mujet-fake: 2.50277202e-05 - sys,mums-res: 2.6161379999999996e-06 - sys,mod-NP2-jes: 2.50525095800525e-05 - sys,lid-eff: 6.11304246e-05 - sys,mixNP2-jes: 7.74069778806172e-06 - sys,mixNP1-jes: 8.24850968712858e-05 - sys,btag-eff: 0.0002621483231953316 - sys,pileoffrho-jes: 3.268372683134086e-05 - sys,modNP4-jes: 2.1043655322030776e-05 - sys,mcstat: 1.35603153e-05 - sys,modNP3-jes: 2.2982507207295513e-05 - sys,mod-NP1-jes: 5.873302641898084e-05 + syst_singletop-xsec: 5.005728244933783e-05 + syst_wjet-scale: 9.629627677231602e-05 + syst_laltrealcr-mujet-fake: 7.512676290000001e-05 + syst_eta-jes: 8.012668278616041e-05 + syst_statNP3-jes: 1.9722125706267354e-05 + syst_laltrealcr-ejet-fake: 7.5431979e-06 + syst_pileoffmu-jes: 1.632246728096107e-05 + syst_lstat-ejet-fake: 0.00015021206246552545 + syst_lstat-mujet-fake: 2.0730624005423196e-05 + syst_etmsoft-scale: 2.5214045550703997e-06 + syst_hardscat-model: 0.0010011960126 + syst_statNP2-jes: 9.154899684358554e-06 + syst_elen-scale: 2.5607684986569582e-05 + syst_punch-jes: 6.63482460952425e-06 + syst_pileoffnpv-jes: 7.279201249110801e-06 + syst_lrec-eff: 1.5217202699999999e-05 + syst_pileoffpt-jes: 5.425421065893756e-06 + syst_jeten-res: 0.0 + syst_lighttag-eff: 6.344137646720941e-05 + syst_laltfakecr-ejet-fake: 4.0506536699999996e-05 + syst_laltpar-mujet-fake: 2.5289333999999998e-05 + syst_jetrec-eff: 2.3981265e-06 + syst_c/tautag-eff: 6.161008075795404e-05 + syst_dibos-xsec: 1.13802003e-05 + syst_elen-res: 1.0518913674894285e-06 + syst_flavcomp-jes: 1.0864237319548224e-05 + syst_detNP2-jes: 1.1275473847366157e-05 + syst_detNP3-jes: 1.0780156148421065e-05 + syst_jetvxfrac: 2.4707648324075146e-05 + syst_ltrig-eff: 6.0781606199999994e-05 + syst_btag-jes: 5.517975289189525e-05 + syst_mup-scale: 1.4307601611297427e-06 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 4.8333695313190696e-06 + syst_detNP1-jes: 0.0001237150452802432 + syst_laltpar-ejet-fake: 3.5623079099999995e-05 + syst_statNP1-jes: 1.7497674157327238e-05 + syst_muid-res: 1.6568874e-06 + syst_pdf: 0.00014148946349999998 + syst_isr-fsr: 0.00030218619234146775 + syst_zjet-xsec: 0.0001249205895 + syst_ps-model: 0.00037615704210000004 + syst_flavres-jes: 4.344423858555926e-05 + syst_laltfakecr-mujet-fake: 2.50277202e-05 + syst_mums-res: 2.6161379999999996e-06 + syst_mod-NP2-jes: 2.50525095800525e-05 + syst_lid-eff: 6.11304246e-05 + syst_mixNP2-jes: 7.74069778806172e-06 + syst_mixNP1-jes: 8.24850968712858e-05 + syst_btag-eff: 0.0002621483231953316 + syst_pileoffrho-jes: 3.268372683134086e-05 + syst_modNP4-jes: 2.1043655322030776e-05 + syst_mcstat: 1.35603153e-05 + syst_modNP3-jes: 2.2982507207295513e-05 + syst_mod-NP1-jes: 5.873302641898084e-05 lumi: 0.00012208643999999999 diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dmttBar_norm.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dmttBar_norm.yaml index ab3ea5150a..5b32f9361b 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dmttBar_norm.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dmttBar_norm.yaml @@ -99,223 +99,223 @@ definitions: definition: artificial uncertainty 25 treatment: ADD type: ATLAS8TEVTTB151104716unc25 - sys,singletop-xsec: + syst_singletop-xsec: definition: '' treatment: MULT type: CORR - sys,wjet-scale: + syst_wjet-scale: definition: '' treatment: MULT type: CORR - sys,laltrealcr-mujet-fake: + syst_laltrealcr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,eta-jes: + syst_eta-jes: definition: '' treatment: MULT type: CORR - sys,statNP3-jes: + syst_statNP3-jes: definition: '' treatment: MULT type: CORR - sys,laltrealcr-ejet-fake: + syst_laltrealcr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,pileoffmu-jes: + syst_pileoffmu-jes: definition: '' treatment: MULT type: CORR - sys,lstat-ejet-fake: + syst_lstat-ejet-fake: definition: '' treatment: MULT type: CORR - sys,lstat-mujet-fake: + syst_lstat-mujet-fake: definition: '' treatment: MULT type: CORR - sys,etmsoft-scale: + syst_etmsoft-scale: definition: '' treatment: MULT type: CORR - sys,hardscat-model: + syst_hardscat-model: definition: '' treatment: MULT type: CORR - sys,statNP2-jes: + syst_statNP2-jes: definition: '' treatment: MULT type: CORR - sys,elen-scale: + syst_elen-scale: definition: '' treatment: MULT type: CORR - sys,punch-jes: + syst_punch-jes: definition: '' treatment: MULT type: CORR - sys,pileoffnpv-jes: + syst_pileoffnpv-jes: definition: '' treatment: MULT type: CORR - sys,lrec-eff: + syst_lrec-eff: definition: '' treatment: MULT type: CORR - sys,pileoffpt-jes: + syst_pileoffpt-jes: definition: '' treatment: MULT type: CORR - sys,jeten-res: + syst_jeten-res: definition: '' treatment: MULT type: CORR - sys,lighttag-eff: + syst_lighttag-eff: definition: '' treatment: MULT type: CORR - sys,laltfakecr-ejet-fake: + syst_laltfakecr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,laltpar-mujet-fake: + syst_laltpar-mujet-fake: definition: '' treatment: MULT type: CORR - sys,jetrec-eff: + syst_jetrec-eff: definition: '' treatment: MULT type: CORR - sys,c/tautag-eff: + syst_c/tautag-eff: definition: '' treatment: MULT type: CORR - sys,dibos-xsec: + syst_dibos-xsec: definition: '' treatment: MULT type: CORR - sys,elen-res: + syst_elen-res: definition: '' treatment: MULT type: CORR - sys,flavcomp-jes: + syst_flavcomp-jes: definition: '' treatment: MULT type: CORR - sys,detNP2-jes: + syst_detNP2-jes: definition: '' treatment: MULT type: CORR - sys,detNP3-jes: + syst_detNP3-jes: definition: '' treatment: MULT type: CORR - sys,jetvxfrac: + syst_jetvxfrac: definition: '' treatment: MULT type: CORR - sys,ltrig-eff: + syst_ltrig-eff: definition: '' treatment: MULT type: CORR - sys,btag-jes: + syst_btag-jes: definition: '' treatment: MULT type: CORR - sys,mup-scale: + syst_mup-scale: definition: '' treatment: MULT type: CORR - sys,singlephpt-jes: + syst_singlephpt-jes: definition: '' treatment: MULT type: CORR - sys,etmsoft-res: + syst_etmsoft-res: definition: '' treatment: MULT type: CORR - sys,detNP1-jes: + syst_detNP1-jes: definition: '' treatment: MULT type: CORR - sys,laltpar-ejet-fake: + syst_laltpar-ejet-fake: definition: '' treatment: MULT type: CORR - sys,statNP1-jes: + syst_statNP1-jes: definition: '' treatment: MULT type: CORR - sys,muid-res: + syst_muid-res: definition: '' treatment: MULT type: CORR - sys,pdf: + syst_pdf: definition: '' treatment: MULT type: CORR - sys,isr-fsr: + syst_isr-fsr: definition: '' treatment: MULT type: CORR - sys,zjet-xsec: + syst_zjet-xsec: definition: '' treatment: MULT type: CORR - sys,ps-model: + syst_ps-model: definition: '' treatment: MULT type: CORR - sys,flavres-jes: + syst_flavres-jes: definition: '' treatment: MULT type: CORR - sys,laltfakecr-mujet-fake: + syst_laltfakecr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,mums-res: + syst_mums-res: definition: '' treatment: MULT type: CORR - sys,mod-NP2-jes: + syst_mod-NP2-jes: definition: '' treatment: MULT type: CORR - sys,lid-eff: + syst_lid-eff: definition: '' treatment: MULT type: CORR - sys,mixNP2-jes: + syst_mixNP2-jes: definition: '' treatment: MULT type: CORR - sys,mixNP1-jes: + syst_mixNP1-jes: definition: '' treatment: MULT type: CORR - sys,btag-eff: + syst_btag-eff: definition: '' treatment: MULT type: CORR - sys,pileoffrho-jes: + syst_pileoffrho-jes: definition: '' treatment: MULT type: CORR - sys,modNP4-jes: + syst_modNP4-jes: definition: '' treatment: MULT type: CORR - sys,mcstat: + syst_mcstat: definition: '' treatment: MULT type: CORR - sys,modNP3-jes: + syst_modNP3-jes: definition: '' treatment: MULT type: CORR - sys,mod-NP1-jes: + syst_mod-NP1-jes: definition: '' treatment: MULT type: CORR @@ -345,61 +345,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: -1.391867233734582e-10 - sys,singletop-xsec: 3.1743785749517576e-06 - sys,wjet-scale: 4.531221273738544e-06 - sys,laltrealcr-mujet-fake: 1.2884699999999999e-05 - sys,eta-jes: 2.7345151384532924e-05 - sys,statNP3-jes: 2.4502875014763734e-05 - sys,laltrealcr-ejet-fake: 3.00643e-07 - sys,pileoffmu-jes: 3.37163327748033e-06 - sys,lstat-ejet-fake: 7.81093426409895e-06 - sys,lstat-mujet-fake: 1.078652826946998e-06 - sys,etmsoft-scale: 3.4210570265920374e-06 - sys,hardscat-model: 0.00010324939599999999 - sys,statNP2-jes: 3.9821737486763124e-06 - sys,elen-scale: 8.370841634389981e-06 - sys,punch-jes: 4.878068690875519e-07 - sys,pileoffnpv-jes: 8.515823920896557e-06 - sys,lrec-eff: 9.019290000000001e-07 - sys,pileoffpt-jes: 1.6131623817399012e-06 - sys,jeten-res: 0.0 - sys,lighttag-eff: 3.50047524291956e-06 - sys,laltfakecr-ejet-fake: 3.3070730000000004e-06 - sys,laltpar-mujet-fake: 3.650665e-06 - sys,jetrec-eff: 1.9327049999999997e-06 - sys,c/tautag-eff: 5.153880000000001e-07 - sys,dibos-xsec: 8.5898e-08 - sys,elen-res: 6.681296411939826e-07 - sys,flavcomp-jes: 1.7990799870309177e-05 - sys,detNP2-jes: 2.873092286974959e-07 - sys,detNP3-jes: 4.4577565532925586e-06 - sys,jetvxfrac: 1.4840355604223035e-05 - sys,ltrig-eff: 4.5198593845467135e-07 - sys,btag-jes: 4.697189403007193e-05 - sys,mup-scale: 7.448277940240616e-07 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 3.087178780572442e-06 - sys,detNP1-jes: 4.011602138768535e-05 - sys,laltpar-ejet-fake: 1.2455210000000001e-06 - sys,statNP1-jes: 9.201516287324606e-07 - sys,muid-res: 8.5898e-08 - sys,pdf: 1.9670642000000003e-05 - sys,isr-fsr: 4.63820715587209e-05 - sys,zjet-xsec: 1.0307760000000001e-06 - sys,ps-model: 1.8425121e-05 - sys,flavres-jes: 2.581891378811732e-05 - sys,laltfakecr-mujet-fake: 2.7057870000000003e-06 - sys,mums-res: 8.5898e-08 - sys,mod-NP2-jes: 2.442718475404964e-06 - sys,lid-eff: 1.653815366095245e-06 - sys,mixNP2-jes: 1.3976583919037582e-06 - sys,mixNP1-jes: 3.3457491533291156e-05 - sys,btag-eff: 2.277107214298659e-06 - sys,pileoffrho-jes: 4.03717116090021e-05 - sys,modNP4-jes: 5.7945746033555165e-06 - sys,mcstat: 5.712217000000001e-06 - sys,modNP3-jes: 2.237939648845145e-05 - sys,mod-NP1-jes: 7.102940107926915e-05 + syst_singletop-xsec: 3.1743785749517576e-06 + syst_wjet-scale: 4.531221273738544e-06 + syst_laltrealcr-mujet-fake: 1.2884699999999999e-05 + syst_eta-jes: 2.7345151384532924e-05 + syst_statNP3-jes: 2.4502875014763734e-05 + syst_laltrealcr-ejet-fake: 3.00643e-07 + syst_pileoffmu-jes: 3.37163327748033e-06 + syst_lstat-ejet-fake: 7.81093426409895e-06 + syst_lstat-mujet-fake: 1.078652826946998e-06 + syst_etmsoft-scale: 3.4210570265920374e-06 + syst_hardscat-model: 0.00010324939599999999 + syst_statNP2-jes: 3.9821737486763124e-06 + syst_elen-scale: 8.370841634389981e-06 + syst_punch-jes: 4.878068690875519e-07 + syst_pileoffnpv-jes: 8.515823920896557e-06 + syst_lrec-eff: 9.019290000000001e-07 + syst_pileoffpt-jes: 1.6131623817399012e-06 + syst_jeten-res: 0.0 + syst_lighttag-eff: 3.50047524291956e-06 + syst_laltfakecr-ejet-fake: 3.3070730000000004e-06 + syst_laltpar-mujet-fake: 3.650665e-06 + syst_jetrec-eff: 1.9327049999999997e-06 + syst_c/tautag-eff: 5.153880000000001e-07 + syst_dibos-xsec: 8.5898e-08 + syst_elen-res: 6.681296411939826e-07 + syst_flavcomp-jes: 1.7990799870309177e-05 + syst_detNP2-jes: 2.873092286974959e-07 + syst_detNP3-jes: 4.4577565532925586e-06 + syst_jetvxfrac: 1.4840355604223035e-05 + syst_ltrig-eff: 4.5198593845467135e-07 + syst_btag-jes: 4.697189403007193e-05 + syst_mup-scale: 7.448277940240616e-07 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 3.087178780572442e-06 + syst_detNP1-jes: 4.011602138768535e-05 + syst_laltpar-ejet-fake: 1.2455210000000001e-06 + syst_statNP1-jes: 9.201516287324606e-07 + syst_muid-res: 8.5898e-08 + syst_pdf: 1.9670642000000003e-05 + syst_isr-fsr: 4.63820715587209e-05 + syst_zjet-xsec: 1.0307760000000001e-06 + syst_ps-model: 1.8425121e-05 + syst_flavres-jes: 2.581891378811732e-05 + syst_laltfakecr-mujet-fake: 2.7057870000000003e-06 + syst_mums-res: 8.5898e-08 + syst_mod-NP2-jes: 2.442718475404964e-06 + syst_lid-eff: 1.653815366095245e-06 + syst_mixNP2-jes: 1.3976583919037582e-06 + syst_mixNP1-jes: 3.3457491533291156e-05 + syst_btag-eff: 2.277107214298659e-06 + syst_pileoffrho-jes: 4.03717116090021e-05 + syst_modNP4-jes: 5.7945746033555165e-06 + syst_mcstat: 5.712217000000001e-06 + syst_modNP3-jes: 2.237939648845145e-05 + syst_mod-NP1-jes: 7.102940107926915e-05 - ArtUnc_1: -2.249418640781195e-07 ArtUnc_2: 6.58062560816945e-08 ArtUnc_3: 1.1291627037546456e-07 @@ -425,61 +425,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: -2.166339986260926e-10 - sys,singletop-xsec: 2.456952303829714e-06 - sys,wjet-scale: 5.5296417921977936e-06 - sys,laltrealcr-mujet-fake: 7.6530818e-06 - sys,eta-jes: 1.0412488413852888e-05 - sys,statNP3-jes: 6.139573406258221e-06 - sys,laltrealcr-ejet-fake: 4.20499e-07 - sys,pileoffmu-jes: 1.3798199108645546e-06 - sys,lstat-ejet-fake: 5.353193399109504e-06 - sys,lstat-mujet-fake: 8.011581957850958e-07 - sys,etmsoft-scale: 9.97522543180417e-07 - sys,hardscat-model: 0.0001174033208 - sys,statNP2-jes: 1.8352023574926411e-06 - sys,elen-scale: 2.841636915679617e-06 - sys,punch-jes: 4.917321850447328e-07 - sys,pileoffnpv-jes: 1.350842811318297e-06 - sys,lrec-eff: 5.466486999999999e-07 - sys,pileoffpt-jes: 4.663559510408653e-07 - sys,jeten-res: 0.0 - sys,lighttag-eff: 3.4901417e-06 - sys,laltfakecr-ejet-fake: 2.3127445e-06 - sys,laltpar-mujet-fake: 2.7332434999999998e-06 - sys,jetrec-eff: 8.40998e-07 - sys,c/tautag-eff: 1.535109335995592e-06 - sys,dibos-xsec: 3.363992e-07 - sys,elen-res: 4.881230927018793e-07 - sys,flavcomp-jes: 6.623626783064406e-06 - sys,detNP2-jes: 2.293454898021159e-06 - sys,detNP3-jes: 1.4717465000000001e-06 - sys,jetvxfrac: 8.820561668880842e-06 - sys,ltrig-eff: 1.2614969999999997e-07 - sys,btag-jes: 9.99680547598845e-06 - sys,mup-scale: 1.0924884487978577e-07 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 1.2017372936776439e-06 - sys,detNP1-jes: 1.6002224665989904e-05 - sys,laltpar-ejet-fake: 1.9763452999999996e-06 - sys,statNP1-jes: 6.269948899567869e-06 - sys,muid-res: 2.5229939999999994e-07 - sys,pdf: 5.8028862e-06 - sys,isr-fsr: 3.873379582151344e-05 - sys,zjet-xsec: 4.499339299999999e-06 - sys,ps-model: 0.0001406989654 - sys,flavres-jes: 2.595793833975573e-06 - sys,laltfakecr-mujet-fake: 2.102495e-07 - sys,mums-res: 2.102495e-07 - sys,mod-NP2-jes: 2.399793355929333e-06 - sys,lid-eff: 7.568981999999999e-07 - sys,mixNP2-jes: 2.1898270187636097e-06 - sys,mixNP1-jes: 1.1042943437199937e-05 - sys,btag-eff: 5.385343021975853e-06 - sys,pileoffrho-jes: 1.995488183553719e-06 - sys,modNP4-jes: 2.7390596057611506e-06 - sys,mcstat: 3.0696426999999997e-06 - sys,modNP3-jes: 7.2785206691188025e-06 - sys,mod-NP1-jes: 5.460135392381411e-06 + syst_singletop-xsec: 2.456952303829714e-06 + syst_wjet-scale: 5.5296417921977936e-06 + syst_laltrealcr-mujet-fake: 7.6530818e-06 + syst_eta-jes: 1.0412488413852888e-05 + syst_statNP3-jes: 6.139573406258221e-06 + syst_laltrealcr-ejet-fake: 4.20499e-07 + syst_pileoffmu-jes: 1.3798199108645546e-06 + syst_lstat-ejet-fake: 5.353193399109504e-06 + syst_lstat-mujet-fake: 8.011581957850958e-07 + syst_etmsoft-scale: 9.97522543180417e-07 + syst_hardscat-model: 0.0001174033208 + syst_statNP2-jes: 1.8352023574926411e-06 + syst_elen-scale: 2.841636915679617e-06 + syst_punch-jes: 4.917321850447328e-07 + syst_pileoffnpv-jes: 1.350842811318297e-06 + syst_lrec-eff: 5.466486999999999e-07 + syst_pileoffpt-jes: 4.663559510408653e-07 + syst_jeten-res: 0.0 + syst_lighttag-eff: 3.4901417e-06 + syst_laltfakecr-ejet-fake: 2.3127445e-06 + syst_laltpar-mujet-fake: 2.7332434999999998e-06 + syst_jetrec-eff: 8.40998e-07 + syst_c/tautag-eff: 1.535109335995592e-06 + syst_dibos-xsec: 3.363992e-07 + syst_elen-res: 4.881230927018793e-07 + syst_flavcomp-jes: 6.623626783064406e-06 + syst_detNP2-jes: 2.293454898021159e-06 + syst_detNP3-jes: 1.4717465000000001e-06 + syst_jetvxfrac: 8.820561668880842e-06 + syst_ltrig-eff: 1.2614969999999997e-07 + syst_btag-jes: 9.99680547598845e-06 + syst_mup-scale: 1.0924884487978577e-07 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 1.2017372936776439e-06 + syst_detNP1-jes: 1.6002224665989904e-05 + syst_laltpar-ejet-fake: 1.9763452999999996e-06 + syst_statNP1-jes: 6.269948899567869e-06 + syst_muid-res: 2.5229939999999994e-07 + syst_pdf: 5.8028862e-06 + syst_isr-fsr: 3.873379582151344e-05 + syst_zjet-xsec: 4.499339299999999e-06 + syst_ps-model: 0.0001406989654 + syst_flavres-jes: 2.595793833975573e-06 + syst_laltfakecr-mujet-fake: 2.102495e-07 + syst_mums-res: 2.102495e-07 + syst_mod-NP2-jes: 2.399793355929333e-06 + syst_lid-eff: 7.568981999999999e-07 + syst_mixNP2-jes: 2.1898270187636097e-06 + syst_mixNP1-jes: 1.1042943437199937e-05 + syst_btag-eff: 5.385343021975853e-06 + syst_pileoffrho-jes: 1.995488183553719e-06 + syst_modNP4-jes: 2.7390596057611506e-06 + syst_mcstat: 3.0696426999999997e-06 + syst_modNP3-jes: 7.2785206691188025e-06 + syst_mod-NP1-jes: 5.460135392381411e-06 - ArtUnc_1: -1.3543726250249885e-07 ArtUnc_2: 2.2551055394730445e-08 ArtUnc_3: 7.979443498213276e-08 @@ -505,61 +505,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: -2.4880349196648454e-10 - sys,singletop-xsec: 6.179390541356e-07 - sys,wjet-scale: 1.7899350000000003e-06 - sys,laltrealcr-mujet-fake: 1.176243e-06 - sys,eta-jes: 5.6880857374659165e-06 - sys,statNP3-jes: 7.148807113259714e-06 - sys,laltrealcr-ejet-fake: 5.625509999999999e-07 - sys,pileoffmu-jes: 9.82636088255305e-07 - sys,lstat-ejet-fake: 1.5610052017863963e-06 - sys,lstat-mujet-fake: 2.2144702587469984e-07 - sys,etmsoft-scale: 1.115984624987588e-06 - sys,hardscat-model: 4.41091125e-05 - sys,statNP2-jes: 6.011787150512628e-07 - sys,elen-scale: 1.693068357029922e-06 - sys,punch-jes: 4.428940517493998e-08 - sys,pileoffnpv-jes: 5.3022113506273665e-06 - sys,lrec-eff: 5.1141e-08 - sys,pileoffpt-jes: 5.654492726604659e-07 - sys,jeten-res: 0.0 - sys,lighttag-eff: 9.71679e-07 - sys,laltfakecr-ejet-fake: 4.3469850000000006e-07 - sys,laltpar-mujet-fake: 2.55705e-08 - sys,jetrec-eff: 2.8127549999999997e-07 - sys,c/tautag-eff: 1.035763080565574e-06 - sys,dibos-xsec: 5.625509999999999e-07 - sys,elen-res: 2.6573643104963986e-07 - sys,flavcomp-jes: 1.1622657226474515e-05 - sys,detNP2-jes: 1.3522781898232653e-06 - sys,detNP3-jes: 1.037024860330594e-06 - sys,jetvxfrac: 1.2104870523183014e-06 - sys,ltrig-eff: 3.835575e-07 - sys,btag-jes: 1.4789362460733128e-05 - sys,mup-scale: 2.3013449999999998e-07 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 1.0186563190236194e-06 - sys,detNP1-jes: 7.2510629842524255e-06 - sys,laltpar-ejet-fake: 3.06846e-07 - sys,statNP1-jes: 4.431449539765571e-06 - sys,muid-res: 2.55705e-08 - sys,pdf: 1.0483905e-06 - sys,isr-fsr: 3.567883624105047e-05 - sys,zjet-xsec: 2.6849025e-06 - sys,ps-model: 1.3501224e-05 - sys,flavres-jes: 9.792091015106742e-06 - sys,laltfakecr-mujet-fake: 1.329666e-06 - sys,mums-res: 0.0 - sys,mod-NP2-jes: 5.32087637159754e-07 - sys,lid-eff: 3.06846e-07 - sys,mixNP2-jes: 1.9174039041365767e-06 - sys,mixNP1-jes: 7.696805451358424e-06 - sys,btag-eff: 3.632631303970374e-06 - sys,pileoffrho-jes: 1.766310398168001e-05 - sys,modNP4-jes: 7.307751853865096e-07 - sys,mcstat: 2.8383255e-06 - sys,modNP3-jes: 5.727906152880344e-06 - sys,mod-NP1-jes: 2.92807654818672e-05 + syst_singletop-xsec: 6.179390541356e-07 + syst_wjet-scale: 1.7899350000000003e-06 + syst_laltrealcr-mujet-fake: 1.176243e-06 + syst_eta-jes: 5.6880857374659165e-06 + syst_statNP3-jes: 7.148807113259714e-06 + syst_laltrealcr-ejet-fake: 5.625509999999999e-07 + syst_pileoffmu-jes: 9.82636088255305e-07 + syst_lstat-ejet-fake: 1.5610052017863963e-06 + syst_lstat-mujet-fake: 2.2144702587469984e-07 + syst_etmsoft-scale: 1.115984624987588e-06 + syst_hardscat-model: 4.41091125e-05 + syst_statNP2-jes: 6.011787150512628e-07 + syst_elen-scale: 1.693068357029922e-06 + syst_punch-jes: 4.428940517493998e-08 + syst_pileoffnpv-jes: 5.3022113506273665e-06 + syst_lrec-eff: 5.1141e-08 + syst_pileoffpt-jes: 5.654492726604659e-07 + syst_jeten-res: 0.0 + syst_lighttag-eff: 9.71679e-07 + syst_laltfakecr-ejet-fake: 4.3469850000000006e-07 + syst_laltpar-mujet-fake: 2.55705e-08 + syst_jetrec-eff: 2.8127549999999997e-07 + syst_c/tautag-eff: 1.035763080565574e-06 + syst_dibos-xsec: 5.625509999999999e-07 + syst_elen-res: 2.6573643104963986e-07 + syst_flavcomp-jes: 1.1622657226474515e-05 + syst_detNP2-jes: 1.3522781898232653e-06 + syst_detNP3-jes: 1.037024860330594e-06 + syst_jetvxfrac: 1.2104870523183014e-06 + syst_ltrig-eff: 3.835575e-07 + syst_btag-jes: 1.4789362460733128e-05 + syst_mup-scale: 2.3013449999999998e-07 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 1.0186563190236194e-06 + syst_detNP1-jes: 7.2510629842524255e-06 + syst_laltpar-ejet-fake: 3.06846e-07 + syst_statNP1-jes: 4.431449539765571e-06 + syst_muid-res: 2.55705e-08 + syst_pdf: 1.0483905e-06 + syst_isr-fsr: 3.567883624105047e-05 + syst_zjet-xsec: 2.6849025e-06 + syst_ps-model: 1.3501224e-05 + syst_flavres-jes: 9.792091015106742e-06 + syst_laltfakecr-mujet-fake: 1.329666e-06 + syst_mums-res: 0.0 + syst_mod-NP2-jes: 5.32087637159754e-07 + syst_lid-eff: 3.06846e-07 + syst_mixNP2-jes: 1.9174039041365767e-06 + syst_mixNP1-jes: 7.696805451358424e-06 + syst_btag-eff: 3.632631303970374e-06 + syst_pileoffrho-jes: 1.766310398168001e-05 + syst_modNP4-jes: 7.307751853865096e-07 + syst_mcstat: 2.8383255e-06 + syst_modNP3-jes: 5.727906152880344e-06 + syst_mod-NP1-jes: 2.92807654818672e-05 - ArtUnc_1: 2.4369672756894332e-08 ArtUnc_2: 2.861213571368313e-08 ArtUnc_3: -9.591817581806466e-09 @@ -585,61 +585,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: -3.3825933354841384e-10 - sys,singletop-xsec: 5.069814483720681e-07 - sys,wjet-scale: 1.2206841648843775e-06 - sys,laltrealcr-mujet-fake: 3.4178158000000007e-06 - sys,eta-jes: 7.114710150070321e-06 - sys,statNP3-jes: 6.195679140252326e-06 - sys,laltrealcr-ejet-fake: 1.583544e-07 - sys,pileoffmu-jes: 1.0855228129117647e-06 - sys,lstat-ejet-fake: 9.640228287248546e-07 - sys,lstat-mujet-fake: 1.337641715995356e-07 - sys,etmsoft-scale: 8.750135436260802e-07 - sys,hardscat-model: 5.885505200000001e-06 - sys,statNP2-jes: 1.1124013073934425e-06 - sys,elen-scale: 2.0514043618548345e-06 - sys,punch-jes: 8.753368811697586e-08 - sys,pileoffnpv-jes: 1.8583112617417003e-06 - sys,lrec-eff: 2.375316e-07 - sys,pileoffpt-jes: 3.045864773966665e-07 - sys,jeten-res: 0.0 - sys,lighttag-eff: 8.116199413166917e-07 - sys,laltfakecr-ejet-fake: 1.1348732000000001e-06 - sys,laltpar-mujet-fake: 1.2008542e-06 - sys,jetrec-eff: 5.542404e-07 - sys,c/tautag-eff: 6.0111551798552e-08 - sys,dibos-xsec: 2.6392400000000004e-08 - sys,elen-res: 2.8078772526182484e-07 - sys,flavcomp-jes: 3.696326052456406e-06 - sys,detNP2-jes: 2.8016685612118726e-07 - sys,detNP3-jes: 1.0703574401669567e-06 - sys,jetvxfrac: 4.345874515555583e-06 - sys,ltrig-eff: 1.652162131979486e-07 - sys,btag-jes: 1.1255645007949649e-05 - sys,mup-scale: 1.257106887676223e-07 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 1.1085397100417605e-06 - sys,detNP1-jes: 1.014830679613377e-05 - sys,laltpar-ejet-fake: 5.014556e-07 - sys,statNP1-jes: 6.158540853889987e-07 - sys,muid-res: 1.0556960000000002e-07 - sys,pdf: 3.496993e-06 - sys,isr-fsr: 7.1883657486213126e-06 - sys,zjet-xsec: 3.0351260000000006e-07 - sys,ps-model: 2.2367559000000004e-05 - sys,flavres-jes: 6.0287536912292135e-06 - sys,laltfakecr-mujet-fake: 6.993986e-07 - sys,mums-res: 3.95886e-08 - sys,mod-NP2-jes: 6.928633361980342e-07 - sys,lid-eff: 4.6855802177022863e-07 - sys,mixNP2-jes: 2.285648886684042e-07 - sys,mixNP1-jes: 8.366578125663118e-06 - sys,btag-eff: 4.711985891436859e-08 - sys,pileoffrho-jes: 9.181426257542008e-06 - sys,modNP4-jes: 1.4666791214909007e-06 - sys,mcstat: 2.045411e-06 - sys,modNP3-jes: 5.906958007327018e-06 - sys,mod-NP1-jes: 1.5805096783102236e-05 + syst_singletop-xsec: 5.069814483720681e-07 + syst_wjet-scale: 1.2206841648843775e-06 + syst_laltrealcr-mujet-fake: 3.4178158000000007e-06 + syst_eta-jes: 7.114710150070321e-06 + syst_statNP3-jes: 6.195679140252326e-06 + syst_laltrealcr-ejet-fake: 1.583544e-07 + syst_pileoffmu-jes: 1.0855228129117647e-06 + syst_lstat-ejet-fake: 9.640228287248546e-07 + syst_lstat-mujet-fake: 1.337641715995356e-07 + syst_etmsoft-scale: 8.750135436260802e-07 + syst_hardscat-model: 5.885505200000001e-06 + syst_statNP2-jes: 1.1124013073934425e-06 + syst_elen-scale: 2.0514043618548345e-06 + syst_punch-jes: 8.753368811697586e-08 + syst_pileoffnpv-jes: 1.8583112617417003e-06 + syst_lrec-eff: 2.375316e-07 + syst_pileoffpt-jes: 3.045864773966665e-07 + syst_jeten-res: 0.0 + syst_lighttag-eff: 8.116199413166917e-07 + syst_laltfakecr-ejet-fake: 1.1348732000000001e-06 + syst_laltpar-mujet-fake: 1.2008542e-06 + syst_jetrec-eff: 5.542404e-07 + syst_c/tautag-eff: 6.0111551798552e-08 + syst_dibos-xsec: 2.6392400000000004e-08 + syst_elen-res: 2.8078772526182484e-07 + syst_flavcomp-jes: 3.696326052456406e-06 + syst_detNP2-jes: 2.8016685612118726e-07 + syst_detNP3-jes: 1.0703574401669567e-06 + syst_jetvxfrac: 4.345874515555583e-06 + syst_ltrig-eff: 1.652162131979486e-07 + syst_btag-jes: 1.1255645007949649e-05 + syst_mup-scale: 1.257106887676223e-07 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 1.1085397100417605e-06 + syst_detNP1-jes: 1.014830679613377e-05 + syst_laltpar-ejet-fake: 5.014556e-07 + syst_statNP1-jes: 6.158540853889987e-07 + syst_muid-res: 1.0556960000000002e-07 + syst_pdf: 3.496993e-06 + syst_isr-fsr: 7.1883657486213126e-06 + syst_zjet-xsec: 3.0351260000000006e-07 + syst_ps-model: 2.2367559000000004e-05 + syst_flavres-jes: 6.0287536912292135e-06 + syst_laltfakecr-mujet-fake: 6.993986e-07 + syst_mums-res: 3.95886e-08 + syst_mod-NP2-jes: 6.928633361980342e-07 + syst_lid-eff: 4.6855802177022863e-07 + syst_mixNP2-jes: 2.285648886684042e-07 + syst_mixNP1-jes: 8.366578125663118e-06 + syst_btag-eff: 4.711985891436859e-08 + syst_pileoffrho-jes: 9.181426257542008e-06 + syst_modNP4-jes: 1.4666791214909007e-06 + syst_mcstat: 2.045411e-06 + syst_modNP3-jes: 5.906958007327018e-06 + syst_mod-NP1-jes: 1.5805096783102236e-05 - ArtUnc_1: 8.110562962572771e-08 ArtUnc_2: -4.910414281981282e-09 ArtUnc_3: -4.0798754361998537e-08 @@ -665,61 +665,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: -5.55223147075849e-10 - sys,singletop-xsec: 9.199410832139046e-07 - sys,wjet-scale: 1.670686016055413e-06 - sys,laltrealcr-mujet-fake: 2.79271944e-06 - sys,eta-jes: 4.211250890927899e-06 - sys,statNP3-jes: 2.7845428535095734e-06 - sys,laltrealcr-ejet-fake: 1.662333e-07 - sys,pileoffmu-jes: 4.087953667786761e-07 - sys,lstat-ejet-fake: 1.6027798364047732e-06 - sys,lstat-mujet-fake: 2.543333273336915e-07 - sys,etmsoft-scale: 3.6016149386159326e-07 - sys,hardscat-model: 1.0943692250000002e-05 - sys,statNP2-jes: 7.009613657247369e-07 - sys,elen-scale: 1.119948668560893e-06 - sys,punch-jes: 1.1894041340011426e-07 - sys,pileoffnpv-jes: 5.203040328337132e-07 - sys,lrec-eff: 2.0502107e-07 - sys,pileoffpt-jes: 2.1417662488693163e-07 - sys,jeten-res: 0.0 - sys,lighttag-eff: 1.2218210374214902e-06 - sys,laltfakecr-ejet-fake: 5.984398800000001e-07 - sys,laltpar-mujet-fake: 9.5861203e-07 - sys,jetrec-eff: 3.5463104e-07 - sys,c/tautag-eff: 4.932210457333793e-07 - sys,dibos-xsec: 1.7177441000000002e-07 - sys,elen-res: 1.151698086039358e-07 - sys,flavcomp-jes: 1.5483883098343e-06 - sys,detNP2-jes: 7.148461430505451e-07 - sys,detNP3-jes: 6.255321750390482e-07 - sys,jetvxfrac: 3.303183668997555e-06 - sys,ltrig-eff: 2.7705550000000002e-08 - sys,btag-jes: 4.8235378463581526e-06 - sys,mup-scale: 6.384311027245834e-08 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 4.606792344157432e-07 - sys,detNP1-jes: 6.467436802190622e-06 - sys,laltpar-ejet-fake: 3.0476105000000003e-07 - sys,statNP1-jes: 2.0664161761826284e-06 - sys,muid-res: 3.3246660000000004e-08 - sys,pdf: 2.7428494500000003e-06 - sys,isr-fsr: 1.538797349333178e-05 - sys,zjet-xsec: 1.13592755e-06 - sys,ps-model: 2.5167721620000003e-05 - sys,flavres-jes: 1.5972123421737275e-06 - sys,laltfakecr-mujet-fake: 9.973998e-08 - sys,mums-res: 3.878777e-08 - sys,mod-NP2-jes: 7.929984719767374e-07 - sys,lid-eff: 2.9093466004154897e-07 - sys,mixNP2-jes: 5.995355549930186e-07 - sys,mixNP1-jes: 4.698920088582462e-06 - sys,btag-eff: 2.017938065912141e-06 - sys,pileoffrho-jes: 1.6038809658105564e-06 - sys,modNP4-jes: 1.114755105841271e-06 - sys,mcstat: 1.20242087e-06 - sys,modNP3-jes: 3.0562260820316015e-06 - sys,mod-NP1-jes: 3.6027954627442204e-06 + syst_singletop-xsec: 9.199410832139046e-07 + syst_wjet-scale: 1.670686016055413e-06 + syst_laltrealcr-mujet-fake: 2.79271944e-06 + syst_eta-jes: 4.211250890927899e-06 + syst_statNP3-jes: 2.7845428535095734e-06 + syst_laltrealcr-ejet-fake: 1.662333e-07 + syst_pileoffmu-jes: 4.087953667786761e-07 + syst_lstat-ejet-fake: 1.6027798364047732e-06 + syst_lstat-mujet-fake: 2.543333273336915e-07 + syst_etmsoft-scale: 3.6016149386159326e-07 + syst_hardscat-model: 1.0943692250000002e-05 + syst_statNP2-jes: 7.009613657247369e-07 + syst_elen-scale: 1.119948668560893e-06 + syst_punch-jes: 1.1894041340011426e-07 + syst_pileoffnpv-jes: 5.203040328337132e-07 + syst_lrec-eff: 2.0502107e-07 + syst_pileoffpt-jes: 2.1417662488693163e-07 + syst_jeten-res: 0.0 + syst_lighttag-eff: 1.2218210374214902e-06 + syst_laltfakecr-ejet-fake: 5.984398800000001e-07 + syst_laltpar-mujet-fake: 9.5861203e-07 + syst_jetrec-eff: 3.5463104e-07 + syst_c/tautag-eff: 4.932210457333793e-07 + syst_dibos-xsec: 1.7177441000000002e-07 + syst_elen-res: 1.151698086039358e-07 + syst_flavcomp-jes: 1.5483883098343e-06 + syst_detNP2-jes: 7.148461430505451e-07 + syst_detNP3-jes: 6.255321750390482e-07 + syst_jetvxfrac: 3.303183668997555e-06 + syst_ltrig-eff: 2.7705550000000002e-08 + syst_btag-jes: 4.8235378463581526e-06 + syst_mup-scale: 6.384311027245834e-08 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 4.606792344157432e-07 + syst_detNP1-jes: 6.467436802190622e-06 + syst_laltpar-ejet-fake: 3.0476105000000003e-07 + syst_statNP1-jes: 2.0664161761826284e-06 + syst_muid-res: 3.3246660000000004e-08 + syst_pdf: 2.7428494500000003e-06 + syst_isr-fsr: 1.538797349333178e-05 + syst_zjet-xsec: 1.13592755e-06 + syst_ps-model: 2.5167721620000003e-05 + syst_flavres-jes: 1.5972123421737275e-06 + syst_laltfakecr-mujet-fake: 9.973998e-08 + syst_mums-res: 3.878777e-08 + syst_mod-NP2-jes: 7.929984719767374e-07 + syst_lid-eff: 2.9093466004154897e-07 + syst_mixNP2-jes: 5.995355549930186e-07 + syst_mixNP1-jes: 4.698920088582462e-06 + syst_btag-eff: 2.017938065912141e-06 + syst_pileoffrho-jes: 1.6038809658105564e-06 + syst_modNP4-jes: 1.114755105841271e-06 + syst_mcstat: 1.20242087e-06 + syst_modNP3-jes: 3.0562260820316015e-06 + syst_mod-NP1-jes: 3.6027954627442204e-06 - ArtUnc_1: 3.16576191151217e-08 ArtUnc_2: -1.4652075299943979e-08 ArtUnc_3: -3.267382665125685e-08 @@ -745,61 +745,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: -1.1901120822901483e-09 - sys,singletop-xsec: 5.447338649155137e-07 - sys,wjet-scale: 1.0398893287434434e-06 - sys,laltrealcr-mujet-fake: 1.12509096e-06 - sys,eta-jes: 1.4577136282684484e-06 - sys,statNP3-jes: 6.854781588846228e-07 - sys,laltrealcr-ejet-fake: 1.1777430000000001e-07 - sys,pileoffmu-jes: 1.392973129001231e-07 - sys,lstat-ejet-fake: 1.6139293592222392e-06 - sys,lstat-mujet-fake: 2.3518970587922594e-07 - sys,etmsoft-scale: 8.982523960839235e-08 - sys,hardscat-model: 1.043618856e-05 - sys,statNP2-jes: 2.5767597397198266e-07 - sys,elen-scale: 4.884650989883968e-07 - sys,punch-jes: 9.014792613688459e-08 - sys,pileoffnpv-jes: 4.1478801656554005e-07 - sys,lrec-eff: 8.729154e-08 - sys,pileoffpt-jes: 9.894050254422402e-08 - sys,jeten-res: 0.0 - sys,lighttag-eff: 6.893323164137282e-07 - sys,laltfakecr-ejet-fake: 2.5217556e-07 - sys,laltpar-mujet-fake: 4.2121632e-07 - sys,jetrec-eff: 1.0391849999999999e-07 - sys,c/tautag-eff: 3.776023280205082e-07 - sys,dibos-xsec: 1.4410032e-07 - sys,elen-res: 4.6178494165101356e-08 - sys,flavcomp-jes: 1.393915516883457e-06 - sys,detNP2-jes: 3.5966611704271554e-07 - sys,detNP3-jes: 2.0912161934925715e-07 - sys,jetvxfrac: 1.3172375741229407e-06 - sys,ltrig-eff: 5.7509916261746544e-08 - sys,btag-jes: 1.055229019032271e-06 - sys,mup-scale: 3.138269879046734e-08 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 4.1492279871883397e-08 - sys,detNP1-jes: 2.3825936482361216e-06 - sys,laltpar-ejet-fake: 3.6994986e-07 - sys,statNP1-jes: 1.1111977988139539e-06 - sys,muid-res: 8.31348e-09 - sys,pdf: 1.69317876e-06 - sys,isr-fsr: 8.883929672886813e-06 - sys,zjet-xsec: 8.964702599999999e-07 - sys,ps-model: 1.15488093e-05 - sys,flavres-jes: 6.930670605989582e-08 - sys,laltfakecr-mujet-fake: 9.283386e-08 - sys,mums-res: 1.1084639999999999e-08 - sys,mod-NP2-jes: 3.8347475389449915e-07 - sys,lid-eff: 9.839569327600268e-08 - sys,mixNP2-jes: 4.213097451326456e-07 - sys,mixNP1-jes: 1.4860736299476734e-06 - sys,btag-eff: 1.3597637665432627e-06 - sys,pileoffrho-jes: 4.3834066625254057e-07 - sys,modNP4-jes: 4.2582414980957053e-07 - sys,mcstat: 4.9465206e-07 - sys,modNP3-jes: 8.59239483086014e-07 - sys,mod-NP1-jes: 1.4707231714172012e-07 + syst_singletop-xsec: 5.447338649155137e-07 + syst_wjet-scale: 1.0398893287434434e-06 + syst_laltrealcr-mujet-fake: 1.12509096e-06 + syst_eta-jes: 1.4577136282684484e-06 + syst_statNP3-jes: 6.854781588846228e-07 + syst_laltrealcr-ejet-fake: 1.1777430000000001e-07 + syst_pileoffmu-jes: 1.392973129001231e-07 + syst_lstat-ejet-fake: 1.6139293592222392e-06 + syst_lstat-mujet-fake: 2.3518970587922594e-07 + syst_etmsoft-scale: 8.982523960839235e-08 + syst_hardscat-model: 1.043618856e-05 + syst_statNP2-jes: 2.5767597397198266e-07 + syst_elen-scale: 4.884650989883968e-07 + syst_punch-jes: 9.014792613688459e-08 + syst_pileoffnpv-jes: 4.1478801656554005e-07 + syst_lrec-eff: 8.729154e-08 + syst_pileoffpt-jes: 9.894050254422402e-08 + syst_jeten-res: 0.0 + syst_lighttag-eff: 6.893323164137282e-07 + syst_laltfakecr-ejet-fake: 2.5217556e-07 + syst_laltpar-mujet-fake: 4.2121632e-07 + syst_jetrec-eff: 1.0391849999999999e-07 + syst_c/tautag-eff: 3.776023280205082e-07 + syst_dibos-xsec: 1.4410032e-07 + syst_elen-res: 4.6178494165101356e-08 + syst_flavcomp-jes: 1.393915516883457e-06 + syst_detNP2-jes: 3.5966611704271554e-07 + syst_detNP3-jes: 2.0912161934925715e-07 + syst_jetvxfrac: 1.3172375741229407e-06 + syst_ltrig-eff: 5.7509916261746544e-08 + syst_btag-jes: 1.055229019032271e-06 + syst_mup-scale: 3.138269879046734e-08 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 4.1492279871883397e-08 + syst_detNP1-jes: 2.3825936482361216e-06 + syst_laltpar-ejet-fake: 3.6994986e-07 + syst_statNP1-jes: 1.1111977988139539e-06 + syst_muid-res: 8.31348e-09 + syst_pdf: 1.69317876e-06 + syst_isr-fsr: 8.883929672886813e-06 + syst_zjet-xsec: 8.964702599999999e-07 + syst_ps-model: 1.15488093e-05 + syst_flavres-jes: 6.930670605989582e-08 + syst_laltfakecr-mujet-fake: 9.283386e-08 + syst_mums-res: 1.1084639999999999e-08 + syst_mod-NP2-jes: 3.8347475389449915e-07 + syst_lid-eff: 9.839569327600268e-08 + syst_mixNP2-jes: 4.213097451326456e-07 + syst_mixNP1-jes: 1.4860736299476734e-06 + syst_btag-eff: 1.3597637665432627e-06 + syst_pileoffrho-jes: 4.3834066625254057e-07 + syst_modNP4-jes: 4.2582414980957053e-07 + syst_mcstat: 4.9465206e-07 + syst_modNP3-jes: 8.59239483086014e-07 + syst_mod-NP1-jes: 1.4707231714172012e-07 - ArtUnc_1: 1.4686244725180736e-08 ArtUnc_2: -9.726225897816575e-09 ArtUnc_3: -1.1433168508008663e-08 @@ -825,58 +825,58 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: -1.7849675711024787e-09 - sys,singletop-xsec: 1.478956903429531e-07 - sys,wjet-scale: 3.053465985460292e-07 - sys,laltrealcr-mujet-fake: 2.5355106e-07 - sys,eta-jes: 2.422688429612099e-07 - sys,statNP3-jes: 6.927227285809743e-08 - sys,laltrealcr-ejet-fake: 3.6996750000000003e-08 - sys,pileoffmu-jes: 3.4788119002741444e-08 - sys,lstat-ejet-fake: 5.681782230056583e-07 - sys,lstat-mujet-fake: 7.26242841435804e-08 - sys,etmsoft-scale: 1.0550141970944043e-08 - sys,hardscat-model: 4.786228440000001e-06 - sys,statNP2-jes: 4.8822351695741535e-08 - sys,elen-scale: 8.155728e-08 - sys,punch-jes: 2.549288166557628e-08 - sys,pileoffnpv-jes: 8.232685962582733e-08 - sys,lrec-eff: 1.7594010000000002e-08 - sys,pileoffpt-jes: 2.150201259859179e-08 - sys,jeten-res: 0.0 - sys,lighttag-eff: 1.5933538502330768e-07 - sys,laltfakecr-ejet-fake: 9.915129e-08 - sys,laltpar-mujet-fake: 5.9030370000000005e-08 - sys,jetrec-eff: 1.7922870000000003e-08 - sys,c/tautag-eff: 8.164355183273862e-08 - sys,dibos-xsec: 3.041955e-08 - sys,elen-res: 2.766140749618501e-09 - sys,flavcomp-jes: 2.848429769756696e-07 - sys,detNP2-jes: 7.586961278394072e-08 - sys,detNP3-jes: 3.6413356702189656e-08 - sys,jetvxfrac: 2.1004469955249314e-07 - sys,ltrig-eff: 2.1132133928382034e-08 - sys,btag-jes: 1.2077433869874546e-07 - sys,mup-scale: 2.833735307910744e-09 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 1.9793677443054263e-08 - sys,detNP1-jes: 4.4852226536568096e-07 - sys,laltpar-ejet-fake: 8.698347000000002e-08 - sys,statNP1-jes: 2.2494216314967367e-07 - sys,muid-res: 5.919480000000001e-09 - sys,pdf: 4.717496700000001e-07 - sys,isr-fsr: 2.293724338808399e-06 - sys,zjet-xsec: 3.2359824000000003e-07 - sys,ps-model: 2.28853674e-06 - sys,flavres-jes: 3.4591613190093856e-08 - sys,laltfakecr-mujet-fake: 5.985252e-08 - sys,mums-res: 1.0030230000000002e-08 - sys,mod-NP2-jes: 1.0954108287504865e-07 - sys,lid-eff: 1.4391852578895987e-08 - sys,mixNP2-jes: 9.723264648490087e-08 - sys,mixNP1-jes: 2.676181474028489e-07 - sys,btag-eff: 3.016453000471986e-07 - sys,pileoffrho-jes: 1.7133817059280657e-07 - sys,modNP4-jes: 7.752499597510262e-08 - sys,mcstat: 1.6870518000000005e-07 - sys,modNP3-jes: 1.108900547248872e-07 - sys,mod-NP1-jes: 1.761144899389363e-07 + syst_singletop-xsec: 1.478956903429531e-07 + syst_wjet-scale: 3.053465985460292e-07 + syst_laltrealcr-mujet-fake: 2.5355106e-07 + syst_eta-jes: 2.422688429612099e-07 + syst_statNP3-jes: 6.927227285809743e-08 + syst_laltrealcr-ejet-fake: 3.6996750000000003e-08 + syst_pileoffmu-jes: 3.4788119002741444e-08 + syst_lstat-ejet-fake: 5.681782230056583e-07 + syst_lstat-mujet-fake: 7.26242841435804e-08 + syst_etmsoft-scale: 1.0550141970944043e-08 + syst_hardscat-model: 4.786228440000001e-06 + syst_statNP2-jes: 4.8822351695741535e-08 + syst_elen-scale: 8.155728e-08 + syst_punch-jes: 2.549288166557628e-08 + syst_pileoffnpv-jes: 8.232685962582733e-08 + syst_lrec-eff: 1.7594010000000002e-08 + syst_pileoffpt-jes: 2.150201259859179e-08 + syst_jeten-res: 0.0 + syst_lighttag-eff: 1.5933538502330768e-07 + syst_laltfakecr-ejet-fake: 9.915129e-08 + syst_laltpar-mujet-fake: 5.9030370000000005e-08 + syst_jetrec-eff: 1.7922870000000003e-08 + syst_c/tautag-eff: 8.164355183273862e-08 + syst_dibos-xsec: 3.041955e-08 + syst_elen-res: 2.766140749618501e-09 + syst_flavcomp-jes: 2.848429769756696e-07 + syst_detNP2-jes: 7.586961278394072e-08 + syst_detNP3-jes: 3.6413356702189656e-08 + syst_jetvxfrac: 2.1004469955249314e-07 + syst_ltrig-eff: 2.1132133928382034e-08 + syst_btag-jes: 1.2077433869874546e-07 + syst_mup-scale: 2.833735307910744e-09 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 1.9793677443054263e-08 + syst_detNP1-jes: 4.4852226536568096e-07 + syst_laltpar-ejet-fake: 8.698347000000002e-08 + syst_statNP1-jes: 2.2494216314967367e-07 + syst_muid-res: 5.919480000000001e-09 + syst_pdf: 4.717496700000001e-07 + syst_isr-fsr: 2.293724338808399e-06 + syst_zjet-xsec: 3.2359824000000003e-07 + syst_ps-model: 2.28853674e-06 + syst_flavres-jes: 3.4591613190093856e-08 + syst_laltfakecr-mujet-fake: 5.985252e-08 + syst_mums-res: 1.0030230000000002e-08 + syst_mod-NP2-jes: 1.0954108287504865e-07 + syst_lid-eff: 1.4391852578895987e-08 + syst_mixNP2-jes: 9.723264648490087e-08 + syst_mixNP1-jes: 2.676181474028489e-07 + syst_btag-eff: 3.016453000471986e-07 + syst_pileoffrho-jes: 1.7133817059280657e-07 + syst_modNP4-jes: 7.752499597510262e-08 + syst_mcstat: 1.6870518000000005e-07 + syst_modNP3-jes: 1.108900547248872e-07 + syst_mod-NP1-jes: 1.761144899389363e-07 diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dpTt.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dpTt.yaml index 827b904233..a78f526daa 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dpTt.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dpTt.yaml @@ -99,223 +99,223 @@ definitions: definition: artificial uncertainty 25 treatment: ADD type: ATLAS8TEVTTB151104716unc25 - sys,singletop-xsec: + syst_singletop-xsec: definition: '' treatment: MULT type: CORR - sys,wjet-scale: + syst_wjet-scale: definition: '' treatment: MULT type: CORR - sys,laltrealcr-mujet-fake: + syst_laltrealcr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,eta-jes: + syst_eta-jes: definition: '' treatment: MULT type: CORR - sys,statNP3-jes: + syst_statNP3-jes: definition: '' treatment: MULT type: CORR - sys,laltrealcr-ejet-fake: + syst_laltrealcr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,pileoffmu-jes: + syst_pileoffmu-jes: definition: '' treatment: MULT type: CORR - sys,lstat-ejet-fake: + syst_lstat-ejet-fake: definition: '' treatment: MULT type: CORR - sys,lstat-mujet-fake: + syst_lstat-mujet-fake: definition: '' treatment: MULT type: CORR - sys,etmsoft-scale: + syst_etmsoft-scale: definition: '' treatment: MULT type: CORR - sys,hardscat-model: + syst_hardscat-model: definition: '' treatment: MULT type: CORR - sys,statNP2-jes: + syst_statNP2-jes: definition: '' treatment: MULT type: CORR - sys,elen-scale: + syst_elen-scale: definition: '' treatment: MULT type: CORR - sys,punch-jes: + syst_punch-jes: definition: '' treatment: MULT type: CORR - sys,pileoffnpv-jes: + syst_pileoffnpv-jes: definition: '' treatment: MULT type: CORR - sys,lrec-eff: + syst_lrec-eff: definition: '' treatment: MULT type: CORR - sys,pileoffpt-jes: + syst_pileoffpt-jes: definition: '' treatment: MULT type: CORR - sys,jeten-res: + syst_jeten-res: definition: '' treatment: MULT type: CORR - sys,lighttag-eff: + syst_lighttag-eff: definition: '' treatment: MULT type: CORR - sys,laltfakecr-ejet-fake: + syst_laltfakecr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,laltpar-mujet-fake: + syst_laltpar-mujet-fake: definition: '' treatment: MULT type: CORR - sys,jetrec-eff: + syst_jetrec-eff: definition: '' treatment: MULT type: CORR - sys,c/tautag-eff: + syst_c/tautag-eff: definition: '' treatment: MULT type: CORR - sys,dibos-xsec: + syst_dibos-xsec: definition: '' treatment: MULT type: CORR - sys,elen-res: + syst_elen-res: definition: '' treatment: MULT type: CORR - sys,flavcomp-jes: + syst_flavcomp-jes: definition: '' treatment: MULT type: CORR - sys,detNP2-jes: + syst_detNP2-jes: definition: '' treatment: MULT type: CORR - sys,detNP3-jes: + syst_detNP3-jes: definition: '' treatment: MULT type: CORR - sys,jetvxfrac: + syst_jetvxfrac: definition: '' treatment: MULT type: CORR - sys,ltrig-eff: + syst_ltrig-eff: definition: '' treatment: MULT type: CORR - sys,btag-jes: + syst_btag-jes: definition: '' treatment: MULT type: CORR - sys,mup-scale: + syst_mup-scale: definition: '' treatment: MULT type: CORR - sys,singlephpt-jes: + syst_singlephpt-jes: definition: '' treatment: MULT type: CORR - sys,etmsoft-res: + syst_etmsoft-res: definition: '' treatment: MULT type: CORR - sys,detNP1-jes: + syst_detNP1-jes: definition: '' treatment: MULT type: CORR - sys,laltpar-ejet-fake: + syst_laltpar-ejet-fake: definition: '' treatment: MULT type: CORR - sys,statNP1-jes: + syst_statNP1-jes: definition: '' treatment: MULT type: CORR - sys,muid-res: + syst_muid-res: definition: '' treatment: MULT type: CORR - sys,pdf: + syst_pdf: definition: '' treatment: MULT type: CORR - sys,isr-fsr: + syst_isr-fsr: definition: '' treatment: MULT type: CORR - sys,zjet-xsec: + syst_zjet-xsec: definition: '' treatment: MULT type: CORR - sys,ps-model: + syst_ps-model: definition: '' treatment: MULT type: CORR - sys,flavres-jes: + syst_flavres-jes: definition: '' treatment: MULT type: CORR - sys,laltfakecr-mujet-fake: + syst_laltfakecr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,mums-res: + syst_mums-res: definition: '' treatment: MULT type: CORR - sys,mod-NP2-jes: + syst_mod-NP2-jes: definition: '' treatment: MULT type: CORR - sys,lid-eff: + syst_lid-eff: definition: '' treatment: MULT type: CORR - sys,mixNP2-jes: + syst_mixNP2-jes: definition: '' treatment: MULT type: CORR - sys,mixNP1-jes: + syst_mixNP1-jes: definition: '' treatment: MULT type: CORR - sys,btag-eff: + syst_btag-eff: definition: '' treatment: MULT type: CORR - sys,pileoffrho-jes: + syst_pileoffrho-jes: definition: '' treatment: MULT type: CORR - sys,modNP4-jes: + syst_modNP4-jes: definition: '' treatment: MULT type: CORR - sys,mcstat: + syst_mcstat: definition: '' treatment: MULT type: CORR - sys,modNP3-jes: + syst_modNP3-jes: definition: '' treatment: MULT type: CORR - sys,mod-NP1-jes: + syst_mod-NP1-jes: definition: '' treatment: MULT type: CORR @@ -349,61 +349,61 @@ bins: ArtUnc_23: 1.495347132432585e-05 ArtUnc_24: -6.1240207398808746e-06 ArtUnc_25: -5.608533743091472e-07 - sys,singletop-xsec: 0.0027208277951652872 - sys,wjet-scale: 0.005862784 - sys,laltrealcr-mujet-fake: 0.0005216 - sys,eta-jes: 0.001812918330047992 - sys,statNP3-jes: 0.004038447368216651 - sys,laltrealcr-ejet-fake: 0.000166912 - sys,pileoffmu-jes: 0.0017669541635073613 - sys,lstat-ejet-fake: 0.002610961007102174 - sys,lstat-mujet-fake: 0.0003704094575034498 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.032985983999999996 - sys,statNP2-jes: 0.0016410766173850628 - sys,elen-scale: 0.0018553935944526701 - sys,punch-jes: 0.000165849115813139 - sys,pileoffnpv-jes: 0.0057075244325616335 - sys,lrec-eff: 0.0023159039999999997 - sys,pileoffpt-jes: 0.00040224120031642703 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.0059931885395979306 - sys,laltfakecr-ejet-fake: 0.003672063999999999 - sys,laltpar-mujet-fake: 0.00135616 - sys,jetrec-eff: 0.0009180159999999998 - sys,c/tautag-eff: 0.011626466340062572 - sys,dibos-xsec: 0.0014709119999999998 - sys,elen-res: 0.000500736 - sys,flavcomp-jes: 0.02019186496023307 - sys,detNP2-jes: 0.0023488685269567556 - sys,detNP3-jes: 0.0004317638323342982 - sys,jetvxfrac: 0.010764373328619181 - sys,ltrig-eff: 0.013624192 - sys,btag-jes: 0.0006261807456892938 - sys,mup-scale: 0.0002685606915689636 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.004928421724084903 - sys,laltpar-ejet-fake: 0.0014187520000000001 - sys,statNP1-jes: 0.011472386571472913 - sys,muid-res: 0.0 - sys,pdf: 0.002336768 - sys,isr-fsr: 0.05530095076995678 - sys,zjet-xsec: 0.015564543999999998 - sys,ps-model: 0.020467584 - sys,flavres-jes: 0.009243640914365291 - sys,laltfakecr-mujet-fake: 0.003640767999999999 - sys,mums-res: 4.1728e-05 - sys,mod-NP2-jes: 0.001218033726271978 - sys,lid-eff: 0.013259071999999998 - sys,mixNP2-jes: 0.00462488045906832 - sys,mixNP1-jes: 0.002237238411742476 - sys,btag-eff: 0.041809707861658725 - sys,pileoffrho-jes: 0.015206638589744413 - sys,modNP4-jes: 0.0007684886998466536 - sys,mcstat: 0.00177344 - sys,modNP3-jes: 0.005773230819844291 - sys,mod-NP1-jes: 0.017619654176434678 + syst_singletop-xsec: 0.0027208277951652872 + syst_wjet-scale: 0.005862784 + syst_laltrealcr-mujet-fake: 0.0005216 + syst_eta-jes: 0.001812918330047992 + syst_statNP3-jes: 0.004038447368216651 + syst_laltrealcr-ejet-fake: 0.000166912 + syst_pileoffmu-jes: 0.0017669541635073613 + syst_lstat-ejet-fake: 0.002610961007102174 + syst_lstat-mujet-fake: 0.0003704094575034498 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.032985983999999996 + syst_statNP2-jes: 0.0016410766173850628 + syst_elen-scale: 0.0018553935944526701 + syst_punch-jes: 0.000165849115813139 + syst_pileoffnpv-jes: 0.0057075244325616335 + syst_lrec-eff: 0.0023159039999999997 + syst_pileoffpt-jes: 0.00040224120031642703 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.0059931885395979306 + syst_laltfakecr-ejet-fake: 0.003672063999999999 + syst_laltpar-mujet-fake: 0.00135616 + syst_jetrec-eff: 0.0009180159999999998 + syst_c/tautag-eff: 0.011626466340062572 + syst_dibos-xsec: 0.0014709119999999998 + syst_elen-res: 0.000500736 + syst_flavcomp-jes: 0.02019186496023307 + syst_detNP2-jes: 0.0023488685269567556 + syst_detNP3-jes: 0.0004317638323342982 + syst_jetvxfrac: 0.010764373328619181 + syst_ltrig-eff: 0.013624192 + syst_btag-jes: 0.0006261807456892938 + syst_mup-scale: 0.0002685606915689636 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.004928421724084903 + syst_laltpar-ejet-fake: 0.0014187520000000001 + syst_statNP1-jes: 0.011472386571472913 + syst_muid-res: 0.0 + syst_pdf: 0.002336768 + syst_isr-fsr: 0.05530095076995678 + syst_zjet-xsec: 0.015564543999999998 + syst_ps-model: 0.020467584 + syst_flavres-jes: 0.009243640914365291 + syst_laltfakecr-mujet-fake: 0.003640767999999999 + syst_mums-res: 4.1728e-05 + syst_mod-NP2-jes: 0.001218033726271978 + syst_lid-eff: 0.013259071999999998 + syst_mixNP2-jes: 0.00462488045906832 + syst_mixNP1-jes: 0.002237238411742476 + syst_btag-eff: 0.041809707861658725 + syst_pileoffrho-jes: 0.015206638589744413 + syst_modNP4-jes: 0.0007684886998466536 + syst_mcstat: 0.00177344 + syst_modNP3-jes: 0.005773230819844291 + syst_mod-NP1-jes: 0.017619654176434678 lumi: 0.029209599999999995 - ArtUnc_1: -0.002319650049081386 ArtUnc_2: 0.003251116347488226 @@ -430,61 +430,61 @@ bins: ArtUnc_23: 1.1670911909300759e-05 ArtUnc_24: -3.7760024053737695e-06 ArtUnc_25: -3.937165408554248e-07 - sys,singletop-xsec: 0.004530407301516207 - sys,wjet-scale: 0.008193043200000001 - sys,laltrealcr-mujet-fake: 0.0012577040000000003 - sys,eta-jes: 0.004348136643994234 - sys,statNP3-jes: 0.004276495559086632 - sys,laltrealcr-ejet-fake: 0.00034137680000000006 - sys,pileoffmu-jes: 0.0029960945754788715 - sys,lstat-ejet-fake: 0.005310721074792416 - sys,lstat-mujet-fake: 0.0006535221686647824 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.11499008000000002 - sys,statNP2-jes: 0.0023647865946795966 - sys,elen-scale: 0.002636835812302677 - sys,punch-jes: 0.0002335736 - sys,pileoffnpv-jes: 0.010179061031812561 - sys,lrec-eff: 0.004096521600000001 - sys,pileoffpt-jes: 0.00036766967580696675 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.009926886129951424 - sys,laltfakecr-ejet-fake: 0.0066658312 - sys,laltpar-mujet-fake: 0.0026771128 - sys,jetrec-eff: 0.0013475400000000002 - sys,c/tautag-eff: 0.019772907681598853 - sys,dibos-xsec: 0.0023177688000000003 - sys,elen-res: 0.0006738897706879962 - sys,flavcomp-jes: 0.03422811961714467 - sys,detNP2-jes: 0.004045493730592161 - sys,detNP3-jes: 0.0004490901550146029 - sys,jetvxfrac: 0.016752937541700666 - sys,ltrig-eff: 0.0231237864 - sys,btag-jes: 0.0032700304000000003 - sys,mup-scale: 0.00042394597364985086 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.006007550349215587 - sys,laltpar-ejet-fake: 0.004222292 - sys,statNP1-jes: 0.019253066269302198 - sys,muid-res: 0.0 - sys,pdf: 0.0064502247999999995 - sys,isr-fsr: 0.10960684009897637 - sys,zjet-xsec: 0.0242736872 - sys,ps-model: 0.11001316560000002 - sys,flavres-jes: 0.01690868172140469 - sys,laltfakecr-mujet-fake: 0.005839340000000001 - sys,mums-res: 0.0 - sys,mod-NP2-jes: 0.0021614474390917954 - sys,lid-eff: 0.023105819200000002 - sys,mixNP2-jes: 0.007645307508488392 - sys,mixNP1-jes: 0.001555045330930414 - sys,btag-eff: 0.0715512378484355 - sys,pileoffrho-jes: 0.028121985708937054 - sys,modNP4-jes: 0.0010628020253858007 - sys,mcstat: 0.0022998016000000004 - sys,modNP3-jes: 0.007421541123213221 - sys,mod-NP1-jes: 0.03491360650346469 + syst_singletop-xsec: 0.004530407301516207 + syst_wjet-scale: 0.008193043200000001 + syst_laltrealcr-mujet-fake: 0.0012577040000000003 + syst_eta-jes: 0.004348136643994234 + syst_statNP3-jes: 0.004276495559086632 + syst_laltrealcr-ejet-fake: 0.00034137680000000006 + syst_pileoffmu-jes: 0.0029960945754788715 + syst_lstat-ejet-fake: 0.005310721074792416 + syst_lstat-mujet-fake: 0.0006535221686647824 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.11499008000000002 + syst_statNP2-jes: 0.0023647865946795966 + syst_elen-scale: 0.002636835812302677 + syst_punch-jes: 0.0002335736 + syst_pileoffnpv-jes: 0.010179061031812561 + syst_lrec-eff: 0.004096521600000001 + syst_pileoffpt-jes: 0.00036766967580696675 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.009926886129951424 + syst_laltfakecr-ejet-fake: 0.0066658312 + syst_laltpar-mujet-fake: 0.0026771128 + syst_jetrec-eff: 0.0013475400000000002 + syst_c/tautag-eff: 0.019772907681598853 + syst_dibos-xsec: 0.0023177688000000003 + syst_elen-res: 0.0006738897706879962 + syst_flavcomp-jes: 0.03422811961714467 + syst_detNP2-jes: 0.004045493730592161 + syst_detNP3-jes: 0.0004490901550146029 + syst_jetvxfrac: 0.016752937541700666 + syst_ltrig-eff: 0.0231237864 + syst_btag-jes: 0.0032700304000000003 + syst_mup-scale: 0.00042394597364985086 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.006007550349215587 + syst_laltpar-ejet-fake: 0.004222292 + syst_statNP1-jes: 0.019253066269302198 + syst_muid-res: 0.0 + syst_pdf: 0.0064502247999999995 + syst_isr-fsr: 0.10960684009897637 + syst_zjet-xsec: 0.0242736872 + syst_ps-model: 0.11001316560000002 + syst_flavres-jes: 0.01690868172140469 + syst_laltfakecr-mujet-fake: 0.005839340000000001 + syst_mums-res: 0.0 + syst_mod-NP2-jes: 0.0021614474390917954 + syst_lid-eff: 0.023105819200000002 + syst_mixNP2-jes: 0.007645307508488392 + syst_mixNP1-jes: 0.001555045330930414 + syst_btag-eff: 0.0715512378484355 + syst_pileoffrho-jes: 0.028121985708937054 + syst_modNP4-jes: 0.0010628020253858007 + syst_mcstat: 0.0022998016000000004 + syst_modNP3-jes: 0.007421541123213221 + syst_mod-NP1-jes: 0.03491360650346469 lumi: 0.05030816 - ArtUnc_1: -0.001795524926448868 ArtUnc_2: 0.0025635166095283814 @@ -511,61 +511,61 @@ bins: ArtUnc_23: 1.710239422538106e-05 ArtUnc_24: -3.7389210067386742e-06 ArtUnc_25: -6.177314405707341e-07 - sys,singletop-xsec: 0.0034814089640236817 - sys,wjet-scale: 0.004881766199999999 - sys,laltrealcr-mujet-fake: 0.0040267676 - sys,eta-jes: 0.006047243101822766 - sys,statNP3-jes: 0.0010758201841905273 - sys,laltrealcr-ejet-fake: 0.001103224 - sys,pileoffmu-jes: 0.0026813801304789117 - sys,lstat-ejet-fake: 0.0056728063097590585 - sys,lstat-mujet-fake: 0.00011942750125808546 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.0872236475 - sys,statNP2-jes: 0.0011961843263699622 - sys,elen-scale: 0.0014600144318378536 - sys,punch-jes: 0.00010388590967791302 - sys,pileoffnpv-jes: 0.009016568069454193 - sys,lrec-eff: 0.0033234623 - sys,pileoffpt-jes: 0.00019636050734999518 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.0071157948 - sys,laltfakecr-ejet-fake: 0.0055299103 - sys,laltpar-mujet-fake: 0.0031303981000000004 - sys,jetrec-eff: 0.000689515 - sys,c/tautag-eff: 0.013673085927130168 - sys,dibos-xsec: 0.0009929015999999998 - sys,elen-res: 0.00038662059701306654 - sys,flavcomp-jes: 0.027815588894338272 - sys,detNP2-jes: 0.0030382193577743054 - sys,detNP3-jes: 0.0003055726356828929 - sys,jetvxfrac: 0.009654241754808397 - sys,ltrig-eff: 0.017403358600000002 - sys,btag-jes: 0.008957442100376226 - sys,mup-scale: 0.00024152717394137558 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.0009450129463275305 - sys,laltpar-ejet-fake: 0.0057367647999999995 - sys,statNP1-jes: 0.01373563723553443 - sys,muid-res: 2.75806e-05 - sys,pdf: 0.006205635000000001 - sys,isr-fsr: 0.1008889344963606 - sys,zjet-xsec: 0.0134317522 - sys,ps-model: 0.0854860697 - sys,flavres-jes: 0.016638822747305927 - sys,laltfakecr-mujet-fake: 0.0031166078000000002 - sys,mums-res: 5.51612e-05 - sys,mod-NP2-jes: 0.0016253113256821498 - sys,lid-eff: 0.0181066639 - sys,mixNP2-jes: 0.005816217402845078 - sys,mixNP1-jes: 0.0035007471717318217 - sys,btag-eff: 0.055227844029878424 - sys,pileoffrho-jes: 0.02624075867364732 - sys,modNP4-jes: 0.0001625852391778233 - sys,mcstat: 0.0017513681000000001 - sys,modNP3-jes: 0.0015505351183529442 - sys,mod-NP1-jes: 0.03606165559414297 + syst_singletop-xsec: 0.0034814089640236817 + syst_wjet-scale: 0.004881766199999999 + syst_laltrealcr-mujet-fake: 0.0040267676 + syst_eta-jes: 0.006047243101822766 + syst_statNP3-jes: 0.0010758201841905273 + syst_laltrealcr-ejet-fake: 0.001103224 + syst_pileoffmu-jes: 0.0026813801304789117 + syst_lstat-ejet-fake: 0.0056728063097590585 + syst_lstat-mujet-fake: 0.00011942750125808546 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.0872236475 + syst_statNP2-jes: 0.0011961843263699622 + syst_elen-scale: 0.0014600144318378536 + syst_punch-jes: 0.00010388590967791302 + syst_pileoffnpv-jes: 0.009016568069454193 + syst_lrec-eff: 0.0033234623 + syst_pileoffpt-jes: 0.00019636050734999518 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.0071157948 + syst_laltfakecr-ejet-fake: 0.0055299103 + syst_laltpar-mujet-fake: 0.0031303981000000004 + syst_jetrec-eff: 0.000689515 + syst_c/tautag-eff: 0.013673085927130168 + syst_dibos-xsec: 0.0009929015999999998 + syst_elen-res: 0.00038662059701306654 + syst_flavcomp-jes: 0.027815588894338272 + syst_detNP2-jes: 0.0030382193577743054 + syst_detNP3-jes: 0.0003055726356828929 + syst_jetvxfrac: 0.009654241754808397 + syst_ltrig-eff: 0.017403358600000002 + syst_btag-jes: 0.008957442100376226 + syst_mup-scale: 0.00024152717394137558 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.0009450129463275305 + syst_laltpar-ejet-fake: 0.0057367647999999995 + syst_statNP1-jes: 0.01373563723553443 + syst_muid-res: 2.75806e-05 + syst_pdf: 0.006205635000000001 + syst_isr-fsr: 0.1008889344963606 + syst_zjet-xsec: 0.0134317522 + syst_ps-model: 0.0854860697 + syst_flavres-jes: 0.016638822747305927 + syst_laltfakecr-mujet-fake: 0.0031166078000000002 + syst_mums-res: 5.51612e-05 + syst_mod-NP2-jes: 0.0016253113256821498 + syst_lid-eff: 0.0181066639 + syst_mixNP2-jes: 0.005816217402845078 + syst_mixNP1-jes: 0.0035007471717318217 + syst_btag-eff: 0.055227844029878424 + syst_pileoffrho-jes: 0.02624075867364732 + syst_modNP4-jes: 0.0001625852391778233 + syst_mcstat: 0.0017513681000000001 + syst_modNP3-jes: 0.0015505351183529442 + syst_mod-NP1-jes: 0.03606165559414297 lumi: 0.038612839999999995 - ArtUnc_1: -0.0009373027791595771 ArtUnc_2: 0.0013054203345935214 @@ -592,61 +592,61 @@ bins: ArtUnc_23: 2.224439077767299e-05 ArtUnc_24: -3.87177503699263e-06 ArtUnc_25: -8.096192773194858e-07 - sys,singletop-xsec: 0.001939862285678121 - sys,wjet-scale: 0.00223594736 - sys,laltrealcr-mujet-fake: 0.00312591325 - sys,eta-jes: 0.004729122688352804 - sys,statNP3-jes: 0.002622961116324107 - sys,laltrealcr-ejet-fake: 0.0006251826500000001 - sys,pileoffmu-jes: 0.0015675274034024358 - sys,lstat-ejet-fake: 0.003484223048555126 - sys,lstat-mujet-fake: 0.00021019992797498928 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.02597817788 - sys,statNP2-jes: 0.0002132659046694644 - sys,elen-scale: 0.0005484846007664609 - sys,punch-jes: 4.823055051570841e-05 - sys,pileoffnpv-jes: 0.005319194782471096 - sys,lrec-eff: 0.00190496831 - sys,pileoffpt-jes: 0.00030574534978180254 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.0035892839199999998 - sys,laltfakecr-ejet-fake: 0.00305971744 - sys,laltpar-mujet-fake: 0.0025963467699999998 - sys,jetrec-eff: 0.00023536288 - sys,c/tautag-eff: 0.006101049371723527 - sys,dibos-xsec: 0.00030155869 - sys,elen-res: 0.00015445689 - sys,flavcomp-jes: 0.014412378617811942 - sys,detNP2-jes: 0.0013372263622214522 - sys,detNP3-jes: 0.0005841048323941171 - sys,jetvxfrac: 0.0031385018809770284 - sys,ltrig-eff: 0.009149731959999999 - sys,btag-jes: 0.006973936388943911 - sys,mup-scale: 8.090598999999999e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.004153483860254841 - sys,laltpar-ejet-fake: 0.00297145636 - sys,statNP1-jes: 0.00605681278542609 - sys,muid-res: 1.471018e-05 - sys,pdf: 0.00336127613 - sys,isr-fsr: 0.054766142381684736 - sys,zjet-xsec: 0.00489113485 - sys,ps-model: 0.036164977529999996 - sys,flavres-jes: 0.010448631508692815 - sys,laltfakecr-mujet-fake: 0.00097822697 - sys,mums-res: 7.35509e-06 - sys,mod-NP2-jes: 0.0005186251235440424 - sys,lid-eff: 0.00991466132 - sys,mixNP2-jes: 0.002743941494216021 - sys,mixNP1-jes: 0.0047438491691863495 - sys,btag-eff: 0.03082799001821557 - sys,pileoffrho-jes: 0.014582045602159867 - sys,modNP4-jes: 0.0005423225623889464 - sys,mcstat: 0.00131656111 - sys,modNP3-jes: 0.0014067078323907172 - sys,mod-NP1-jes: 0.020899504412826748 + syst_singletop-xsec: 0.001939862285678121 + syst_wjet-scale: 0.00223594736 + syst_laltrealcr-mujet-fake: 0.00312591325 + syst_eta-jes: 0.004729122688352804 + syst_statNP3-jes: 0.002622961116324107 + syst_laltrealcr-ejet-fake: 0.0006251826500000001 + syst_pileoffmu-jes: 0.0015675274034024358 + syst_lstat-ejet-fake: 0.003484223048555126 + syst_lstat-mujet-fake: 0.00021019992797498928 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.02597817788 + syst_statNP2-jes: 0.0002132659046694644 + syst_elen-scale: 0.0005484846007664609 + syst_punch-jes: 4.823055051570841e-05 + syst_pileoffnpv-jes: 0.005319194782471096 + syst_lrec-eff: 0.00190496831 + syst_pileoffpt-jes: 0.00030574534978180254 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.0035892839199999998 + syst_laltfakecr-ejet-fake: 0.00305971744 + syst_laltpar-mujet-fake: 0.0025963467699999998 + syst_jetrec-eff: 0.00023536288 + syst_c/tautag-eff: 0.006101049371723527 + syst_dibos-xsec: 0.00030155869 + syst_elen-res: 0.00015445689 + syst_flavcomp-jes: 0.014412378617811942 + syst_detNP2-jes: 0.0013372263622214522 + syst_detNP3-jes: 0.0005841048323941171 + syst_jetvxfrac: 0.0031385018809770284 + syst_ltrig-eff: 0.009149731959999999 + syst_btag-jes: 0.006973936388943911 + syst_mup-scale: 8.090598999999999e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.004153483860254841 + syst_laltpar-ejet-fake: 0.00297145636 + syst_statNP1-jes: 0.00605681278542609 + syst_muid-res: 1.471018e-05 + syst_pdf: 0.00336127613 + syst_isr-fsr: 0.054766142381684736 + syst_zjet-xsec: 0.00489113485 + syst_ps-model: 0.036164977529999996 + syst_flavres-jes: 0.010448631508692815 + syst_laltfakecr-mujet-fake: 0.00097822697 + syst_mums-res: 7.35509e-06 + syst_mod-NP2-jes: 0.0005186251235440424 + syst_lid-eff: 0.00991466132 + syst_mixNP2-jes: 0.002743941494216021 + syst_mixNP1-jes: 0.0047438491691863495 + syst_btag-eff: 0.03082799001821557 + syst_pileoffrho-jes: 0.014582045602159867 + syst_modNP4-jes: 0.0005423225623889464 + syst_mcstat: 0.00131656111 + syst_modNP3-jes: 0.0014067078323907172 + syst_mod-NP1-jes: 0.020899504412826748 lumi: 0.020594251999999997 - ArtUnc_1: -0.00043306809525466363 ArtUnc_2: 0.0006091601014372206 @@ -673,61 +673,61 @@ bins: ArtUnc_23: 3.308669833661444e-05 ArtUnc_24: -3.04235464282074e-06 ArtUnc_25: -1.2300154918761493e-06 - sys,singletop-xsec: 0.0009253184231595801 - sys,wjet-scale: 0.0008718331500000001 - sys,laltrealcr-mujet-fake: 0.00154055105 - sys,eta-jes: 0.001961079280759487 - sys,statNP3-jes: 0.0012952672872833843 - sys,laltrealcr-ejet-fake: 0.0001374934 - sys,pileoffmu-jes: 0.0005712816379795845 - sys,lstat-ejet-fake: 0.001220495966840127 - sys,lstat-mujet-fake: 0.00034639353382602284 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.008902697650000001 - sys,statNP2-jes: 7.306738604142669e-05 - sys,elen-scale: 0.00018356332822289665 - sys,punch-jes: 6.271146545638106e-05 - sys,pileoffnpv-jes: 0.0019462592086471528 - sys,lrec-eff: 0.0008624586000000001 - sys,pileoffpt-jes: 0.00020507683720841688 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.0014249316000000001 - sys,laltfakecr-ejet-fake: 0.0010187011000000001 - sys,laltpar-mujet-fake: 0.00116556905 - sys,jetrec-eff: 3.74982e-05 - sys,c/tautag-eff: 0.0021186529089182913 - sys,dibos-xsec: 0.00011874430000000001 - sys,elen-res: 4.967921870673768e-05 - sys,flavcomp-jes: 0.005376159577487889 - sys,detNP2-jes: 0.0003188993248267498 - sys,detNP3-jes: 0.00034547262469787225 - sys,jetvxfrac: 0.000724013314172113 - sys,ltrig-eff: 0.0038623146 - sys,btag-jes: 0.002839862493739574 - sys,mup-scale: 3.9772847253434096e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.0029850692885960927 - sys,laltpar-ejet-fake: 0.0008718331500000001 - sys,statNP1-jes: 0.001673475337424679 - sys,muid-res: 1.2499400000000003e-05 - sys,pdf: 0.0012374406000000002 - sys,isr-fsr: 0.02335981711552737 - sys,zjet-xsec: 0.0014468055500000002 - sys,ps-model: 0.016696073550000003 - sys,flavres-jes: 0.004592867015914119 - sys,laltfakecr-mujet-fake: 0.0004124802 - sys,mums-res: 6.249700000000001e-06 - sys,mod-NP2-jes: 5.659344049781741e-05 - sys,lid-eff: 0.00430916815 - sys,mixNP2-jes: 0.00084249775045763 - sys,mixNP1-jes: 0.002671094289367193 - sys,btag-eff: 0.014461897880825858 - sys,pileoffrho-jes: 0.005396644900472434 - sys,modNP4-jes: 0.0004916725819309553 - sys,mcstat: 0.0007562137000000001 - sys,modNP3-jes: 0.0009443666876827932 - sys,mod-NP1-jes: 0.007882459210475446 + syst_singletop-xsec: 0.0009253184231595801 + syst_wjet-scale: 0.0008718331500000001 + syst_laltrealcr-mujet-fake: 0.00154055105 + syst_eta-jes: 0.001961079280759487 + syst_statNP3-jes: 0.0012952672872833843 + syst_laltrealcr-ejet-fake: 0.0001374934 + syst_pileoffmu-jes: 0.0005712816379795845 + syst_lstat-ejet-fake: 0.001220495966840127 + syst_lstat-mujet-fake: 0.00034639353382602284 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.008902697650000001 + syst_statNP2-jes: 7.306738604142669e-05 + syst_elen-scale: 0.00018356332822289665 + syst_punch-jes: 6.271146545638106e-05 + syst_pileoffnpv-jes: 0.0019462592086471528 + syst_lrec-eff: 0.0008624586000000001 + syst_pileoffpt-jes: 0.00020507683720841688 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.0014249316000000001 + syst_laltfakecr-ejet-fake: 0.0010187011000000001 + syst_laltpar-mujet-fake: 0.00116556905 + syst_jetrec-eff: 3.74982e-05 + syst_c/tautag-eff: 0.0021186529089182913 + syst_dibos-xsec: 0.00011874430000000001 + syst_elen-res: 4.967921870673768e-05 + syst_flavcomp-jes: 0.005376159577487889 + syst_detNP2-jes: 0.0003188993248267498 + syst_detNP3-jes: 0.00034547262469787225 + syst_jetvxfrac: 0.000724013314172113 + syst_ltrig-eff: 0.0038623146 + syst_btag-jes: 0.002839862493739574 + syst_mup-scale: 3.9772847253434096e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.0029850692885960927 + syst_laltpar-ejet-fake: 0.0008718331500000001 + syst_statNP1-jes: 0.001673475337424679 + syst_muid-res: 1.2499400000000003e-05 + syst_pdf: 0.0012374406000000002 + syst_isr-fsr: 0.02335981711552737 + syst_zjet-xsec: 0.0014468055500000002 + syst_ps-model: 0.016696073550000003 + syst_flavres-jes: 0.004592867015914119 + syst_laltfakecr-mujet-fake: 0.0004124802 + syst_mums-res: 6.249700000000001e-06 + syst_mod-NP2-jes: 5.659344049781741e-05 + syst_lid-eff: 0.00430916815 + syst_mixNP2-jes: 0.00084249775045763 + syst_mixNP1-jes: 0.002671094289367193 + syst_btag-eff: 0.014461897880825858 + syst_pileoffrho-jes: 0.005396644900472434 + syst_modNP4-jes: 0.0004916725819309553 + syst_mcstat: 0.0007562137000000001 + syst_modNP3-jes: 0.0009443666876827932 + syst_mod-NP1-jes: 0.007882459210475446 lumi: 0.00874958 - ArtUnc_1: -0.00016856818719132824 ArtUnc_2: 0.0002281155245664124 @@ -754,61 +754,61 @@ bins: ArtUnc_23: 3.879599674603305e-05 ArtUnc_24: -2.2654801821210144e-06 ArtUnc_25: -1.4817764461118702e-06 - sys,singletop-xsec: 0.0003829948282197537 - sys,wjet-scale: 0.00042778884 - sys,laltrealcr-mujet-fake: 0.0003679904 - sys,eta-jes: 0.000662831774815453 - sys,statNP3-jes: 0.00041913843045413956 - sys,laltrealcr-ejet-fake: 2.184943e-05 - sys,pileoffmu-jes: 0.0001432024354924046 - sys,lstat-ejet-fake: 0.0003077340991793072 - sys,lstat-mujet-fake: 0.00012847151713310883 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.0018169526000000004 - sys,statNP2-jes: 7.532742407059779e-05 - sys,elen-scale: 5.739202919824037e-05 - sys,punch-jes: 3.954877073076324e-05 - sys,pileoffnpv-jes: 0.0006313188671885613 - sys,lrec-eff: 0.00033234133 - sys,pileoffpt-jes: 4.5811548745139584e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.0004852900650397424 - sys,laltfakecr-ejet-fake: 0.00015869586000000002 - sys,laltpar-mujet-fake: 0.00040018956 - sys,jetrec-eff: 3.44991e-06 - sys,c/tautag-eff: 0.0006393853882880218 - sys,dibos-xsec: 2.4149370000000004e-05 - sys,elen-res: 1.520180309724064e-05 - sys,flavcomp-jes: 0.0015780182324636504 - sys,detNP2-jes: 9.395332315042135e-06 - sys,detNP3-jes: 0.00019084219027258275 - sys,jetvxfrac: 0.0001155977277300219 - sys,ltrig-eff: 0.0014202129500000002 - sys,btag-jes: 0.000983005091505954 - sys,mup-scale: 1.195083880307989e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.0015159888159031185 - sys,laltpar-ejet-fake: 0.00021504439 - sys,statNP1-jes: 0.000368960755441411 - sys,muid-res: 2.2999400000000005e-06 - sys,pdf: 0.0001724955 - sys,isr-fsr: 0.008440807452503068 - sys,zjet-xsec: 0.00042318896000000003 - sys,ps-model: 0.00498856986 - sys,flavres-jes: 0.0015892718536345382 - sys,laltfakecr-mujet-fake: 0.00013339652000000002 - sys,mums-res: 8.049790000000001e-06 - sys,mod-NP2-jes: 0.00015484400495197634 - sys,lid-eff: 0.00160420815 - sys,mixNP2-jes: 0.0002420700507562422 - sys,mixNP1-jes: 0.001155692244669068 - sys,btag-eff: 0.006130308544192053 - sys,pileoffrho-jes: 0.0016518181248990225 - sys,modNP4-jes: 0.00030599359822833336 - sys,mcstat: 0.00043008878 - sys,modNP3-jes: 0.0003193543505581361 - sys,mod-NP1-jes: 0.002415398244513266 + syst_singletop-xsec: 0.0003829948282197537 + syst_wjet-scale: 0.00042778884 + syst_laltrealcr-mujet-fake: 0.0003679904 + syst_eta-jes: 0.000662831774815453 + syst_statNP3-jes: 0.00041913843045413956 + syst_laltrealcr-ejet-fake: 2.184943e-05 + syst_pileoffmu-jes: 0.0001432024354924046 + syst_lstat-ejet-fake: 0.0003077340991793072 + syst_lstat-mujet-fake: 0.00012847151713310883 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.0018169526000000004 + syst_statNP2-jes: 7.532742407059779e-05 + syst_elen-scale: 5.739202919824037e-05 + syst_punch-jes: 3.954877073076324e-05 + syst_pileoffnpv-jes: 0.0006313188671885613 + syst_lrec-eff: 0.00033234133 + syst_pileoffpt-jes: 4.5811548745139584e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.0004852900650397424 + syst_laltfakecr-ejet-fake: 0.00015869586000000002 + syst_laltpar-mujet-fake: 0.00040018956 + syst_jetrec-eff: 3.44991e-06 + syst_c/tautag-eff: 0.0006393853882880218 + syst_dibos-xsec: 2.4149370000000004e-05 + syst_elen-res: 1.520180309724064e-05 + syst_flavcomp-jes: 0.0015780182324636504 + syst_detNP2-jes: 9.395332315042135e-06 + syst_detNP3-jes: 0.00019084219027258275 + syst_jetvxfrac: 0.0001155977277300219 + syst_ltrig-eff: 0.0014202129500000002 + syst_btag-jes: 0.000983005091505954 + syst_mup-scale: 1.195083880307989e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.0015159888159031185 + syst_laltpar-ejet-fake: 0.00021504439 + syst_statNP1-jes: 0.000368960755441411 + syst_muid-res: 2.2999400000000005e-06 + syst_pdf: 0.0001724955 + syst_isr-fsr: 0.008440807452503068 + syst_zjet-xsec: 0.00042318896000000003 + syst_ps-model: 0.00498856986 + syst_flavres-jes: 0.0015892718536345382 + syst_laltfakecr-mujet-fake: 0.00013339652000000002 + syst_mums-res: 8.049790000000001e-06 + syst_mod-NP2-jes: 0.00015484400495197634 + syst_lid-eff: 0.00160420815 + syst_mixNP2-jes: 0.0002420700507562422 + syst_mixNP1-jes: 0.001155692244669068 + syst_btag-eff: 0.006130308544192053 + syst_pileoffrho-jes: 0.0016518181248990225 + syst_modNP4-jes: 0.00030599359822833336 + syst_mcstat: 0.00043008878 + syst_modNP3-jes: 0.0003193543505581361 + syst_mod-NP1-jes: 0.002415398244513266 lumi: 0.0032199159999999998 - ArtUnc_1: -5.704825393762075e-05 ArtUnc_2: 8.447043453136964e-05 @@ -835,61 +835,61 @@ bins: ArtUnc_23: 5.46528425444966e-05 ArtUnc_24: 6.8187558192396355e-06 ArtUnc_25: -2.2140935057804415e-06 - sys,singletop-xsec: 0.00014993674589952778 - sys,wjet-scale: 0.000198005512 - sys,laltrealcr-mujet-fake: 6.1268099e-05 - sys,eta-jes: 0.00022478155862948624 - sys,statNP3-jes: 0.00011839999850896705 - sys,laltrealcr-ejet-fake: 2.028745e-05 - sys,pileoffmu-jes: 3.781256328101726e-05 - sys,lstat-ejet-fake: 8.433334597443171e-05 - sys,lstat-mujet-fake: 5.551945276650089e-05 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 5.1530123e-05 - sys,statNP2-jes: 3.6721405336485164e-05 - sys,elen-scale: 1.8860783025908835e-05 - sys,punch-jes: 3.895348892478654e-05 - sys,pileoffnpv-jes: 0.0001907145473774875 - sys,lrec-eff: 0.00012010170399999999 - sys,pileoffpt-jes: 7.730556714322906e-06 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.00017995173996680593 - sys,laltfakecr-ejet-fake: 1.7447206999999997e-05 - sys,laltpar-mujet-fake: 9.0887776e-05 - sys,jetrec-eff: 8.11498e-06 - sys,c/tautag-eff: 0.00019090512009445844 - sys,dibos-xsec: 1.3795465999999999e-05 - sys,elen-res: 7.3484269288282365e-06 - sys,flavcomp-jes: 0.0005367438189682988 - sys,detNP2-jes: 3.348535655335121e-05 - sys,detNP3-jes: 8.734707350608027e-05 - sys,jetvxfrac: 6.504651297898834e-06 - sys,ltrig-eff: 0.000501911513 - sys,btag-jes: 0.000362132005395194 - sys,mup-scale: 2.8111115324810575e-06 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.0007627419670950791 - sys,laltpar-ejet-fake: 4.5443888e-05 - sys,statNP1-jes: 0.00011572598824131657 - sys,muid-res: 1.6229959999999999e-06 - sys,pdf: 3.7734657e-05 - sys,isr-fsr: 0.002857035218600734 - sys,zjet-xsec: 0.000250752882 - sys,ps-model: 0.001699682561 - sys,flavres-jes: 0.0006201654128369569 - sys,laltfakecr-mujet-fake: 3.2865669e-05 - sys,mums-res: 4.0574899999999996e-07 - sys,mod-NP2-jes: 9.60560948055265e-05 - sys,lid-eff: 0.0005668313530000001 - sys,mixNP2-jes: 5.498572661673769e-05 - sys,mixNP1-jes: 0.0005481696019844535 - sys,btag-eff: 0.0024693139016654774 - sys,pileoffrho-jes: 0.0005905133766451836 - sys,modNP4-jes: 0.00014546879739196487 - sys,mcstat: 0.000225190695 - sys,modNP3-jes: 0.00010673647569344536 - sys,mod-NP1-jes: 0.0008546317250597773 + syst_singletop-xsec: 0.00014993674589952778 + syst_wjet-scale: 0.000198005512 + syst_laltrealcr-mujet-fake: 6.1268099e-05 + syst_eta-jes: 0.00022478155862948624 + syst_statNP3-jes: 0.00011839999850896705 + syst_laltrealcr-ejet-fake: 2.028745e-05 + syst_pileoffmu-jes: 3.781256328101726e-05 + syst_lstat-ejet-fake: 8.433334597443171e-05 + syst_lstat-mujet-fake: 5.551945276650089e-05 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 5.1530123e-05 + syst_statNP2-jes: 3.6721405336485164e-05 + syst_elen-scale: 1.8860783025908835e-05 + syst_punch-jes: 3.895348892478654e-05 + syst_pileoffnpv-jes: 0.0001907145473774875 + syst_lrec-eff: 0.00012010170399999999 + syst_pileoffpt-jes: 7.730556714322906e-06 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.00017995173996680593 + syst_laltfakecr-ejet-fake: 1.7447206999999997e-05 + syst_laltpar-mujet-fake: 9.0887776e-05 + syst_jetrec-eff: 8.11498e-06 + syst_c/tautag-eff: 0.00019090512009445844 + syst_dibos-xsec: 1.3795465999999999e-05 + syst_elen-res: 7.3484269288282365e-06 + syst_flavcomp-jes: 0.0005367438189682988 + syst_detNP2-jes: 3.348535655335121e-05 + syst_detNP3-jes: 8.734707350608027e-05 + syst_jetvxfrac: 6.504651297898834e-06 + syst_ltrig-eff: 0.000501911513 + syst_btag-jes: 0.000362132005395194 + syst_mup-scale: 2.8111115324810575e-06 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.0007627419670950791 + syst_laltpar-ejet-fake: 4.5443888e-05 + syst_statNP1-jes: 0.00011572598824131657 + syst_muid-res: 1.6229959999999999e-06 + syst_pdf: 3.7734657e-05 + syst_isr-fsr: 0.002857035218600734 + syst_zjet-xsec: 0.000250752882 + syst_ps-model: 0.001699682561 + syst_flavres-jes: 0.0006201654128369569 + syst_laltfakecr-mujet-fake: 3.2865669e-05 + syst_mums-res: 4.0574899999999996e-07 + syst_mod-NP2-jes: 9.60560948055265e-05 + syst_lid-eff: 0.0005668313530000001 + syst_mixNP2-jes: 5.498572661673769e-05 + syst_mixNP1-jes: 0.0005481696019844535 + syst_btag-eff: 0.0024693139016654774 + syst_pileoffrho-jes: 0.0005905133766451836 + syst_modNP4-jes: 0.00014546879739196487 + syst_mcstat: 0.000225190695 + syst_modNP3-jes: 0.00010673647569344536 + syst_mod-NP1-jes: 0.0008546317250597773 lumi: 0.0011360971999999998 - ArtUnc_1: -1.9686456265600054e-05 ArtUnc_2: 2.925753723137182e-05 @@ -916,59 +916,59 @@ bins: ArtUnc_23: 6.265344865287837e-05 ArtUnc_24: -1.2464455732653925e-05 ArtUnc_25: -3.21890037025863e-06 - sys,singletop-xsec: 5.628830002789741e-05 - sys,wjet-scale: 3.1888122e-05 - sys,laltrealcr-mujet-fake: 1.518482e-05 - sys,eta-jes: 5.2705736416055764e-05 - sys,statNP3-jes: 3.486891459456297e-05 - sys,laltrealcr-ejet-fake: 6.0739279999999995e-06 - sys,pileoffmu-jes: 7.420605356283054e-06 - sys,lstat-ejet-fake: 6.387356509205667e-05 - sys,lstat-mujet-fake: 2.442224547637461e-05 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.000459015416 - sys,statNP2-jes: 1.3909824738196767e-05 - sys,elen-scale: 4.863933715014936e-06 - sys,punch-jes: 8.026811624762395e-06 - sys,pileoffnpv-jes: 5.496795263027798e-05 - sys,lrec-eff: 3.2647363e-05 - sys,pileoffpt-jes: 2.069001023593686e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 5.6889308782654e-05 - sys,laltfakecr-ejet-fake: 2.0174118e-05 - sys,laltpar-mujet-fake: 2.0282581e-05 - sys,jetrec-eff: 3.25389e-06 - sys,c/tautag-eff: 5.135728776659954e-05 - sys,dibos-xsec: 9.110892e-06 - sys,elen-res: 1.301556e-06 - sys,flavcomp-jes: 9.56473172207676e-05 - sys,detNP2-jes: 1.782847674739541e-05 - sys,detNP3-jes: 2.7259192531935843e-05 - sys,jetvxfrac: 1.24250509112996e-05 - sys,ltrig-eff: 0.00013460258300000002 - sys,btag-jes: 0.00010216171079685841 - sys,mup-scale: 1.2211122738187302e-06 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.0003187883627054941 - sys,laltpar-ejet-fake: 2.820038e-05 - sys,statNP1-jes: 1.258684959166999e-05 - sys,muid-res: 1.08463e-06 - sys,pdf: 6.50778e-06 - sys,isr-fsr: 0.0007523633136503267 - sys,zjet-xsec: 4.8699887e-05 - sys,ps-model: 0.000490144297 - sys,flavres-jes: 0.00015691657504507362 - sys,laltfakecr-mujet-fake: 2.1367210999999998e-05 - sys,mums-res: 1.843871e-06 - sys,mod-NP2-jes: 7.445358871766965e-05 - sys,lid-eff: 0.000151522811 - sys,mixNP2-jes: 7.890263923136411e-06 - sys,mixNP1-jes: 0.00020870316191370404 - sys,btag-eff: 0.0007394309627349159 - sys,pileoffrho-jes: 0.00010819643644933498 - sys,modNP4-jes: 4.905813983229777e-05 - sys,mcstat: 0.00011909237400000001 - sys,modNP3-jes: 1.6566458391774656e-05 - sys,mod-NP1-jes: 0.00020172079113294416 + syst_singletop-xsec: 5.628830002789741e-05 + syst_wjet-scale: 3.1888122e-05 + syst_laltrealcr-mujet-fake: 1.518482e-05 + syst_eta-jes: 5.2705736416055764e-05 + syst_statNP3-jes: 3.486891459456297e-05 + syst_laltrealcr-ejet-fake: 6.0739279999999995e-06 + syst_pileoffmu-jes: 7.420605356283054e-06 + syst_lstat-ejet-fake: 6.387356509205667e-05 + syst_lstat-mujet-fake: 2.442224547637461e-05 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.000459015416 + syst_statNP2-jes: 1.3909824738196767e-05 + syst_elen-scale: 4.863933715014936e-06 + syst_punch-jes: 8.026811624762395e-06 + syst_pileoffnpv-jes: 5.496795263027798e-05 + syst_lrec-eff: 3.2647363e-05 + syst_pileoffpt-jes: 2.069001023593686e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 5.6889308782654e-05 + syst_laltfakecr-ejet-fake: 2.0174118e-05 + syst_laltpar-mujet-fake: 2.0282581e-05 + syst_jetrec-eff: 3.25389e-06 + syst_c/tautag-eff: 5.135728776659954e-05 + syst_dibos-xsec: 9.110892e-06 + syst_elen-res: 1.301556e-06 + syst_flavcomp-jes: 9.56473172207676e-05 + syst_detNP2-jes: 1.782847674739541e-05 + syst_detNP3-jes: 2.7259192531935843e-05 + syst_jetvxfrac: 1.24250509112996e-05 + syst_ltrig-eff: 0.00013460258300000002 + syst_btag-jes: 0.00010216171079685841 + syst_mup-scale: 1.2211122738187302e-06 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.0003187883627054941 + syst_laltpar-ejet-fake: 2.820038e-05 + syst_statNP1-jes: 1.258684959166999e-05 + syst_muid-res: 1.08463e-06 + syst_pdf: 6.50778e-06 + syst_isr-fsr: 0.0007523633136503267 + syst_zjet-xsec: 4.8699887e-05 + syst_ps-model: 0.000490144297 + syst_flavres-jes: 0.00015691657504507362 + syst_laltfakecr-mujet-fake: 2.1367210999999998e-05 + syst_mums-res: 1.843871e-06 + syst_mod-NP2-jes: 7.445358871766965e-05 + syst_lid-eff: 0.000151522811 + syst_mixNP2-jes: 7.890263923136411e-06 + syst_mixNP1-jes: 0.00020870316191370404 + syst_btag-eff: 0.0007394309627349159 + syst_pileoffrho-jes: 0.00010819643644933498 + syst_modNP4-jes: 4.905813983229777e-05 + syst_mcstat: 0.00011909237400000001 + syst_modNP3-jes: 1.6566458391774656e-05 + syst_mod-NP1-jes: 0.00020172079113294416 lumi: 0.0003036964 diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dpTt_norm.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dpTt_norm.yaml index e78d4a727e..135f3b12e8 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dpTt_norm.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dpTt_norm.yaml @@ -99,223 +99,223 @@ definitions: definition: artificial uncertainty 25 treatment: ADD type: ATLAS8TEVTTB151104716unc25 - sys,singletop-xsec: + syst_singletop-xsec: definition: '' treatment: MULT type: CORR - sys,wjet-scale: + syst_wjet-scale: definition: '' treatment: MULT type: CORR - sys,laltrealcr-mujet-fake: + syst_laltrealcr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,eta-jes: + syst_eta-jes: definition: '' treatment: MULT type: CORR - sys,statNP3-jes: + syst_statNP3-jes: definition: '' treatment: MULT type: CORR - sys,laltrealcr-ejet-fake: + syst_laltrealcr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,pileoffmu-jes: + syst_pileoffmu-jes: definition: '' treatment: MULT type: CORR - sys,lstat-ejet-fake: + syst_lstat-ejet-fake: definition: '' treatment: MULT type: CORR - sys,lstat-mujet-fake: + syst_lstat-mujet-fake: definition: '' treatment: MULT type: CORR - sys,etmsoft-scale: + syst_etmsoft-scale: definition: '' treatment: MULT type: CORR - sys,hardscat-model: + syst_hardscat-model: definition: '' treatment: MULT type: CORR - sys,statNP2-jes: + syst_statNP2-jes: definition: '' treatment: MULT type: CORR - sys,elen-scale: + syst_elen-scale: definition: '' treatment: MULT type: CORR - sys,punch-jes: + syst_punch-jes: definition: '' treatment: MULT type: CORR - sys,pileoffnpv-jes: + syst_pileoffnpv-jes: definition: '' treatment: MULT type: CORR - sys,lrec-eff: + syst_lrec-eff: definition: '' treatment: MULT type: CORR - sys,pileoffpt-jes: + syst_pileoffpt-jes: definition: '' treatment: MULT type: CORR - sys,jeten-res: + syst_jeten-res: definition: '' treatment: MULT type: CORR - sys,lighttag-eff: + syst_lighttag-eff: definition: '' treatment: MULT type: CORR - sys,laltfakecr-ejet-fake: + syst_laltfakecr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,laltpar-mujet-fake: + syst_laltpar-mujet-fake: definition: '' treatment: MULT type: CORR - sys,jetrec-eff: + syst_jetrec-eff: definition: '' treatment: MULT type: CORR - sys,c/tautag-eff: + syst_c/tautag-eff: definition: '' treatment: MULT type: CORR - sys,dibos-xsec: + syst_dibos-xsec: definition: '' treatment: MULT type: CORR - sys,elen-res: + syst_elen-res: definition: '' treatment: MULT type: CORR - sys,flavcomp-jes: + syst_flavcomp-jes: definition: '' treatment: MULT type: CORR - sys,detNP2-jes: + syst_detNP2-jes: definition: '' treatment: MULT type: CORR - sys,detNP3-jes: + syst_detNP3-jes: definition: '' treatment: MULT type: CORR - sys,jetvxfrac: + syst_jetvxfrac: definition: '' treatment: MULT type: CORR - sys,ltrig-eff: + syst_ltrig-eff: definition: '' treatment: MULT type: CORR - sys,btag-jes: + syst_btag-jes: definition: '' treatment: MULT type: CORR - sys,mup-scale: + syst_mup-scale: definition: '' treatment: MULT type: CORR - sys,singlephpt-jes: + syst_singlephpt-jes: definition: '' treatment: MULT type: CORR - sys,etmsoft-res: + syst_etmsoft-res: definition: '' treatment: MULT type: CORR - sys,detNP1-jes: + syst_detNP1-jes: definition: '' treatment: MULT type: CORR - sys,laltpar-ejet-fake: + syst_laltpar-ejet-fake: definition: '' treatment: MULT type: CORR - sys,statNP1-jes: + syst_statNP1-jes: definition: '' treatment: MULT type: CORR - sys,muid-res: + syst_muid-res: definition: '' treatment: MULT type: CORR - sys,pdf: + syst_pdf: definition: '' treatment: MULT type: CORR - sys,isr-fsr: + syst_isr-fsr: definition: '' treatment: MULT type: CORR - sys,zjet-xsec: + syst_zjet-xsec: definition: '' treatment: MULT type: CORR - sys,ps-model: + syst_ps-model: definition: '' treatment: MULT type: CORR - sys,flavres-jes: + syst_flavres-jes: definition: '' treatment: MULT type: CORR - sys,laltfakecr-mujet-fake: + syst_laltfakecr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,mums-res: + syst_mums-res: definition: '' treatment: MULT type: CORR - sys,mod-NP2-jes: + syst_mod-NP2-jes: definition: '' treatment: MULT type: CORR - sys,lid-eff: + syst_lid-eff: definition: '' treatment: MULT type: CORR - sys,mixNP2-jes: + syst_mixNP2-jes: definition: '' treatment: MULT type: CORR - sys,mixNP1-jes: + syst_mixNP1-jes: definition: '' treatment: MULT type: CORR - sys,btag-eff: + syst_btag-eff: definition: '' treatment: MULT type: CORR - sys,pileoffrho-jes: + syst_pileoffrho-jes: definition: '' treatment: MULT type: CORR - sys,modNP4-jes: + syst_modNP4-jes: definition: '' treatment: MULT type: CORR - sys,mcstat: + syst_mcstat: definition: '' treatment: MULT type: CORR - sys,modNP3-jes: + syst_modNP3-jes: definition: '' treatment: MULT type: CORR - sys,mod-NP1-jes: + syst_mod-NP1-jes: definition: '' treatment: MULT type: CORR @@ -345,61 +345,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 2.545880388475473e-10 - sys,singletop-xsec: 1.9209774703870815e-07 - sys,wjet-scale: 5.599165790908809e-06 - sys,laltrealcr-mujet-fake: 9.1126916e-06 - sys,eta-jes: 8.110405678315356e-06 - sys,statNP3-jes: 1.3571776858687582e-05 - sys,laltrealcr-ejet-fake: 2.0078812000000003e-06 - sys,pileoffmu-jes: 7.35678361415116e-07 - sys,lstat-ejet-fake: 4.9156690512737296e-06 - sys,lstat-mujet-fake: 3.343992551886891e-07 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 6.39432936e-05 - sys,statNP2-jes: 2.5508037143978843e-06 - sys,elen-scale: 2.154389531487268e-06 - sys,punch-jes: 4.385605116944411e-07 - sys,pileoffnpv-jes: 2.60917148357314e-06 - sys,lrec-eff: 6.950358000000001e-07 - sys,pileoffpt-jes: 1.3048714279263764e-06 - sys,jeten-res: 0.0 - sys,lighttag-eff: 1.8343254655842479e-06 - sys,laltfakecr-ejet-fake: 5.405834000000001e-07 - sys,laltpar-mujet-fake: 3.2821135000000006e-06 - sys,jetrec-eff: 1.1970061e-06 - sys,c/tautag-eff: 5.116891402139615e-06 - sys,dibos-xsec: 1.8148157000000002e-06 - sys,elen-res: 5.991255182389643e-07 - sys,flavcomp-jes: 2.2070350842453486e-06 - sys,detNP2-jes: 1.0811668000000002e-06 - sys,detNP3-jes: 2.2217636836188966e-06 - sys,jetvxfrac: 1.1501568277857779e-05 - sys,ltrig-eff: 1.2938269753738822e-06 - sys,btag-jes: 1.906451183432876e-05 - sys,mup-scale: 2.64013100494592e-07 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 1.9871458268681713e-05 - sys,laltpar-ejet-fake: 5.6761257e-06 - sys,statNP1-jes: 6.034123138761332e-06 - sys,muid-res: 0.0 - sys,pdf: 5.1741554e-06 - sys,isr-fsr: 4.967394852131114e-05 - sys,zjet-xsec: 1.5831371e-05 - sys,ps-model: 0.00011939170520000001 - sys,flavres-jes: 9.34452975690223e-06 - sys,laltfakecr-mujet-fake: 3.6296314000000003e-06 - sys,mums-res: 1.5445240000000003e-07 - sys,mod-NP2-jes: 1.1635300229825961e-06 - sys,lid-eff: 1.4868550632744027e-06 - sys,mixNP2-jes: 1.957878368267373e-06 - sys,mixNP1-jes: 1.5813184143109177e-05 - sys,btag-eff: 5.31105610089902e-06 - sys,pileoffrho-jes: 9.295737935621312e-06 - sys,modNP4-jes: 2.841344379725071e-06 - sys,mcstat: 4.6721851000000005e-06 - sys,modNP3-jes: 1.3560333044910071e-05 - sys,mod-NP1-jes: 2.0969295372512035e-05 + syst_singletop-xsec: 1.9209774703870815e-07 + syst_wjet-scale: 5.599165790908809e-06 + syst_laltrealcr-mujet-fake: 9.1126916e-06 + syst_eta-jes: 8.110405678315356e-06 + syst_statNP3-jes: 1.3571776858687582e-05 + syst_laltrealcr-ejet-fake: 2.0078812000000003e-06 + syst_pileoffmu-jes: 7.35678361415116e-07 + syst_lstat-ejet-fake: 4.9156690512737296e-06 + syst_lstat-mujet-fake: 3.343992551886891e-07 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 6.39432936e-05 + syst_statNP2-jes: 2.5508037143978843e-06 + syst_elen-scale: 2.154389531487268e-06 + syst_punch-jes: 4.385605116944411e-07 + syst_pileoffnpv-jes: 2.60917148357314e-06 + syst_lrec-eff: 6.950358000000001e-07 + syst_pileoffpt-jes: 1.3048714279263764e-06 + syst_jeten-res: 0.0 + syst_lighttag-eff: 1.8343254655842479e-06 + syst_laltfakecr-ejet-fake: 5.405834000000001e-07 + syst_laltpar-mujet-fake: 3.2821135000000006e-06 + syst_jetrec-eff: 1.1970061e-06 + syst_c/tautag-eff: 5.116891402139615e-06 + syst_dibos-xsec: 1.8148157000000002e-06 + syst_elen-res: 5.991255182389643e-07 + syst_flavcomp-jes: 2.2070350842453486e-06 + syst_detNP2-jes: 1.0811668000000002e-06 + syst_detNP3-jes: 2.2217636836188966e-06 + syst_jetvxfrac: 1.1501568277857779e-05 + syst_ltrig-eff: 1.2938269753738822e-06 + syst_btag-jes: 1.906451183432876e-05 + syst_mup-scale: 2.64013100494592e-07 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 1.9871458268681713e-05 + syst_laltpar-ejet-fake: 5.6761257e-06 + syst_statNP1-jes: 6.034123138761332e-06 + syst_muid-res: 0.0 + syst_pdf: 5.1741554e-06 + syst_isr-fsr: 4.967394852131114e-05 + syst_zjet-xsec: 1.5831371e-05 + syst_ps-model: 0.00011939170520000001 + syst_flavres-jes: 9.34452975690223e-06 + syst_laltfakecr-mujet-fake: 3.6296314000000003e-06 + syst_mums-res: 1.5445240000000003e-07 + syst_mod-NP2-jes: 1.1635300229825961e-06 + syst_lid-eff: 1.4868550632744027e-06 + syst_mixNP2-jes: 1.957878368267373e-06 + syst_mixNP1-jes: 1.5813184143109177e-05 + syst_btag-eff: 5.31105610089902e-06 + syst_pileoffrho-jes: 9.295737935621312e-06 + syst_modNP4-jes: 2.841344379725071e-06 + syst_mcstat: 4.6721851000000005e-06 + syst_modNP3-jes: 1.3560333044910071e-05 + syst_mod-NP1-jes: 2.0969295372512035e-05 - ArtUnc_1: 3.9350062337906806e-08 ArtUnc_2: 1.429819670640598e-07 ArtUnc_3: 9.194336221081832e-08 @@ -425,61 +425,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 2.1177474910067453e-10 - sys,singletop-xsec: 8.035696811233361e-07 - sys,wjet-scale: 2.5608319570947308e-06 - sys,laltrealcr-mujet-fake: 7.7809563e-06 - sys,eta-jes: 8.730267651636686e-06 - sys,statNP3-jes: 1.3439054403279851e-05 - sys,laltrealcr-ejet-fake: 1.1305663000000002e-06 - sys,pileoffmu-jes: 8.001223478485321e-07 - sys,lstat-ejet-fake: 6.565723620984352e-06 - sys,lstat-mujet-fake: 5.183466016566593e-07 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.0001159162977 - sys,statNP2-jes: 2.686625865881351e-06 - sys,elen-scale: 1.6002446956970643e-06 - sys,punch-jes: 5.672357746400763e-07 - sys,pileoffnpv-jes: 2.8542494588624906e-06 - sys,lrec-eff: 8.645506999999999e-07 - sys,pileoffpt-jes: 1.0942872701640589e-06 - sys,jeten-res: 0.0 - sys,lighttag-eff: 1.6625975000000002e-06 - sys,laltfakecr-ejet-fake: 2.660156e-07 - sys,laltpar-mujet-fake: 4.3892574e-06 - sys,jetrec-eff: 1.1305663000000002e-06 - sys,c/tautag-eff: 7.848023772136171e-06 - sys,dibos-xsec: 2.3276365000000002e-06 - sys,elen-res: 3.687819653843007e-07 - sys,flavcomp-jes: 3.455644011044396e-07 - sys,detNP2-jes: 1.8621092e-06 - sys,detNP3-jes: 2.763515130949868e-06 - sys,jetvxfrac: 1.3204122834558706e-05 - sys,ltrig-eff: 9.310546e-07 - sys,btag-jes: 1.69918115221846e-05 - sys,mup-scale: 3.029396818520273e-07 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 2.514005757632507e-05 - sys,laltpar-ejet-fake: 3.1921872000000003e-06 - sys,statNP1-jes: 8.12219294009151e-06 - sys,muid-res: 6.65039e-08 - sys,pdf: 6.65039e-08 - sys,isr-fsr: 3.452037700587666e-05 - sys,zjet-xsec: 1.7756541300000002e-05 - sys,ps-model: 8.53245037e-05 - sys,flavres-jes: 1.2096902197999886e-05 - sys,laltfakecr-mujet-fake: 4.6552730000000004e-06 - sys,mums-res: 0.0 - sys,mod-NP2-jes: 2.202675028365594e-06 - sys,lid-eff: 1.5960936000000001e-06 - sys,mixNP2-jes: 1.960173543678898e-06 - sys,mixNP1-jes: 1.8987387554062416e-05 - sys,btag-eff: 1.0843807072762238e-05 - sys,pileoffrho-jes: 8.214443170159508e-06 - sys,modNP4-jes: 3.959496104713408e-06 - sys,mcstat: 5.2538081e-06 - sys,modNP3-jes: 1.4066282320505692e-05 - sys,mod-NP1-jes: 1.9207628490380945e-05 + syst_singletop-xsec: 8.035696811233361e-07 + syst_wjet-scale: 2.5608319570947308e-06 + syst_laltrealcr-mujet-fake: 7.7809563e-06 + syst_eta-jes: 8.730267651636686e-06 + syst_statNP3-jes: 1.3439054403279851e-05 + syst_laltrealcr-ejet-fake: 1.1305663000000002e-06 + syst_pileoffmu-jes: 8.001223478485321e-07 + syst_lstat-ejet-fake: 6.565723620984352e-06 + syst_lstat-mujet-fake: 5.183466016566593e-07 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.0001159162977 + syst_statNP2-jes: 2.686625865881351e-06 + syst_elen-scale: 1.6002446956970643e-06 + syst_punch-jes: 5.672357746400763e-07 + syst_pileoffnpv-jes: 2.8542494588624906e-06 + syst_lrec-eff: 8.645506999999999e-07 + syst_pileoffpt-jes: 1.0942872701640589e-06 + syst_jeten-res: 0.0 + syst_lighttag-eff: 1.6625975000000002e-06 + syst_laltfakecr-ejet-fake: 2.660156e-07 + syst_laltpar-mujet-fake: 4.3892574e-06 + syst_jetrec-eff: 1.1305663000000002e-06 + syst_c/tautag-eff: 7.848023772136171e-06 + syst_dibos-xsec: 2.3276365000000002e-06 + syst_elen-res: 3.687819653843007e-07 + syst_flavcomp-jes: 3.455644011044396e-07 + syst_detNP2-jes: 1.8621092e-06 + syst_detNP3-jes: 2.763515130949868e-06 + syst_jetvxfrac: 1.3204122834558706e-05 + syst_ltrig-eff: 9.310546e-07 + syst_btag-jes: 1.69918115221846e-05 + syst_mup-scale: 3.029396818520273e-07 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 2.514005757632507e-05 + syst_laltpar-ejet-fake: 3.1921872000000003e-06 + syst_statNP1-jes: 8.12219294009151e-06 + syst_muid-res: 6.65039e-08 + syst_pdf: 6.65039e-08 + syst_isr-fsr: 3.452037700587666e-05 + syst_zjet-xsec: 1.7756541300000002e-05 + syst_ps-model: 8.53245037e-05 + syst_flavres-jes: 1.2096902197999886e-05 + syst_laltfakecr-mujet-fake: 4.6552730000000004e-06 + syst_mums-res: 0.0 + syst_mod-NP2-jes: 2.202675028365594e-06 + syst_lid-eff: 1.5960936000000001e-06 + syst_mixNP2-jes: 1.960173543678898e-06 + syst_mixNP1-jes: 1.8987387554062416e-05 + syst_btag-eff: 1.0843807072762238e-05 + syst_pileoffrho-jes: 8.214443170159508e-06 + syst_modNP4-jes: 3.959496104713408e-06 + syst_mcstat: 5.2538081e-06 + syst_modNP3-jes: 1.4066282320505692e-05 + syst_mod-NP1-jes: 1.9207628490380945e-05 - ArtUnc_1: -3.01501545172999e-08 ArtUnc_2: -1.2919758631823057e-07 ArtUnc_3: 6.682930169032262e-08 @@ -505,61 +505,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 2.56896928050277e-10 - sys,singletop-xsec: 5.661020182722103e-07 - sys,wjet-scale: 3.2157530999999995e-06 - sys,laltrealcr-mujet-fake: 5.3595885e-06 - sys,eta-jes: 3.3758375287599462e-06 - sys,statNP3-jes: 5.79357237996634e-06 - sys,laltrealcr-ejet-fake: 2.2969665e-06 - sys,pileoffmu-jes: 8.552676196346191e-07 - sys,lstat-ejet-fake: 3.1827701450269258e-06 - sys,lstat-mujet-fake: 1.4587696498040077e-06 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 8.493671679999999e-05 - sys,statNP2-jes: 3.34715924802914e-07 - sys,elen-scale: 8.44923576931701e-07 - sys,punch-jes: 1.822624651390063e-07 - sys,pileoffnpv-jes: 3.0166567593811113e-06 - sys,lrec-eff: 0.0 - sys,pileoffpt-jes: 7.553777788776354e-07 - sys,jeten-res: 0.0 - sys,lighttag-eff: 5.614806999999999e-07 - sys,laltfakecr-ejet-fake: 1.7865295e-06 - sys,laltpar-mujet-fake: 5.614806999999999e-07 - sys,jetrec-eff: 3.573059e-07 - sys,c/tautag-eff: 5.10437e-07 - sys,dibos-xsec: 1.1229613999999998e-06 - sys,elen-res: 2.3251511924188389e-07 - sys,flavcomp-jes: 5.590333894760144e-06 - sys,detNP2-jes: 1.3753412154207186e-06 - sys,detNP3-jes: 2.921109248082781e-07 - sys,jetvxfrac: 1.7790395432496513e-06 - sys,ltrig-eff: 5.614806999999999e-07 - sys,btag-jes: 1.080593441205106e-05 - sys,mup-scale: 1.250310195837017e-07 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 1.2635249075552677e-06 - sys,laltpar-ejet-fake: 6.7888121e-06 - sys,statNP1-jes: 2.4065088158187675e-06 - sys,muid-res: 1.0208739999999999e-07 - sys,pdf: 4.7470641e-06 - sys,isr-fsr: 3.900228875825765e-05 - sys,zjet-xsec: 5.818981800000001e-06 - sys,ps-model: 6.95725631e-05 - sys,flavres-jes: 4.302835559797415e-06 - sys,laltfakecr-mujet-fake: 1.4802673e-06 - sys,mums-res: 2.0417479999999998e-07 - sys,mod-NP2-jes: 1.5971050886483677e-06 - sys,lid-eff: 1.531311e-07 - sys,mixNP2-jes: 1.1762222976314427e-06 - sys,mixNP1-jes: 2.7601382250774253e-06 - sys,btag-eff: 7.200419071381978e-06 - sys,pileoffrho-jes: 1.0898367867988793e-05 - sys,modNP4-jes: 5.797449878610508e-07 - sys,mcstat: 4.7981078e-06 - sys,modNP3-jes: 4.594500117217146e-06 - sys,mod-NP1-jes: 1.9651824499999997e-05 + syst_singletop-xsec: 5.661020182722103e-07 + syst_wjet-scale: 3.2157530999999995e-06 + syst_laltrealcr-mujet-fake: 5.3595885e-06 + syst_eta-jes: 3.3758375287599462e-06 + syst_statNP3-jes: 5.79357237996634e-06 + syst_laltrealcr-ejet-fake: 2.2969665e-06 + syst_pileoffmu-jes: 8.552676196346191e-07 + syst_lstat-ejet-fake: 3.1827701450269258e-06 + syst_lstat-mujet-fake: 1.4587696498040077e-06 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 8.493671679999999e-05 + syst_statNP2-jes: 3.34715924802914e-07 + syst_elen-scale: 8.44923576931701e-07 + syst_punch-jes: 1.822624651390063e-07 + syst_pileoffnpv-jes: 3.0166567593811113e-06 + syst_lrec-eff: 0.0 + syst_pileoffpt-jes: 7.553777788776354e-07 + syst_jeten-res: 0.0 + syst_lighttag-eff: 5.614806999999999e-07 + syst_laltfakecr-ejet-fake: 1.7865295e-06 + syst_laltpar-mujet-fake: 5.614806999999999e-07 + syst_jetrec-eff: 3.573059e-07 + syst_c/tautag-eff: 5.10437e-07 + syst_dibos-xsec: 1.1229613999999998e-06 + syst_elen-res: 2.3251511924188389e-07 + syst_flavcomp-jes: 5.590333894760144e-06 + syst_detNP2-jes: 1.3753412154207186e-06 + syst_detNP3-jes: 2.921109248082781e-07 + syst_jetvxfrac: 1.7790395432496513e-06 + syst_ltrig-eff: 5.614806999999999e-07 + syst_btag-jes: 1.080593441205106e-05 + syst_mup-scale: 1.250310195837017e-07 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 1.2635249075552677e-06 + syst_laltpar-ejet-fake: 6.7888121e-06 + syst_statNP1-jes: 2.4065088158187675e-06 + syst_muid-res: 1.0208739999999999e-07 + syst_pdf: 4.7470641e-06 + syst_isr-fsr: 3.900228875825765e-05 + syst_zjet-xsec: 5.818981800000001e-06 + syst_ps-model: 6.95725631e-05 + syst_flavres-jes: 4.302835559797415e-06 + syst_laltfakecr-mujet-fake: 1.4802673e-06 + syst_mums-res: 2.0417479999999998e-07 + syst_mod-NP2-jes: 1.5971050886483677e-06 + syst_lid-eff: 1.531311e-07 + syst_mixNP2-jes: 1.1762222976314427e-06 + syst_mixNP1-jes: 2.7601382250774253e-06 + syst_btag-eff: 7.200419071381978e-06 + syst_pileoffrho-jes: 1.0898367867988793e-05 + syst_modNP4-jes: 5.797449878610508e-07 + syst_mcstat: 4.7981078e-06 + syst_modNP3-jes: 4.594500117217146e-06 + syst_mod-NP1-jes: 1.9651824499999997e-05 - ArtUnc_1: 1.5856113881826393e-08 ArtUnc_2: 2.0866828886744868e-07 ArtUnc_3: -1.3584951484187687e-08 @@ -585,61 +585,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 3.168162899313319e-10 - sys,singletop-xsec: 4.7153697595416635e-08 - sys,wjet-scale: 3.0900066646163906e-06 - sys,laltrealcr-mujet-fake: 6.479359600000001e-06 - sys,eta-jes: 7.42622084893836e-06 - sys,statNP3-jes: 1.067633024212472e-05 - sys,laltrealcr-ejet-fake: 1.36121e-06 - sys,pileoffmu-jes: 1.2250890000000003e-06 - sys,lstat-ejet-fake: 4.243832783587497e-06 - sys,lstat-mujet-fake: 2.5934533677479144e-07 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 3.4520285600000004e-05 - sys,statNP2-jes: 1.7845354078271103e-06 - sys,elen-scale: 1.3182670555162147e-06 - sys,punch-jes: 2.9162951373650436e-07 - sys,pileoffnpv-jes: 3.336936282606451e-06 - sys,lrec-eff: 4.900355999999999e-07 - sys,pileoffpt-jes: 1.2495729862785406e-06 - sys,jeten-res: 0.0 - sys,lighttag-eff: 1.0755281912612192e-06 - sys,laltfakecr-ejet-fake: 1.36121e-06 - sys,laltpar-mujet-fake: 3.7297154000000004e-06 - sys,jetrec-eff: 6.80605e-07 - sys,c/tautag-eff: 4.152092149096119e-06 - sys,dibos-xsec: 1.4428826e-06 - sys,elen-res: 3.1366957210368685e-07 - sys,flavcomp-jes: 2.389534271294421e-06 - sys,detNP2-jes: 6.69488413227436e-07 - sys,detNP3-jes: 1.7224013598724628e-06 - sys,jetvxfrac: 8.524000925208288e-06 - sys,ltrig-eff: 7.895018000000001e-07 - sys,btag-jes: 1.3910760372307003e-05 - sys,mup-scale: 2.2117044633549029e-07 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 1.421261647852314e-05 - sys,laltpar-ejet-fake: 3.2941281999999997e-06 - sys,statNP1-jes: 3.6452990316103788e-06 - sys,muid-res: 5.44484e-08 - sys,pdf: 2.72242e-06 - sys,isr-fsr: 2.5396293821011984e-05 - sys,zjet-xsec: 1.15975092e-05 - sys,ps-model: 4.08363e-07 - sys,flavres-jes: 8.294707123892949e-06 - sys,laltfakecr-mujet-fake: 3.3213524e-06 - sys,mums-res: 0.0 - sys,mod-NP2-jes: 5.792744267344364e-07 - sys,lid-eff: 1.0483084656138718e-06 - sys,mixNP2-jes: 8.926050893665127e-07 - sys,mixNP1-jes: 1.2154340055764608e-05 - sys,btag-eff: 1.2529048906543226e-06 - sys,pileoffrho-jes: 8.136529587682169e-06 - sys,modNP4-jes: 2.0053723440205913e-06 - sys,mcstat: 4.274199400000001e-06 - sys,modNP3-jes: 1.0723527368832879e-05 - sys,mod-NP1-jes: 1.6647609430086225e-05 + syst_singletop-xsec: 4.7153697595416635e-08 + syst_wjet-scale: 3.0900066646163906e-06 + syst_laltrealcr-mujet-fake: 6.479359600000001e-06 + syst_eta-jes: 7.42622084893836e-06 + syst_statNP3-jes: 1.067633024212472e-05 + syst_laltrealcr-ejet-fake: 1.36121e-06 + syst_pileoffmu-jes: 1.2250890000000003e-06 + syst_lstat-ejet-fake: 4.243832783587497e-06 + syst_lstat-mujet-fake: 2.5934533677479144e-07 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 3.4520285600000004e-05 + syst_statNP2-jes: 1.7845354078271103e-06 + syst_elen-scale: 1.3182670555162147e-06 + syst_punch-jes: 2.9162951373650436e-07 + syst_pileoffnpv-jes: 3.336936282606451e-06 + syst_lrec-eff: 4.900355999999999e-07 + syst_pileoffpt-jes: 1.2495729862785406e-06 + syst_jeten-res: 0.0 + syst_lighttag-eff: 1.0755281912612192e-06 + syst_laltfakecr-ejet-fake: 1.36121e-06 + syst_laltpar-mujet-fake: 3.7297154000000004e-06 + syst_jetrec-eff: 6.80605e-07 + syst_c/tautag-eff: 4.152092149096119e-06 + syst_dibos-xsec: 1.4428826e-06 + syst_elen-res: 3.1366957210368685e-07 + syst_flavcomp-jes: 2.389534271294421e-06 + syst_detNP2-jes: 6.69488413227436e-07 + syst_detNP3-jes: 1.7224013598724628e-06 + syst_jetvxfrac: 8.524000925208288e-06 + syst_ltrig-eff: 7.895018000000001e-07 + syst_btag-jes: 1.3910760372307003e-05 + syst_mup-scale: 2.2117044633549029e-07 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 1.421261647852314e-05 + syst_laltpar-ejet-fake: 3.2941281999999997e-06 + syst_statNP1-jes: 3.6452990316103788e-06 + syst_muid-res: 5.44484e-08 + syst_pdf: 2.72242e-06 + syst_isr-fsr: 2.5396293821011984e-05 + syst_zjet-xsec: 1.15975092e-05 + syst_ps-model: 4.08363e-07 + syst_flavres-jes: 8.294707123892949e-06 + syst_laltfakecr-mujet-fake: 3.3213524e-06 + syst_mums-res: 0.0 + syst_mod-NP2-jes: 5.792744267344364e-07 + syst_lid-eff: 1.0483084656138718e-06 + syst_mixNP2-jes: 8.926050893665127e-07 + syst_mixNP1-jes: 1.2154340055764608e-05 + syst_btag-eff: 1.2529048906543226e-06 + syst_pileoffrho-jes: 8.136529587682169e-06 + syst_modNP4-jes: 2.0053723440205913e-06 + syst_mcstat: 4.274199400000001e-06 + syst_modNP3-jes: 1.0723527368832879e-05 + syst_mod-NP1-jes: 1.6647609430086225e-05 - ArtUnc_1: 1.1754955977231583e-08 ArtUnc_2: -8.748454423277843e-09 ArtUnc_3: -2.682955240840753e-08 @@ -665,61 +665,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 4.749915241736384e-10 - sys,singletop-xsec: 3.7812249638005876e-07 - sys,wjet-scale: 1.5903872794973269e-06 - sys,laltrealcr-mujet-fake: 3.5392878000000004e-06 - sys,eta-jes: 2.94439017079849e-06 - sys,statNP3-jes: 5.2048350000000005e-06 - sys,laltrealcr-ejet-fake: 9.25304e-08 - sys,pileoffmu-jes: 1.101838059057115e-07 - sys,lstat-ejet-fake: 1.8931581196526793e-06 - sys,lstat-mujet-fake: 8.514203183623161e-07 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 2.29706718e-05 - sys,statNP2-jes: 1.315256656537951e-06 - sys,elen-scale: 7.346876977908556e-07 - sys,punch-jes: 2.783131549253646e-07 - sys,pileoffnpv-jes: 7.412365124566045e-07 - sys,lrec-eff: 4.0482050000000006e-07 - sys,pileoffpt-jes: 8.048973128311137e-07 - sys,jeten-res: 0.0 - sys,lighttag-eff: 8.212073000000001e-07 - sys,laltfakecr-ejet-fake: 4.626520000000001e-07 - sys,laltpar-mujet-fake: 1.8159091000000002e-06 - sys,jetrec-eff: 5.320498000000001e-07 - sys,c/tautag-eff: 3.5221757455295797e-06 - sys,dibos-xsec: 6.477128e-07 - sys,elen-res: 1.9933000481595842e-07 - sys,flavcomp-jes: 2.26938351676792e-06 - sys,detNP2-jes: 1.1264382694119229e-06 - sys,detNP3-jes: 1.0839511562895019e-06 - sys,jetvxfrac: 5.855115938048637e-06 - sys,ltrig-eff: 4.3381335181378126e-07 - sys,btag-jes: 5.4525383280703105e-06 - sys,mup-scale: 8.732365002867207e-08 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 1.0549277238950152e-05 - sys,laltpar-ejet-fake: 4.62652e-08 - sys,statNP1-jes: 4.797920264138707e-06 - sys,muid-res: 4.62652e-08 - sys,pdf: 4.510857e-07 - sys,isr-fsr: 1.1050411785861241e-05 - sys,zjet-xsec: 7.2867690000000005e-06 - sys,ps-model: 5.355196900000001e-06 - sys,flavres-jes: 4.08465648999693e-06 - sys,laltfakecr-mujet-fake: 1.4226549e-06 - sys,mums-res: 1.15663e-08 - sys,mod-NP2-jes: 1.1956863669939152e-06 - sys,lid-eff: 7.982423099356926e-07 - sys,mixNP2-jes: 1.4934865908468579e-06 - sys,mixNP1-jes: 7.567825759044306e-06 - sys,btag-eff: 5.588917066065878e-06 - sys,pileoffrho-jes: 6.10142880874101e-07 - sys,modNP4-jes: 1.8182558372140726e-06 - sys,mcstat: 2.5098871000000005e-06 - sys,modNP3-jes: 5.83329229303095e-06 - sys,mod-NP1-jes: 3.3831526357120526e-06 + syst_singletop-xsec: 3.7812249638005876e-07 + syst_wjet-scale: 1.5903872794973269e-06 + syst_laltrealcr-mujet-fake: 3.5392878000000004e-06 + syst_eta-jes: 2.94439017079849e-06 + syst_statNP3-jes: 5.2048350000000005e-06 + syst_laltrealcr-ejet-fake: 9.25304e-08 + syst_pileoffmu-jes: 1.101838059057115e-07 + syst_lstat-ejet-fake: 1.8931581196526793e-06 + syst_lstat-mujet-fake: 8.514203183623161e-07 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 2.29706718e-05 + syst_statNP2-jes: 1.315256656537951e-06 + syst_elen-scale: 7.346876977908556e-07 + syst_punch-jes: 2.783131549253646e-07 + syst_pileoffnpv-jes: 7.412365124566045e-07 + syst_lrec-eff: 4.0482050000000006e-07 + syst_pileoffpt-jes: 8.048973128311137e-07 + syst_jeten-res: 0.0 + syst_lighttag-eff: 8.212073000000001e-07 + syst_laltfakecr-ejet-fake: 4.626520000000001e-07 + syst_laltpar-mujet-fake: 1.8159091000000002e-06 + syst_jetrec-eff: 5.320498000000001e-07 + syst_c/tautag-eff: 3.5221757455295797e-06 + syst_dibos-xsec: 6.477128e-07 + syst_elen-res: 1.9933000481595842e-07 + syst_flavcomp-jes: 2.26938351676792e-06 + syst_detNP2-jes: 1.1264382694119229e-06 + syst_detNP3-jes: 1.0839511562895019e-06 + syst_jetvxfrac: 5.855115938048637e-06 + syst_ltrig-eff: 4.3381335181378126e-07 + syst_btag-jes: 5.4525383280703105e-06 + syst_mup-scale: 8.732365002867207e-08 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 1.0549277238950152e-05 + syst_laltpar-ejet-fake: 4.62652e-08 + syst_statNP1-jes: 4.797920264138707e-06 + syst_muid-res: 4.62652e-08 + syst_pdf: 4.510857e-07 + syst_isr-fsr: 1.1050411785861241e-05 + syst_zjet-xsec: 7.2867690000000005e-06 + syst_ps-model: 5.355196900000001e-06 + syst_flavres-jes: 4.08465648999693e-06 + syst_laltfakecr-mujet-fake: 1.4226549e-06 + syst_mums-res: 1.15663e-08 + syst_mod-NP2-jes: 1.1956863669939152e-06 + syst_lid-eff: 7.982423099356926e-07 + syst_mixNP2-jes: 1.4934865908468579e-06 + syst_mixNP1-jes: 7.567825759044306e-06 + syst_btag-eff: 5.588917066065878e-06 + syst_pileoffrho-jes: 6.10142880874101e-07 + syst_modNP4-jes: 1.8182558372140726e-06 + syst_mcstat: 2.5098871000000005e-06 + syst_modNP3-jes: 5.83329229303095e-06 + syst_mod-NP1-jes: 3.3831526357120526e-06 - ArtUnc_1: 2.6468185837118432e-08 ArtUnc_2: -2.9584803887430473e-08 ArtUnc_3: 2.2012578683225462e-08 @@ -745,61 +745,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 5.798939284403355e-10 - sys,singletop-xsec: 2.96210673250036e-07 - sys,wjet-scale: 1.915434e-07 - sys,laltrealcr-mujet-fake: 5.661171600000001e-07 - sys,eta-jes: 8.758171323468029e-07 - sys,statNP3-jes: 1.7050112377103853e-06 - sys,laltrealcr-ejet-fake: 2.341086e-07 - sys,pileoffmu-jes: 3.878049475035134e-07 - sys,lstat-ejet-fake: 1.1379963639302067e-06 - sys,lstat-mujet-fake: 3.1333162839590584e-07 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 1.4123133360000001e-05 - sys,statNP2-jes: 6.646989046016357e-07 - sys,elen-scale: 3.112211875509095e-07 - sys,punch-jes: 1.645242717368401e-07 - sys,pileoffnpv-jes: 2.774258544984213e-07 - sys,lrec-eff: 2.0856948000000001e-07 - sys,pileoffpt-jes: 1.6588145032724426e-07 - sys,jeten-res: 0.0 - sys,lighttag-eff: 4.4489797812025665e-07 - sys,laltfakecr-ejet-fake: 9.7048656e-07 - sys,laltpar-mujet-fake: 5.6186064e-07 - sys,jetrec-eff: 2.2985208e-07 - sys,c/tautag-eff: 1.8134373822488834e-06 - sys,dibos-xsec: 3.1072596e-07 - sys,elen-res: 1.0434987047788033e-07 - sys,flavcomp-jes: 2.29176349482551e-06 - sys,detNP2-jes: 8.68087487068233e-07 - sys,detNP3-jes: 6.330741814368445e-07 - sys,jetvxfrac: 2.77387647035772e-06 - sys,ltrig-eff: 1.5964787427806611e-07 - sys,btag-jes: 1.7949535898864554e-06 - sys,mup-scale: 4.3146502151896396e-08 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 5.429402747025549e-06 - sys,laltpar-ejet-fake: 4.0862592000000005e-07 - sys,statNP1-jes: 2.681103357663237e-06 - sys,muid-res: 8.513040000000001e-09 - sys,pdf: 8.8535616e-07 - sys,isr-fsr: 4.025227824850474e-06 - sys,zjet-xsec: 3.09023352e-06 - sys,ps-model: 2.5241163599999997e-06 - sys,flavres-jes: 1.108331113975025e-06 - sys,laltfakecr-mujet-fake: 5.873997600000001e-07 - sys,mums-res: 2.9795640000000002e-08 - sys,mod-NP2-jes: 9.42862416796411e-07 - sys,lid-eff: 3.6404445645133567e-07 - sys,mixNP2-jes: 7.997359971196763e-07 - sys,mixNP1-jes: 3.43598464709926e-06 - sys,btag-eff: 5.047763888849473e-06 - sys,pileoffrho-jes: 1.1020750080276103e-06 - sys,modNP4-jes: 1.1331700476978638e-06 - sys,mcstat: 1.41742116e-06 - sys,modNP3-jes: 2.0443740314264796e-06 - sys,mod-NP1-jes: 5.850315378621385e-07 + syst_singletop-xsec: 2.96210673250036e-07 + syst_wjet-scale: 1.915434e-07 + syst_laltrealcr-mujet-fake: 5.661171600000001e-07 + syst_eta-jes: 8.758171323468029e-07 + syst_statNP3-jes: 1.7050112377103853e-06 + syst_laltrealcr-ejet-fake: 2.341086e-07 + syst_pileoffmu-jes: 3.878049475035134e-07 + syst_lstat-ejet-fake: 1.1379963639302067e-06 + syst_lstat-mujet-fake: 3.1333162839590584e-07 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 1.4123133360000001e-05 + syst_statNP2-jes: 6.646989046016357e-07 + syst_elen-scale: 3.112211875509095e-07 + syst_punch-jes: 1.645242717368401e-07 + syst_pileoffnpv-jes: 2.774258544984213e-07 + syst_lrec-eff: 2.0856948000000001e-07 + syst_pileoffpt-jes: 1.6588145032724426e-07 + syst_jeten-res: 0.0 + syst_lighttag-eff: 4.4489797812025665e-07 + syst_laltfakecr-ejet-fake: 9.7048656e-07 + syst_laltpar-mujet-fake: 5.6186064e-07 + syst_jetrec-eff: 2.2985208e-07 + syst_c/tautag-eff: 1.8134373822488834e-06 + syst_dibos-xsec: 3.1072596e-07 + syst_elen-res: 1.0434987047788033e-07 + syst_flavcomp-jes: 2.29176349482551e-06 + syst_detNP2-jes: 8.68087487068233e-07 + syst_detNP3-jes: 6.330741814368445e-07 + syst_jetvxfrac: 2.77387647035772e-06 + syst_ltrig-eff: 1.5964787427806611e-07 + syst_btag-jes: 1.7949535898864554e-06 + syst_mup-scale: 4.3146502151896396e-08 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 5.429402747025549e-06 + syst_laltpar-ejet-fake: 4.0862592000000005e-07 + syst_statNP1-jes: 2.681103357663237e-06 + syst_muid-res: 8.513040000000001e-09 + syst_pdf: 8.8535616e-07 + syst_isr-fsr: 4.025227824850474e-06 + syst_zjet-xsec: 3.09023352e-06 + syst_ps-model: 2.5241163599999997e-06 + syst_flavres-jes: 1.108331113975025e-06 + syst_laltfakecr-mujet-fake: 5.873997600000001e-07 + syst_mums-res: 2.9795640000000002e-08 + syst_mod-NP2-jes: 9.42862416796411e-07 + syst_lid-eff: 3.6404445645133567e-07 + syst_mixNP2-jes: 7.997359971196763e-07 + syst_mixNP1-jes: 3.43598464709926e-06 + syst_btag-eff: 5.047763888849473e-06 + syst_pileoffrho-jes: 1.1020750080276103e-06 + syst_modNP4-jes: 1.1331700476978638e-06 + syst_mcstat: 1.41742116e-06 + syst_modNP3-jes: 2.0443740314264796e-06 + syst_mod-NP1-jes: 5.850315378621385e-07 - ArtUnc_1: -3.955905941633644e-09 ArtUnc_2: -1.823865290553321e-08 ArtUnc_3: -2.000019068663233e-08 @@ -825,61 +825,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 8.364047609253432e-10 - sys,singletop-xsec: 1.6003407188046426e-07 - sys,wjet-scale: 1.0738681106042026e-07 - sys,laltrealcr-mujet-fake: 5.406624e-08 - sys,eta-jes: 2.726615233449003e-07 - sys,statNP3-jes: 4.909770849593222e-07 - sys,laltrealcr-ejet-fake: 1.2915824e-07 - sys,pileoffmu-jes: 1.2943735051458987e-07 - sys,lstat-ejet-fake: 4.0301674223342333e-07 - sys,lstat-mujet-fake: 1.4827200153583685e-07 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 7.27341112e-06 - sys,statNP2-jes: 2.7204039737678964e-07 - sys,elen-scale: 1.1548553926380915e-07 - sys,punch-jes: 1.494368534196943e-07 - sys,pileoffnpv-jes: 2.2057500100857126e-07 - sys,lrec-eff: 8.335888477053421e-08 - sys,pileoffpt-jes: 2.991452662565129e-08 - sys,jeten-res: 0.0 - sys,lighttag-eff: 1.2544410221483034e-07 - sys,laltfakecr-ejet-fake: 4.835924800000001e-07 - sys,laltpar-mujet-fake: 1.2014720000000002e-08 - sys,jetrec-eff: 1.1564168000000001e-07 - sys,c/tautag-eff: 7.682799733606066e-07 - sys,dibos-xsec: 9.161224000000001e-08 - sys,elen-res: 2.83664937525384e-08 - sys,flavcomp-jes: 9.272667139382308e-07 - sys,detNP2-jes: 4.200664199204673e-07 - sys,detNP3-jes: 2.9862361630591246e-07 - sys,jetvxfrac: 1.085257891215649e-06 - sys,ltrig-eff: 5.3325895289204484e-08 - sys,btag-jes: 6.825871060941096e-07 - sys,mup-scale: 2.604512734119763e-08 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 2.7569245218376988e-06 - sys,laltpar-ejet-fake: 2.553128e-07 - sys,statNP1-jes: 1.0211084600099304e-06 - sys,muid-res: 6.007360000000001e-09 - sys,pdf: 3.9948944000000007e-07 - sys,isr-fsr: 7.309237113423851e-07 - sys,zjet-xsec: 7.1187216e-07 - sys,ps-model: 1.12638e-06 - sys,flavres-jes: 6.117721782346093e-07 - sys,laltfakecr-mujet-fake: 2.6132016e-07 - sys,mums-res: 1.5018400000000003e-09 - sys,mod-NP2-jes: 4.856566029156586e-07 - sys,lid-eff: 1.314496130699334e-07 - sys,mixNP2-jes: 3.9555454963340573e-07 - sys,mixNP1-jes: 1.729723804700449e-06 - sys,btag-eff: 2.9143134577445993e-06 - sys,pileoffrho-jes: 3.679806823376054e-07 - sys,modNP4-jes: 5.386939091399911e-07 - sys,mcstat: 7.3740344e-07 - sys,modNP3-jes: 6.980503206298823e-07 - sys,mod-NP1-jes: 2.919244357659674e-07 + syst_singletop-xsec: 1.6003407188046426e-07 + syst_wjet-scale: 1.0738681106042026e-07 + syst_laltrealcr-mujet-fake: 5.406624e-08 + syst_eta-jes: 2.726615233449003e-07 + syst_statNP3-jes: 4.909770849593222e-07 + syst_laltrealcr-ejet-fake: 1.2915824e-07 + syst_pileoffmu-jes: 1.2943735051458987e-07 + syst_lstat-ejet-fake: 4.0301674223342333e-07 + syst_lstat-mujet-fake: 1.4827200153583685e-07 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 7.27341112e-06 + syst_statNP2-jes: 2.7204039737678964e-07 + syst_elen-scale: 1.1548553926380915e-07 + syst_punch-jes: 1.494368534196943e-07 + syst_pileoffnpv-jes: 2.2057500100857126e-07 + syst_lrec-eff: 8.335888477053421e-08 + syst_pileoffpt-jes: 2.991452662565129e-08 + syst_jeten-res: 0.0 + syst_lighttag-eff: 1.2544410221483034e-07 + syst_laltfakecr-ejet-fake: 4.835924800000001e-07 + syst_laltpar-mujet-fake: 1.2014720000000002e-08 + syst_jetrec-eff: 1.1564168000000001e-07 + syst_c/tautag-eff: 7.682799733606066e-07 + syst_dibos-xsec: 9.161224000000001e-08 + syst_elen-res: 2.83664937525384e-08 + syst_flavcomp-jes: 9.272667139382308e-07 + syst_detNP2-jes: 4.200664199204673e-07 + syst_detNP3-jes: 2.9862361630591246e-07 + syst_jetvxfrac: 1.085257891215649e-06 + syst_ltrig-eff: 5.3325895289204484e-08 + syst_btag-jes: 6.825871060941096e-07 + syst_mup-scale: 2.604512734119763e-08 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 2.7569245218376988e-06 + syst_laltpar-ejet-fake: 2.553128e-07 + syst_statNP1-jes: 1.0211084600099304e-06 + syst_muid-res: 6.007360000000001e-09 + syst_pdf: 3.9948944000000007e-07 + syst_isr-fsr: 7.309237113423851e-07 + syst_zjet-xsec: 7.1187216e-07 + syst_ps-model: 1.12638e-06 + syst_flavres-jes: 6.117721782346093e-07 + syst_laltfakecr-mujet-fake: 2.6132016e-07 + syst_mums-res: 1.5018400000000003e-09 + syst_mod-NP2-jes: 4.856566029156586e-07 + syst_lid-eff: 1.314496130699334e-07 + syst_mixNP2-jes: 3.9555454963340573e-07 + syst_mixNP1-jes: 1.729723804700449e-06 + syst_btag-eff: 2.9143134577445993e-06 + syst_pileoffrho-jes: 3.679806823376054e-07 + syst_modNP4-jes: 5.386939091399911e-07 + syst_mcstat: 7.3740344e-07 + syst_modNP3-jes: 6.980503206298823e-07 + syst_mod-NP1-jes: 2.919244357659674e-07 - ArtUnc_1: -6.254459426165127e-10 ArtUnc_2: -8.61534643616709e-09 ArtUnc_3: -9.54429365441051e-09 @@ -905,58 +905,58 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 9.3600136100836e-10 - sys,singletop-xsec: 1.0257758733633549e-07 - sys,wjet-scale: 4.918015930746105e-08 - sys,laltrealcr-mujet-fake: 1.3087726400000002e-07 - sys,eta-jes: 4.6764955844985296e-08 - sys,statNP3-jes: 1.4158352403830106e-07 - sys,laltrealcr-ejet-fake: 3.6533224e-08 - sys,pileoffmu-jes: 8.201959629156767e-08 - sys,lstat-ejet-fake: 2.3414499417137887e-07 - sys,lstat-mujet-fake: 7.509845290426183e-08 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 3.781389416e-06 - sys,statNP2-jes: 8.261066869264953e-08 - sys,elen-scale: 3.148358399659098e-08 - sys,punch-jes: 3.142978557135228e-08 - sys,pileoffnpv-jes: 4.485046492893415e-08 - sys,lrec-eff: 2.408784e-08 - sys,pileoffpt-jes: 7.509416046032107e-08 - sys,jeten-res: 0.0 - sys,lighttag-eff: 1.3907120908196635e-09 - sys,laltfakecr-ejet-fake: 7.1862056e-08 - sys,laltpar-mujet-fake: 1.1642456e-08 - sys,jetrec-eff: 3.5328832e-08 - sys,c/tautag-eff: 2.041683251715078e-07 - sys,dibos-xsec: 4.01464e-09 - sys,elen-res: 8.234906447057672e-09 - sys,flavcomp-jes: 4.2710953835772873e-07 - sys,detNP2-jes: 1.3481541297938946e-07 - sys,detNP3-jes: 9.449596149375004e-08 - sys,jetvxfrac: 3.3622915590611264e-07 - sys,ltrig-eff: 1.3050667819236225e-08 - sys,btag-jes: 2.0272302229146857e-07 - sys,mup-scale: 8.276392384414842e-09 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 1.162627752696449e-06 - sys,laltpar-ejet-fake: 9.233672e-09 - sys,statNP1-jes: 3.644675728388987e-07 - sys,muid-res: 4.01464e-09 - sys,pdf: 1.68213416e-07 - sys,isr-fsr: 4.0156811622417787e-07 - sys,zjet-xsec: 2.5894428000000003e-07 - sys,ps-model: 1.61789992e-07 - sys,flavres-jes: 1.478281815853055e-07 - sys,laltfakecr-mujet-fake: 2.3284912e-08 - sys,mums-res: 6.824888000000001e-09 - sys,mod-NP2-jes: 3.099085609499903e-07 - sys,lid-eff: 3.4931982223945094e-08 - sys,mixNP2-jes: 1.562065160877596e-07 - sys,mixNP1-jes: 6.925858787835182e-07 - sys,btag-eff: 1.0727223816862859e-06 - sys,pileoffrho-jes: 2.7775180286602007e-07 - sys,modNP4-jes: 1.8122564771610002e-07 - sys,mcstat: 3.88617152e-07 - sys,modNP3-jes: 1.358531575666625e-07 - sys,mod-NP1-jes: 1.605919982050383e-07 + syst_singletop-xsec: 1.0257758733633549e-07 + syst_wjet-scale: 4.918015930746105e-08 + syst_laltrealcr-mujet-fake: 1.3087726400000002e-07 + syst_eta-jes: 4.6764955844985296e-08 + syst_statNP3-jes: 1.4158352403830106e-07 + syst_laltrealcr-ejet-fake: 3.6533224e-08 + syst_pileoffmu-jes: 8.201959629156767e-08 + syst_lstat-ejet-fake: 2.3414499417137887e-07 + syst_lstat-mujet-fake: 7.509845290426183e-08 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 3.781389416e-06 + syst_statNP2-jes: 8.261066869264953e-08 + syst_elen-scale: 3.148358399659098e-08 + syst_punch-jes: 3.142978557135228e-08 + syst_pileoffnpv-jes: 4.485046492893415e-08 + syst_lrec-eff: 2.408784e-08 + syst_pileoffpt-jes: 7.509416046032107e-08 + syst_jeten-res: 0.0 + syst_lighttag-eff: 1.3907120908196635e-09 + syst_laltfakecr-ejet-fake: 7.1862056e-08 + syst_laltpar-mujet-fake: 1.1642456e-08 + syst_jetrec-eff: 3.5328832e-08 + syst_c/tautag-eff: 2.041683251715078e-07 + syst_dibos-xsec: 4.01464e-09 + syst_elen-res: 8.234906447057672e-09 + syst_flavcomp-jes: 4.2710953835772873e-07 + syst_detNP2-jes: 1.3481541297938946e-07 + syst_detNP3-jes: 9.449596149375004e-08 + syst_jetvxfrac: 3.3622915590611264e-07 + syst_ltrig-eff: 1.3050667819236225e-08 + syst_btag-jes: 2.0272302229146857e-07 + syst_mup-scale: 8.276392384414842e-09 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 1.162627752696449e-06 + syst_laltpar-ejet-fake: 9.233672e-09 + syst_statNP1-jes: 3.644675728388987e-07 + syst_muid-res: 4.01464e-09 + syst_pdf: 1.68213416e-07 + syst_isr-fsr: 4.0156811622417787e-07 + syst_zjet-xsec: 2.5894428000000003e-07 + syst_ps-model: 1.61789992e-07 + syst_flavres-jes: 1.478281815853055e-07 + syst_laltfakecr-mujet-fake: 2.3284912e-08 + syst_mums-res: 6.824888000000001e-09 + syst_mod-NP2-jes: 3.099085609499903e-07 + syst_lid-eff: 3.4931982223945094e-08 + syst_mixNP2-jes: 1.562065160877596e-07 + syst_mixNP1-jes: 6.925858787835182e-07 + syst_btag-eff: 1.0727223816862859e-06 + syst_pileoffrho-jes: 2.7775180286602007e-07 + syst_modNP4-jes: 1.8122564771610002e-07 + syst_mcstat: 3.88617152e-07 + syst_modNP3-jes: 1.358531575666625e-07 + syst_mod-NP1-jes: 1.605919982050383e-07 diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyt.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyt.yaml index 5e3e73b496..e2bdea856d 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyt.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyt.yaml @@ -99,223 +99,223 @@ definitions: definition: artificial uncertainty 25 treatment: ADD type: ATLAS8TEVTTB151104716unc25 - sys,singletop-xsec: + syst_singletop-xsec: definition: '' treatment: MULT type: CORR - sys,wjet-scale: + syst_wjet-scale: definition: '' treatment: MULT type: CORR - sys,laltrealcr-mujet-fake: + syst_laltrealcr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,eta-jes: + syst_eta-jes: definition: '' treatment: MULT type: CORR - sys,statNP3-jes: + syst_statNP3-jes: definition: '' treatment: MULT type: CORR - sys,laltrealcr-ejet-fake: + syst_laltrealcr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,pileoffmu-jes: + syst_pileoffmu-jes: definition: '' treatment: MULT type: CORR - sys,lstat-ejet-fake: + syst_lstat-ejet-fake: definition: '' treatment: MULT type: CORR - sys,lstat-mujet-fake: + syst_lstat-mujet-fake: definition: '' treatment: MULT type: CORR - sys,etmsoft-scale: + syst_etmsoft-scale: definition: '' treatment: MULT type: CORR - sys,hardscat-model: + syst_hardscat-model: definition: '' treatment: MULT type: CORR - sys,statNP2-jes: + syst_statNP2-jes: definition: '' treatment: MULT type: CORR - sys,elen-scale: + syst_elen-scale: definition: '' treatment: MULT type: CORR - sys,punch-jes: + syst_punch-jes: definition: '' treatment: MULT type: CORR - sys,pileoffnpv-jes: + syst_pileoffnpv-jes: definition: '' treatment: MULT type: CORR - sys,lrec-eff: + syst_lrec-eff: definition: '' treatment: MULT type: CORR - sys,pileoffpt-jes: + syst_pileoffpt-jes: definition: '' treatment: MULT type: CORR - sys,jeten-res: + syst_jeten-res: definition: '' treatment: MULT type: CORR - sys,lighttag-eff: + syst_lighttag-eff: definition: '' treatment: MULT type: CORR - sys,laltfakecr-ejet-fake: + syst_laltfakecr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,laltpar-mujet-fake: + syst_laltpar-mujet-fake: definition: '' treatment: MULT type: CORR - sys,jetrec-eff: + syst_jetrec-eff: definition: '' treatment: MULT type: CORR - sys,c/tautag-eff: + syst_c/tautag-eff: definition: '' treatment: MULT type: CORR - sys,dibos-xsec: + syst_dibos-xsec: definition: '' treatment: MULT type: CORR - sys,elen-res: + syst_elen-res: definition: '' treatment: MULT type: CORR - sys,flavcomp-jes: + syst_flavcomp-jes: definition: '' treatment: MULT type: CORR - sys,detNP2-jes: + syst_detNP2-jes: definition: '' treatment: MULT type: CORR - sys,detNP3-jes: + syst_detNP3-jes: definition: '' treatment: MULT type: CORR - sys,jetvxfrac: + syst_jetvxfrac: definition: '' treatment: MULT type: CORR - sys,ltrig-eff: + syst_ltrig-eff: definition: '' treatment: MULT type: CORR - sys,btag-jes: + syst_btag-jes: definition: '' treatment: MULT type: CORR - sys,mup-scale: + syst_mup-scale: definition: '' treatment: MULT type: CORR - sys,singlephpt-jes: + syst_singlephpt-jes: definition: '' treatment: MULT type: CORR - sys,etmsoft-res: + syst_etmsoft-res: definition: '' treatment: MULT type: CORR - sys,detNP1-jes: + syst_detNP1-jes: definition: '' treatment: MULT type: CORR - sys,laltpar-ejet-fake: + syst_laltpar-ejet-fake: definition: '' treatment: MULT type: CORR - sys,statNP1-jes: + syst_statNP1-jes: definition: '' treatment: MULT type: CORR - sys,muid-res: + syst_muid-res: definition: '' treatment: MULT type: CORR - sys,pdf: + syst_pdf: definition: '' treatment: MULT type: CORR - sys,isr-fsr: + syst_isr-fsr: definition: '' treatment: MULT type: CORR - sys,zjet-xsec: + syst_zjet-xsec: definition: '' treatment: MULT type: CORR - sys,ps-model: + syst_ps-model: definition: '' treatment: MULT type: CORR - sys,flavres-jes: + syst_flavres-jes: definition: '' treatment: MULT type: CORR - sys,laltfakecr-mujet-fake: + syst_laltfakecr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,mums-res: + syst_mums-res: definition: '' treatment: MULT type: CORR - sys,mod-NP2-jes: + syst_mod-NP2-jes: definition: '' treatment: MULT type: CORR - sys,lid-eff: + syst_lid-eff: definition: '' treatment: MULT type: CORR - sys,mixNP2-jes: + syst_mixNP2-jes: definition: '' treatment: MULT type: CORR - sys,mixNP1-jes: + syst_mixNP1-jes: definition: '' treatment: MULT type: CORR - sys,btag-eff: + syst_btag-eff: definition: '' treatment: MULT type: CORR - sys,pileoffrho-jes: + syst_pileoffrho-jes: definition: '' treatment: MULT type: CORR - sys,modNP4-jes: + syst_modNP4-jes: definition: '' treatment: MULT type: CORR - sys,mcstat: + syst_mcstat: definition: '' treatment: MULT type: CORR - sys,modNP3-jes: + syst_modNP3-jes: definition: '' treatment: MULT type: CORR - sys,mod-NP1-jes: + syst_mod-NP1-jes: definition: '' treatment: MULT type: CORR @@ -349,61 +349,61 @@ bins: ArtUnc_23: -5.948989742695756e-07 ArtUnc_24: -5.610902314048098e-07 ArtUnc_25: -0.00010061224703916684 - sys,singletop-xsec: 0.5394515474022182 - sys,wjet-scale: 0.8396472000000001 - sys,laltrealcr-mujet-fake: 0.45815532000000003 - sys,eta-jes: 0.0581458257371963 - sys,statNP3-jes: 0.07362061144359779 - sys,laltrealcr-ejet-fake: 0.05293428000000001 - sys,pileoffmu-jes: 0.1570623956590807 - sys,lstat-ejet-fake: 0.7635135956872972 - sys,lstat-mujet-fake: 0.042680884230966915 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 5.828246760000001 - sys,statNP2-jes: 0.14064442985397183 - sys,elen-scale: 0.21172728507331412 - sys,punch-jes: 0.0060539015623315194 - sys,pileoffnpv-jes: 1.086666515529627 - sys,lrec-eff: 0.44355276000000005 - sys,pileoffpt-jes: 0.022130828860501363 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.9427786635039423 - sys,laltfakecr-ejet-fake: 0.7155254400000002 - sys,laltpar-mujet-fake: 0.41252232000000005 - sys,jetrec-eff: 0.09126600000000001 - sys,c/tautag-eff: 1.8344484162379069 - sys,dibos-xsec: 0.16792944 - sys,elen-res: 0.06116184027414154 - sys,flavcomp-jes: 3.056162444132086 - sys,detNP2-jes: 0.3508035205499603 - sys,detNP3-jes: 0.05664377068673307 - sys,jetvxfrac: 1.187367503216403 - sys,ltrig-eff: 2.30172852 - sys,btag-jes: 0.9656805389070645 - sys,mup-scale: 0.032176928171763076 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.335029517843956 - sys,laltpar-ejet-fake: 0.6479886 - sys,statNP1-jes: 1.595363095119158 - sys,muid-res: 0.005475960000000001 - sys,pdf: 0.38879316 - sys,isr-fsr: 11.78468718365993 - sys,zjet-xsec: 2.00237604 - sys,ps-model: 6.403222560000001 - sys,flavres-jes: 1.9411460779263754 - sys,laltfakecr-mujet-fake: 0.43077551999999997 - sys,mums-res: 0.0182532 - sys,mod-NP2-jes: 0.13507368 - sys,lid-eff: 2.4094224000000004 - sys,mixNP2-jes: 0.6842266253482544 - sys,mixNP1-jes: 0.5330582911745285 - sys,btag-eff: 7.6195853392974575 - sys,pileoffrho-jes: 3.007221624574729 - sys,modNP4-jes: 0.044440076073827786 - sys,mcstat: 0.273798 - sys,modNP3-jes: 0.22546027236531319 - sys,mod-NP1-jes: 4.05039567079057 + syst_singletop-xsec: 0.5394515474022182 + syst_wjet-scale: 0.8396472000000001 + syst_laltrealcr-mujet-fake: 0.45815532000000003 + syst_eta-jes: 0.0581458257371963 + syst_statNP3-jes: 0.07362061144359779 + syst_laltrealcr-ejet-fake: 0.05293428000000001 + syst_pileoffmu-jes: 0.1570623956590807 + syst_lstat-ejet-fake: 0.7635135956872972 + syst_lstat-mujet-fake: 0.042680884230966915 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 5.828246760000001 + syst_statNP2-jes: 0.14064442985397183 + syst_elen-scale: 0.21172728507331412 + syst_punch-jes: 0.0060539015623315194 + syst_pileoffnpv-jes: 1.086666515529627 + syst_lrec-eff: 0.44355276000000005 + syst_pileoffpt-jes: 0.022130828860501363 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.9427786635039423 + syst_laltfakecr-ejet-fake: 0.7155254400000002 + syst_laltpar-mujet-fake: 0.41252232000000005 + syst_jetrec-eff: 0.09126600000000001 + syst_c/tautag-eff: 1.8344484162379069 + syst_dibos-xsec: 0.16792944 + syst_elen-res: 0.06116184027414154 + syst_flavcomp-jes: 3.056162444132086 + syst_detNP2-jes: 0.3508035205499603 + syst_detNP3-jes: 0.05664377068673307 + syst_jetvxfrac: 1.187367503216403 + syst_ltrig-eff: 2.30172852 + syst_btag-jes: 0.9656805389070645 + syst_mup-scale: 0.032176928171763076 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.335029517843956 + syst_laltpar-ejet-fake: 0.6479886 + syst_statNP1-jes: 1.595363095119158 + syst_muid-res: 0.005475960000000001 + syst_pdf: 0.38879316 + syst_isr-fsr: 11.78468718365993 + syst_zjet-xsec: 2.00237604 + syst_ps-model: 6.403222560000001 + syst_flavres-jes: 1.9411460779263754 + syst_laltfakecr-mujet-fake: 0.43077551999999997 + syst_mums-res: 0.0182532 + syst_mod-NP2-jes: 0.13507368 + syst_lid-eff: 2.4094224000000004 + syst_mixNP2-jes: 0.6842266253482544 + syst_mixNP1-jes: 0.5330582911745285 + syst_btag-eff: 7.6195853392974575 + syst_pileoffrho-jes: 3.007221624574729 + syst_modNP4-jes: 0.044440076073827786 + syst_mcstat: 0.273798 + syst_modNP3-jes: 0.22546027236531319 + syst_mod-NP1-jes: 4.05039567079057 lumi: 5.110896 - ArtUnc_1: -0.2616642012488623 ArtUnc_2: 0.49696569066969876 @@ -430,61 +430,61 @@ bins: ArtUnc_23: -5.756599746459576e-07 ArtUnc_24: -5.416079551925264e-07 ArtUnc_25: -9.738078881718213e-05 - sys,singletop-xsec: 0.4633094724383548 - sys,wjet-scale: 0.68134758 - sys,laltrealcr-mujet-fake: 0.35310714 - sys,eta-jes: 0.32095922336419247 - sys,statNP3-jes: 0.06115276064512786 - sys,laltrealcr-ejet-fake: 0.07294231999999999 - sys,pileoffmu-jes: 0.1976498589996307 - sys,lstat-ejet-fake: 0.6776504002768013 - sys,lstat-mujet-fake: 0.024406553096058034 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 5.96303466 - sys,statNP2-jes: 0.10964870725693895 - sys,elen-scale: 0.18620345541985628 - sys,punch-jes: 0.005498234244955376 - sys,pileoffnpv-jes: 1.0867715211409281 - sys,lrec-eff: 0.40449831999999997 - sys,pileoffpt-jes: 0.024406553096058034 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.82889 - sys,laltfakecr-ejet-fake: 0.68632092 - sys,laltpar-mujet-fake: 0.35973826 - sys,jetrec-eff: 0.09117789999999999 - sys,c/tautag-eff: 1.617164814853865 - sys,dibos-xsec: 0.1492002 - sys,elen-res: 0.04903078781457524 - sys,flavcomp-jes: 3.0365682333196817 - sys,detNP2-jes: 0.2846510119441299 - sys,detNP3-jes: 0.05701293176521534 - sys,jetvxfrac: 1.1148305498560496 - sys,ltrig-eff: 2.09543392 - sys,btag-jes: 0.9002508618105465 - sys,mup-scale: 0.029597293050674413 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.3081286391308585 - sys,laltpar-ejet-fake: 0.54872518 - sys,statNP1-jes: 1.4540467195686515 - sys,muid-res: 0.0016577800000000002 - sys,pdf: 0.24203587999999998 - sys,isr-fsr: 10.53934101107919 - sys,zjet-xsec: 1.7821135 - sys,ps-model: 5.91661682 - sys,flavres-jes: 1.8646250405561642 - sys,laltfakecr-mujet-fake: 0.3812894 - sys,mums-res: 0.0082889 - sys,mod-NP2-jes: 0.10472591343221742 - sys,lid-eff: 2.1882696 - sys,mixNP2-jes: 0.6292820205851145 - sys,mixNP1-jes: 0.49811530585551894 - sys,btag-eff: 6.979915699297444 - sys,pileoffrho-jes: 2.774135709265587 - sys,modNP4-jes: 0.046499177575723846 - sys,mcstat: 0.24037809999999998 - sys,modNP3-jes: 0.21676226302892923 - sys,mod-NP1-jes: 3.7358603801011654 + syst_singletop-xsec: 0.4633094724383548 + syst_wjet-scale: 0.68134758 + syst_laltrealcr-mujet-fake: 0.35310714 + syst_eta-jes: 0.32095922336419247 + syst_statNP3-jes: 0.06115276064512786 + syst_laltrealcr-ejet-fake: 0.07294231999999999 + syst_pileoffmu-jes: 0.1976498589996307 + syst_lstat-ejet-fake: 0.6776504002768013 + syst_lstat-mujet-fake: 0.024406553096058034 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 5.96303466 + syst_statNP2-jes: 0.10964870725693895 + syst_elen-scale: 0.18620345541985628 + syst_punch-jes: 0.005498234244955376 + syst_pileoffnpv-jes: 1.0867715211409281 + syst_lrec-eff: 0.40449831999999997 + syst_pileoffpt-jes: 0.024406553096058034 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.82889 + syst_laltfakecr-ejet-fake: 0.68632092 + syst_laltpar-mujet-fake: 0.35973826 + syst_jetrec-eff: 0.09117789999999999 + syst_c/tautag-eff: 1.617164814853865 + syst_dibos-xsec: 0.1492002 + syst_elen-res: 0.04903078781457524 + syst_flavcomp-jes: 3.0365682333196817 + syst_detNP2-jes: 0.2846510119441299 + syst_detNP3-jes: 0.05701293176521534 + syst_jetvxfrac: 1.1148305498560496 + syst_ltrig-eff: 2.09543392 + syst_btag-jes: 0.9002508618105465 + syst_mup-scale: 0.029597293050674413 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.3081286391308585 + syst_laltpar-ejet-fake: 0.54872518 + syst_statNP1-jes: 1.4540467195686515 + syst_muid-res: 0.0016577800000000002 + syst_pdf: 0.24203587999999998 + syst_isr-fsr: 10.53934101107919 + syst_zjet-xsec: 1.7821135 + syst_ps-model: 5.91661682 + syst_flavres-jes: 1.8646250405561642 + syst_laltfakecr-mujet-fake: 0.3812894 + syst_mums-res: 0.0082889 + syst_mod-NP2-jes: 0.10472591343221742 + syst_lid-eff: 2.1882696 + syst_mixNP2-jes: 0.6292820205851145 + syst_mixNP1-jes: 0.49811530585551894 + syst_btag-eff: 6.979915699297444 + syst_pileoffrho-jes: 2.774135709265587 + syst_modNP4-jes: 0.046499177575723846 + syst_mcstat: 0.24037809999999998 + syst_modNP3-jes: 0.21676226302892923 + syst_mod-NP1-jes: 3.7358603801011654 lumi: 4.6417839999999995 - ArtUnc_1: 0.12870895065416935 ArtUnc_2: 0.35648034359873954 @@ -511,61 +511,61 @@ bins: ArtUnc_23: -5.421633338037342e-07 ArtUnc_24: -5.045881491190621e-07 ArtUnc_25: -9.17201470386624e-05 - sys,singletop-xsec: 0.349206905811841 - sys,wjet-scale: 0.48995788 - sys,laltrealcr-mujet-fake: 0.2758476 - sys,eta-jes: 0.6995271253015907 - sys,statNP3-jes: 0.039690384283813634 - sys,laltrealcr-ejet-fake: 0.05516952 - sys,pileoffmu-jes: 0.24432216 - sys,lstat-ejet-fake: 0.47550690568714643 - sys,lstat-mujet-fake: 0.05232851115217401 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 4.96394324 - sys,statNP2-jes: 0.0965511277877809 - sys,elen-scale: 0.14589832199389682 - sys,punch-jes: 0.0034127289881852618 - sys,pileoffnpv-jes: 0.8281375976489035 - sys,lrec-eff: 0.32313576 - sys,pileoffpt-jes: 0.011375763293950873 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.6502122 - sys,laltfakecr-ejet-fake: 0.49915279999999995 - sys,laltpar-mujet-fake: 0.361229 - sys,jetrec-eff: 0.08012715999999999 - sys,c/tautag-eff: 1.2012509790923467 - sys,dibos-xsec: 0.09983056 - sys,elen-res: 0.03682656297633 - sys,flavcomp-jes: 2.6258228676624693 - sys,detNP2-jes: 0.22461876 - sys,detNP3-jes: 0.03415256 - sys,jetvxfrac: 0.8950200620974569 - sys,ltrig-eff: 1.6708483200000002 - sys,btag-jes: 0.680730846291879 - sys,mup-scale: 0.023644079999999998 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.23428549519542008 - sys,laltpar-ejet-fake: 0.32707644 - sys,statNP1-jes: 1.1690698759091809 - sys,muid-res: 0.0065678 - sys,pdf: 0.06173731999999999 - sys,isr-fsr: 8.341458068021787 - sys,zjet-xsec: 1.2557633599999998 - sys,ps-model: 5.2397908399999995 - sys,flavres-jes: 1.5871327533470925 - sys,laltfakecr-mujet-fake: 0.3218222 - sys,mums-res: 0.0065678 - sys,mod-NP2-jes: 0.08540160622439838 - sys,lid-eff: 1.7299585199999998 - sys,mixNP2-jes: 0.47880072832640475 - sys,mixNP1-jes: 0.37528009198848317 - sys,btag-eff: 5.579234883114004 - sys,pileoffrho-jes: 2.2481627368105412 - sys,modNP4-jes: 0.023644079999999998 - sys,mcstat: 0.23381368 - sys,modNP3-jes: 0.17617381316846384 - sys,mod-NP1-jes: 3.065917137255724 + syst_singletop-xsec: 0.349206905811841 + syst_wjet-scale: 0.48995788 + syst_laltrealcr-mujet-fake: 0.2758476 + syst_eta-jes: 0.6995271253015907 + syst_statNP3-jes: 0.039690384283813634 + syst_laltrealcr-ejet-fake: 0.05516952 + syst_pileoffmu-jes: 0.24432216 + syst_lstat-ejet-fake: 0.47550690568714643 + syst_lstat-mujet-fake: 0.05232851115217401 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 4.96394324 + syst_statNP2-jes: 0.0965511277877809 + syst_elen-scale: 0.14589832199389682 + syst_punch-jes: 0.0034127289881852618 + syst_pileoffnpv-jes: 0.8281375976489035 + syst_lrec-eff: 0.32313576 + syst_pileoffpt-jes: 0.011375763293950873 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.6502122 + syst_laltfakecr-ejet-fake: 0.49915279999999995 + syst_laltpar-mujet-fake: 0.361229 + syst_jetrec-eff: 0.08012715999999999 + syst_c/tautag-eff: 1.2012509790923467 + syst_dibos-xsec: 0.09983056 + syst_elen-res: 0.03682656297633 + syst_flavcomp-jes: 2.6258228676624693 + syst_detNP2-jes: 0.22461876 + syst_detNP3-jes: 0.03415256 + syst_jetvxfrac: 0.8950200620974569 + syst_ltrig-eff: 1.6708483200000002 + syst_btag-jes: 0.680730846291879 + syst_mup-scale: 0.023644079999999998 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.23428549519542008 + syst_laltpar-ejet-fake: 0.32707644 + syst_statNP1-jes: 1.1690698759091809 + syst_muid-res: 0.0065678 + syst_pdf: 0.06173731999999999 + syst_isr-fsr: 8.341458068021787 + syst_zjet-xsec: 1.2557633599999998 + syst_ps-model: 5.2397908399999995 + syst_flavres-jes: 1.5871327533470925 + syst_laltfakecr-mujet-fake: 0.3218222 + syst_mums-res: 0.0065678 + syst_mod-NP2-jes: 0.08540160622439838 + syst_lid-eff: 1.7299585199999998 + syst_mixNP2-jes: 0.47880072832640475 + syst_mixNP1-jes: 0.37528009198848317 + syst_btag-eff: 5.579234883114004 + syst_pileoffrho-jes: 2.2481627368105412 + syst_modNP4-jes: 0.023644079999999998 + syst_mcstat: 0.23381368 + syst_modNP3-jes: 0.17617381316846384 + syst_mod-NP1-jes: 3.065917137255724 lumi: 3.677968 - ArtUnc_1: 0.16411375613270995 ArtUnc_2: 0.07862385023610152 @@ -592,61 +592,61 @@ bins: ArtUnc_23: -4.870951971964978e-07 ArtUnc_24: -4.490747432486816e-07 ArtUnc_25: -8.241473296330671e-05 - sys,singletop-xsec: 0.21391223179042176 - sys,wjet-scale: 0.31405718400000004 - sys,laltrealcr-mujet-fake: 0.19254696399999996 - sys,eta-jes: 0.9585860028298857 - sys,statNP3-jes: 0.03739652172960511 - sys,laltrealcr-ejet-fake: 0.017759186 - sys,pileoffmu-jes: 0.27671994338762085 - sys,lstat-ejet-fake: 0.23555540589058357 - sys,lstat-mujet-fake: 0.10765934358573065 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 4.26687811 - sys,statNP2-jes: 0.07709384216230053 - sys,elen-scale: 0.09913926986015682 - sys,punch-jes: 0.008604770830222673 - sys,pileoffnpv-jes: 0.5547839997903166 - sys,lrec-eff: 0.232738806 - sys,pileoffpt-jes: 0.01295149998023827 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.49024744851648017 - sys,laltfakecr-ejet-fake: 0.260779626 - sys,laltpar-mujet-fake: 0.20095920999999997 - sys,jetrec-eff: 0.03364898399999999 - sys,c/tautag-eff: 0.79963099014256 - sys,dibos-xsec: 0.06636327399999999 - sys,elen-res: 0.025236737999999998 - sys,flavcomp-jes: 1.9815096151360259 - sys,detNP2-jes: 0.18343354866331543 - sys,detNP3-jes: 0.03999299936750315 - sys,jetvxfrac: 0.6229121933459856 - sys,ltrig-eff: 1.192669544 - sys,btag-jes: 0.4752500794837133 - sys,mup-scale: 0.01760478221740817 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.19491171539146393 - sys,laltpar-ejet-fake: 0.250497992 - sys,statNP1-jes: 0.8979781648504658 - sys,muid-res: 0.002804082 - sys,pdf: 0.014955103999999999 - sys,isr-fsr: 6.667310267198808 - sys,zjet-xsec: 0.7608409159999999 - sys,ps-model: 3.297600432 - sys,flavres-jes: 1.2463143868143987 - sys,laltfakecr-mujet-fake: 0.20750206799999998 - sys,mums-res: 0.00934694 - sys,mod-NP2-jes: 0.07330498218072648 - sys,lid-eff: 1.230991998 - sys,mixNP2-jes: 0.3832815292991476 - sys,mixNP1-jes: 0.32154560435120055 - sys,btag-eff: 4.01071681625937 - sys,pileoffrho-jes: 1.7101300866330107 - sys,modNP4-jes: 0.030631734953885065 - sys,mcstat: 0.20843676199999997 - sys,modNP3-jes: 0.1346024267740495 - sys,mod-NP1-jes: 2.2692007958142004 + syst_singletop-xsec: 0.21391223179042176 + syst_wjet-scale: 0.31405718400000004 + syst_laltrealcr-mujet-fake: 0.19254696399999996 + syst_eta-jes: 0.9585860028298857 + syst_statNP3-jes: 0.03739652172960511 + syst_laltrealcr-ejet-fake: 0.017759186 + syst_pileoffmu-jes: 0.27671994338762085 + syst_lstat-ejet-fake: 0.23555540589058357 + syst_lstat-mujet-fake: 0.10765934358573065 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 4.26687811 + syst_statNP2-jes: 0.07709384216230053 + syst_elen-scale: 0.09913926986015682 + syst_punch-jes: 0.008604770830222673 + syst_pileoffnpv-jes: 0.5547839997903166 + syst_lrec-eff: 0.232738806 + syst_pileoffpt-jes: 0.01295149998023827 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.49024744851648017 + syst_laltfakecr-ejet-fake: 0.260779626 + syst_laltpar-mujet-fake: 0.20095920999999997 + syst_jetrec-eff: 0.03364898399999999 + syst_c/tautag-eff: 0.79963099014256 + syst_dibos-xsec: 0.06636327399999999 + syst_elen-res: 0.025236737999999998 + syst_flavcomp-jes: 1.9815096151360259 + syst_detNP2-jes: 0.18343354866331543 + syst_detNP3-jes: 0.03999299936750315 + syst_jetvxfrac: 0.6229121933459856 + syst_ltrig-eff: 1.192669544 + syst_btag-jes: 0.4752500794837133 + syst_mup-scale: 0.01760478221740817 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.19491171539146393 + syst_laltpar-ejet-fake: 0.250497992 + syst_statNP1-jes: 0.8979781648504658 + syst_muid-res: 0.002804082 + syst_pdf: 0.014955103999999999 + syst_isr-fsr: 6.667310267198808 + syst_zjet-xsec: 0.7608409159999999 + syst_ps-model: 3.297600432 + syst_flavres-jes: 1.2463143868143987 + syst_laltfakecr-mujet-fake: 0.20750206799999998 + syst_mums-res: 0.00934694 + syst_mod-NP2-jes: 0.07330498218072648 + syst_lid-eff: 1.230991998 + syst_mixNP2-jes: 0.3832815292991476 + syst_mixNP1-jes: 0.32154560435120055 + syst_btag-eff: 4.01071681625937 + syst_pileoffrho-jes: 1.7101300866330107 + syst_modNP4-jes: 0.030631734953885065 + syst_mcstat: 0.20843676199999997 + syst_modNP3-jes: 0.1346024267740495 + syst_mod-NP1-jes: 2.2692007958142004 lumi: 2.6171432 - ArtUnc_1: 0.035804518664529494 ArtUnc_2: -0.02044939747415 @@ -673,59 +673,59 @@ bins: ArtUnc_23: -6.877283115465248e-07 ArtUnc_24: -6.239551004495684e-07 ArtUnc_25: -0.00011640007192427719 - sys,singletop-xsec: 0.0823934793165242 - sys,wjet-scale: 0.12045110000000002 - sys,laltrealcr-mujet-fake: 0.08525435 - sys,eta-jes: 0.5775054988205571 - sys,statNP3-jes: 0.017767032650670735 - sys,laltrealcr-ejet-fake: 0.032850300000000006 - sys,pileoffmu-jes: 0.1972975312932646 - sys,lstat-ejet-fake: 0.029546227412437953 - sys,lstat-mujet-fake: 0.03725489732634993 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 1.7469320249999998 - sys,statNP2-jes: 0.02937751651674925 - sys,elen-scale: 0.04520596950029111 - sys,punch-jes: 0.0010160426543549981 - sys,pileoffnpv-jes: 0.2154045928081009 - sys,lrec-eff: 0.09776875 - sys,pileoffpt-jes: 0.013215789296283064 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.24305326981089037 - sys,laltfakecr-ejet-fake: 0.05279512500000001 - sys,laltpar-mujet-fake: 0.10676347500000002 - sys,jetrec-eff: 0.014860850000000002 - sys,c/tautag-eff: 0.306016312444064 - sys,dibos-xsec: 0.031677075 - sys,elen-res: 0.01015289751210215 - sys,flavcomp-jes: 0.8362586350157776 - sys,detNP2-jes: 0.07560704668412613 - sys,detNP3-jes: 0.012712945413306814 - sys,jetvxfrac: 0.23588825788446025 - sys,ltrig-eff: 0.4990117 - sys,btag-jes: 0.18831256170496954 - sys,mup-scale: 0.007061042963596101 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.0937167400663455 - sys,laltpar-ejet-fake: 0.0391075 - sys,statNP1-jes: 0.37661984434241597 - sys,muid-res: 0.0023464500000000004 - sys,pdf: 0.57331595 - sys,isr-fsr: 2.8794391685527114 - sys,zjet-xsec: 0.30973140000000005 - sys,ps-model: 1.1830018750000002 - sys,flavres-jes: 0.5552946553919699 - sys,laltfakecr-mujet-fake: 0.09151155000000001 - sys,mums-res: 0.0015643000000000002 - sys,mod-NP2-jes: 0.026616094470357593 - sys,lid-eff: 0.51700115 - sys,mixNP2-jes: 0.16217781702949927 - sys,mixNP1-jes: 0.14000921948488784 - sys,btag-eff: 1.6999937474017894 - sys,pileoffrho-jes: 0.7254991474820986 - sys,modNP4-jes: 0.011171336490769582 - sys,mcstat: 0.12084217500000001 - sys,modNP3-jes: 0.04576914313263495 - sys,mod-NP1-jes: 0.9537044242645872 + syst_singletop-xsec: 0.0823934793165242 + syst_wjet-scale: 0.12045110000000002 + syst_laltrealcr-mujet-fake: 0.08525435 + syst_eta-jes: 0.5775054988205571 + syst_statNP3-jes: 0.017767032650670735 + syst_laltrealcr-ejet-fake: 0.032850300000000006 + syst_pileoffmu-jes: 0.1972975312932646 + syst_lstat-ejet-fake: 0.029546227412437953 + syst_lstat-mujet-fake: 0.03725489732634993 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 1.7469320249999998 + syst_statNP2-jes: 0.02937751651674925 + syst_elen-scale: 0.04520596950029111 + syst_punch-jes: 0.0010160426543549981 + syst_pileoffnpv-jes: 0.2154045928081009 + syst_lrec-eff: 0.09776875 + syst_pileoffpt-jes: 0.013215789296283064 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.24305326981089037 + syst_laltfakecr-ejet-fake: 0.05279512500000001 + syst_laltpar-mujet-fake: 0.10676347500000002 + syst_jetrec-eff: 0.014860850000000002 + syst_c/tautag-eff: 0.306016312444064 + syst_dibos-xsec: 0.031677075 + syst_elen-res: 0.01015289751210215 + syst_flavcomp-jes: 0.8362586350157776 + syst_detNP2-jes: 0.07560704668412613 + syst_detNP3-jes: 0.012712945413306814 + syst_jetvxfrac: 0.23588825788446025 + syst_ltrig-eff: 0.4990117 + syst_btag-jes: 0.18831256170496954 + syst_mup-scale: 0.007061042963596101 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.0937167400663455 + syst_laltpar-ejet-fake: 0.0391075 + syst_statNP1-jes: 0.37661984434241597 + syst_muid-res: 0.0023464500000000004 + syst_pdf: 0.57331595 + syst_isr-fsr: 2.8794391685527114 + syst_zjet-xsec: 0.30973140000000005 + syst_ps-model: 1.1830018750000002 + syst_flavres-jes: 0.5552946553919699 + syst_laltfakecr-mujet-fake: 0.09151155000000001 + syst_mums-res: 0.0015643000000000002 + syst_mod-NP2-jes: 0.026616094470357593 + syst_lid-eff: 0.51700115 + syst_mixNP2-jes: 0.16217781702949927 + syst_mixNP1-jes: 0.14000921948488784 + syst_btag-eff: 1.6999937474017894 + syst_pileoffrho-jes: 0.7254991474820986 + syst_modNP4-jes: 0.011171336490769582 + syst_mcstat: 0.12084217500000001 + syst_modNP3-jes: 0.04576914313263495 + syst_mod-NP1-jes: 0.9537044242645872 lumi: 1.09501 diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyt_norm.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyt_norm.yaml index ba1a791a48..ea89c1dc9f 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyt_norm.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyt_norm.yaml @@ -99,223 +99,223 @@ definitions: definition: artificial uncertainty 25 treatment: ADD type: ATLAS8TEVTTB151104716unc25 - sys,singletop-xsec: + syst_singletop-xsec: definition: '' treatment: MULT type: CORR - sys,wjet-scale: + syst_wjet-scale: definition: '' treatment: MULT type: CORR - sys,laltrealcr-mujet-fake: + syst_laltrealcr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,eta-jes: + syst_eta-jes: definition: '' treatment: MULT type: CORR - sys,statNP3-jes: + syst_statNP3-jes: definition: '' treatment: MULT type: CORR - sys,laltrealcr-ejet-fake: + syst_laltrealcr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,pileoffmu-jes: + syst_pileoffmu-jes: definition: '' treatment: MULT type: CORR - sys,lstat-ejet-fake: + syst_lstat-ejet-fake: definition: '' treatment: MULT type: CORR - sys,lstat-mujet-fake: + syst_lstat-mujet-fake: definition: '' treatment: MULT type: CORR - sys,etmsoft-scale: + syst_etmsoft-scale: definition: '' treatment: MULT type: CORR - sys,hardscat-model: + syst_hardscat-model: definition: '' treatment: MULT type: CORR - sys,statNP2-jes: + syst_statNP2-jes: definition: '' treatment: MULT type: CORR - sys,elen-scale: + syst_elen-scale: definition: '' treatment: MULT type: CORR - sys,punch-jes: + syst_punch-jes: definition: '' treatment: MULT type: CORR - sys,pileoffnpv-jes: + syst_pileoffnpv-jes: definition: '' treatment: MULT type: CORR - sys,lrec-eff: + syst_lrec-eff: definition: '' treatment: MULT type: CORR - sys,pileoffpt-jes: + syst_pileoffpt-jes: definition: '' treatment: MULT type: CORR - sys,jeten-res: + syst_jeten-res: definition: '' treatment: MULT type: CORR - sys,lighttag-eff: + syst_lighttag-eff: definition: '' treatment: MULT type: CORR - sys,laltfakecr-ejet-fake: + syst_laltfakecr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,laltpar-mujet-fake: + syst_laltpar-mujet-fake: definition: '' treatment: MULT type: CORR - sys,jetrec-eff: + syst_jetrec-eff: definition: '' treatment: MULT type: CORR - sys,c/tautag-eff: + syst_c/tautag-eff: definition: '' treatment: MULT type: CORR - sys,dibos-xsec: + syst_dibos-xsec: definition: '' treatment: MULT type: CORR - sys,elen-res: + syst_elen-res: definition: '' treatment: MULT type: CORR - sys,flavcomp-jes: + syst_flavcomp-jes: definition: '' treatment: MULT type: CORR - sys,detNP2-jes: + syst_detNP2-jes: definition: '' treatment: MULT type: CORR - sys,detNP3-jes: + syst_detNP3-jes: definition: '' treatment: MULT type: CORR - sys,jetvxfrac: + syst_jetvxfrac: definition: '' treatment: MULT type: CORR - sys,ltrig-eff: + syst_ltrig-eff: definition: '' treatment: MULT type: CORR - sys,btag-jes: + syst_btag-jes: definition: '' treatment: MULT type: CORR - sys,mup-scale: + syst_mup-scale: definition: '' treatment: MULT type: CORR - sys,singlephpt-jes: + syst_singlephpt-jes: definition: '' treatment: MULT type: CORR - sys,etmsoft-res: + syst_etmsoft-res: definition: '' treatment: MULT type: CORR - sys,detNP1-jes: + syst_detNP1-jes: definition: '' treatment: MULT type: CORR - sys,laltpar-ejet-fake: + syst_laltpar-ejet-fake: definition: '' treatment: MULT type: CORR - sys,statNP1-jes: + syst_statNP1-jes: definition: '' treatment: MULT type: CORR - sys,muid-res: + syst_muid-res: definition: '' treatment: MULT type: CORR - sys,pdf: + syst_pdf: definition: '' treatment: MULT type: CORR - sys,isr-fsr: + syst_isr-fsr: definition: '' treatment: MULT type: CORR - sys,zjet-xsec: + syst_zjet-xsec: definition: '' treatment: MULT type: CORR - sys,ps-model: + syst_ps-model: definition: '' treatment: MULT type: CORR - sys,flavres-jes: + syst_flavres-jes: definition: '' treatment: MULT type: CORR - sys,laltfakecr-mujet-fake: + syst_laltfakecr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,mums-res: + syst_mums-res: definition: '' treatment: MULT type: CORR - sys,mod-NP2-jes: + syst_mod-NP2-jes: definition: '' treatment: MULT type: CORR - sys,lid-eff: + syst_lid-eff: definition: '' treatment: MULT type: CORR - sys,mixNP2-jes: + syst_mixNP2-jes: definition: '' treatment: MULT type: CORR - sys,mixNP1-jes: + syst_mixNP1-jes: definition: '' treatment: MULT type: CORR - sys,btag-eff: + syst_btag-eff: definition: '' treatment: MULT type: CORR - sys,pileoffrho-jes: + syst_pileoffrho-jes: definition: '' treatment: MULT type: CORR - sys,modNP4-jes: + syst_modNP4-jes: definition: '' treatment: MULT type: CORR - sys,mcstat: + syst_mcstat: definition: '' treatment: MULT type: CORR - sys,modNP3-jes: + syst_modNP3-jes: definition: '' treatment: MULT type: CORR - sys,mod-NP1-jes: + syst_mod-NP1-jes: definition: '' treatment: MULT type: CORR @@ -345,61 +345,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 3.795466826868431e-14 - sys,singletop-xsec: 0.0002179152294247873 - sys,wjet-scale: 0.00047283342524136694 - sys,laltrealcr-mujet-fake: 0.00020016699000000002 - sys,eta-jes: 0.0036242895400030208 - sys,statNP3-jes: 8.368606126713518e-05 - sys,laltrealcr-ejet-fake: 8.973003e-05 - sys,pileoffmu-jes: 0.0008021541474912204 - sys,lstat-ejet-fake: 0.0005738472772603555 - sys,lstat-mujet-fake: 0.00016737212253427035 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.00425182296 - sys,statNP2-jes: 2.8458953190757042e-05 - sys,elen-scale: 2.289237245671798e-05 - sys,punch-jes: 2.2630736750337474e-05 - sys,pileoffnpv-jes: 0.00010301567605523092 - sys,lrec-eff: 2.0706930000000002e-05 - sys,pileoffpt-jes: 6.575333385274905e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 4.831617000000001e-05 - sys,laltfakecr-ejet-fake: 0.00033821319 - sys,laltpar-mujet-fake: 8.973003e-05 - sys,jetrec-eff: 0.0 - sys,c/tautag-eff: 0.0005384686638825927 - sys,dibos-xsec: 5.5218480000000005e-05 - sys,elen-res: 3.144151839883175e-05 - sys,flavcomp-jes: 0.0016116080923609592 - sys,detNP2-jes: 6.511626230760563e-05 - sys,detNP3-jes: 2.988787902397685e-05 - sys,jetvxfrac: 4.719383483001435e-05 - sys,ltrig-eff: 4.831617000000001e-05 - sys,btag-jes: 6.867711737015395e-05 - sys,mup-scale: 1.7932727414386108e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 9.05229470013499e-05 - sys,laltpar-ejet-fake: 0.00051767325 - sys,statNP1-jes: 0.0002218155878547702 - sys,muid-res: 2.0706930000000002e-05 - sys,pdf: 0.00216042303 - sys,isr-fsr: 0.001899712713575944 - sys,zjet-xsec: 0.00081447258 - sys,ps-model: 0.00033131088000000003 - sys,flavres-jes: 0.0009386316839227532 - sys,laltfakecr-mujet-fake: 1.3804620000000001e-05 - sys,mums-res: 7.592541e-05 - sys,mod-NP2-jes: 4.526147350067495e-05 - sys,lid-eff: 6.902310000000001e-06 - sys,mixNP2-jes: 6.996573098870815e-05 - sys,mixNP1-jes: 0.00012708500482329889 - sys,btag-eff: 0.0004452257473357511 - sys,pileoffrho-jes: 0.0005266941960903654 - sys,modNP4-jes: 3.5865454828772216e-05 - sys,mcstat: 0.0008627887500000001 - sys,modNP3-jes: 5.97757580479537e-05 - sys,mod-NP1-jes: 0.0006838513923559398 + syst_singletop-xsec: 0.0002179152294247873 + syst_wjet-scale: 0.00047283342524136694 + syst_laltrealcr-mujet-fake: 0.00020016699000000002 + syst_eta-jes: 0.0036242895400030208 + syst_statNP3-jes: 8.368606126713518e-05 + syst_laltrealcr-ejet-fake: 8.973003e-05 + syst_pileoffmu-jes: 0.0008021541474912204 + syst_lstat-ejet-fake: 0.0005738472772603555 + syst_lstat-mujet-fake: 0.00016737212253427035 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.00425182296 + syst_statNP2-jes: 2.8458953190757042e-05 + syst_elen-scale: 2.289237245671798e-05 + syst_punch-jes: 2.2630736750337474e-05 + syst_pileoffnpv-jes: 0.00010301567605523092 + syst_lrec-eff: 2.0706930000000002e-05 + syst_pileoffpt-jes: 6.575333385274905e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 4.831617000000001e-05 + syst_laltfakecr-ejet-fake: 0.00033821319 + syst_laltpar-mujet-fake: 8.973003e-05 + syst_jetrec-eff: 0.0 + syst_c/tautag-eff: 0.0005384686638825927 + syst_dibos-xsec: 5.5218480000000005e-05 + syst_elen-res: 3.144151839883175e-05 + syst_flavcomp-jes: 0.0016116080923609592 + syst_detNP2-jes: 6.511626230760563e-05 + syst_detNP3-jes: 2.988787902397685e-05 + syst_jetvxfrac: 4.719383483001435e-05 + syst_ltrig-eff: 4.831617000000001e-05 + syst_btag-jes: 6.867711737015395e-05 + syst_mup-scale: 1.7932727414386108e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 9.05229470013499e-05 + syst_laltpar-ejet-fake: 0.00051767325 + syst_statNP1-jes: 0.0002218155878547702 + syst_muid-res: 2.0706930000000002e-05 + syst_pdf: 0.00216042303 + syst_isr-fsr: 0.001899712713575944 + syst_zjet-xsec: 0.00081447258 + syst_ps-model: 0.00033131088000000003 + syst_flavres-jes: 0.0009386316839227532 + syst_laltfakecr-mujet-fake: 1.3804620000000001e-05 + syst_mums-res: 7.592541e-05 + syst_mod-NP2-jes: 4.526147350067495e-05 + syst_lid-eff: 6.902310000000001e-06 + syst_mixNP2-jes: 6.996573098870815e-05 + syst_mixNP1-jes: 0.00012708500482329889 + syst_btag-eff: 0.0004452257473357511 + syst_pileoffrho-jes: 0.0005266941960903654 + syst_modNP4-jes: 3.5865454828772216e-05 + syst_mcstat: 0.0008627887500000001 + syst_modNP3-jes: 5.97757580479537e-05 + syst_mod-NP1-jes: 0.0006838513923559398 - ArtUnc_1: -0.00016143691145700182 ArtUnc_2: -0.0006422143077395175 ArtUnc_3: -0.001957942043872373 @@ -425,61 +425,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 3.702459160576196e-14 - sys,singletop-xsec: 9.726683648077179e-05 - sys,wjet-scale: 0.0001253752 - sys,laltrealcr-mujet-fake: 5.641884e-05 - sys,eta-jes: 0.001986264791873774 - sys,statNP3-jes: 0.0001574382568966044 - sys,laltrealcr-ejet-fake: 1.253752e-05 - sys,pileoffmu-jes: 0.0005658228753202076 - sys,lstat-ejet-fake: 0.0004772807610246643 - sys,lstat-mujet-fake: 0.00020629840558865402 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.00122867696 - sys,statNP2-jes: 5.319219246757178e-05 - sys,elen-scale: 5.428905410227737e-06 - sys,punch-jes: 2.0791124820788316e-05 - sys,pileoffnpv-jes: 0.00043458379331505866 - sys,lrec-eff: 1.253752e-05 - sys,pileoffpt-jes: 0.00011400701361478251 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.00015045024000000001 - sys,laltfakecr-ejet-fake: 0.00044508195999999996 - sys,laltpar-mujet-fake: 0.00013164396 - sys,jetrec-eff: 3.13438e-05 - sys,c/tautag-eff: 0.00030406717147891585 - sys,dibos-xsec: 3.7612560000000004e-05 - sys,elen-res: 1.6286716230683214e-05 - sys,flavcomp-jes: 0.0005009029524604458 - sys,detNP2-jes: 0.00010243238241561699 - sys,detNP3-jes: 2.714452705113869e-05 - sys,jetvxfrac: 0.00019544059476819854 - sys,ltrig-eff: 2.855556080179831e-05 - sys,btag-jes: 0.0001523644008853026 - sys,mup-scale: 1.0857810820455475e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.0001574382568966044 - sys,laltpar-ejet-fake: 0.00031970675999999995 - sys,statNP1-jes: 0.0003148765137932088 - sys,muid-res: 6.26876e-06 - sys,pdf: 0.00154211496 - sys,isr-fsr: 0.0018561537953755937 - sys,zjet-xsec: 0.0006018009600000001 - sys,ps-model: 9.40314e-05 - sys,flavres-jes: 0.00046206332150148084 - sys,laltfakecr-mujet-fake: 2.507504e-05 - sys,mums-res: 3.13438e-05 - sys,mod-NP2-jes: 5.1216191207808496e-05 - sys,lid-eff: 0.0 - sys,mixNP2-jes: 5.9717959512505106e-05 - sys,mixNP1-jes: 0.00013029372984546571 - sys,btag-eff: 0.00017871464066294176 - sys,pileoffrho-jes: 0.00031323419904314857 - sys,modNP4-jes: 4.34312432818219e-05 - sys,mcstat: 0.0007522512 - sys,modNP3-jes: 9.772029738409927e-05 - sys,mod-NP1-jes: 0.0003857580167018007 + syst_singletop-xsec: 9.726683648077179e-05 + syst_wjet-scale: 0.0001253752 + syst_laltrealcr-mujet-fake: 5.641884e-05 + syst_eta-jes: 0.001986264791873774 + syst_statNP3-jes: 0.0001574382568966044 + syst_laltrealcr-ejet-fake: 1.253752e-05 + syst_pileoffmu-jes: 0.0005658228753202076 + syst_lstat-ejet-fake: 0.0004772807610246643 + syst_lstat-mujet-fake: 0.00020629840558865402 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.00122867696 + syst_statNP2-jes: 5.319219246757178e-05 + syst_elen-scale: 5.428905410227737e-06 + syst_punch-jes: 2.0791124820788316e-05 + syst_pileoffnpv-jes: 0.00043458379331505866 + syst_lrec-eff: 1.253752e-05 + syst_pileoffpt-jes: 0.00011400701361478251 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.00015045024000000001 + syst_laltfakecr-ejet-fake: 0.00044508195999999996 + syst_laltpar-mujet-fake: 0.00013164396 + syst_jetrec-eff: 3.13438e-05 + syst_c/tautag-eff: 0.00030406717147891585 + syst_dibos-xsec: 3.7612560000000004e-05 + syst_elen-res: 1.6286716230683214e-05 + syst_flavcomp-jes: 0.0005009029524604458 + syst_detNP2-jes: 0.00010243238241561699 + syst_detNP3-jes: 2.714452705113869e-05 + syst_jetvxfrac: 0.00019544059476819854 + syst_ltrig-eff: 2.855556080179831e-05 + syst_btag-jes: 0.0001523644008853026 + syst_mup-scale: 1.0857810820455475e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.0001574382568966044 + syst_laltpar-ejet-fake: 0.00031970675999999995 + syst_statNP1-jes: 0.0003148765137932088 + syst_muid-res: 6.26876e-06 + syst_pdf: 0.00154211496 + syst_isr-fsr: 0.0018561537953755937 + syst_zjet-xsec: 0.0006018009600000001 + syst_ps-model: 9.40314e-05 + syst_flavres-jes: 0.00046206332150148084 + syst_laltfakecr-mujet-fake: 2.507504e-05 + syst_mums-res: 3.13438e-05 + syst_mod-NP2-jes: 5.1216191207808496e-05 + syst_lid-eff: 0.0 + syst_mixNP2-jes: 5.9717959512505106e-05 + syst_mixNP1-jes: 0.00013029372984546571 + syst_btag-eff: 0.00017871464066294176 + syst_pileoffrho-jes: 0.00031323419904314857 + syst_modNP4-jes: 4.34312432818219e-05 + syst_mcstat: 0.0007522512 + syst_modNP3-jes: 9.772029738409927e-05 + syst_mod-NP1-jes: 0.0003857580167018007 - ArtUnc_1: -0.0011331839724454012 ArtUnc_2: 0.0008926633691618374 ArtUnc_3: -0.001054727537670549 @@ -505,61 +505,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 3.592655218274785e-14 - sys,singletop-xsec: 8.237036664335057e-06 - sys,wjet-scale: 9.195881874570595e-05 - sys,laltrealcr-mujet-fake: 6.457256e-05 - sys,eta-jes: 0.00023513949462510632 - sys,statNP3-jes: 4.7318173140103366e-05 - sys,laltrealcr-ejet-fake: 4.96712e-06 - sys,pileoffmu-jes: 9.345580510282708e-05 - sys,lstat-ejet-fake: 0.00013335121521301858 - sys,lstat-mujet-fake: 3.871486893281185e-05 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 3.4769839999999996e-05 - sys,statNP2-jes: 2.1508260518228807e-05 - sys,elen-scale: 8.603304207291522e-06 - sys,punch-jes: 1.7206608414583045e-05 - sys,pileoffnpv-jes: 0.0002451941699078084 - sys,lrec-eff: 4.96712e-06 - sys,pileoffpt-jes: 5.463832e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.00014404648 - sys,laltfakecr-ejet-fake: 0.00018378343999999998 - sys,laltpar-mujet-fake: 0.00017881631999999999 - sys,jetrec-eff: 5.4638319999999996e-05 - sys,c/tautag-eff: 6.218826305298453e-05 - sys,dibos-xsec: 3.4769839999999996e-05 - sys,elen-res: 4.96712e-06 - sys,flavcomp-jes: 0.00045697504 - sys,detNP2-jes: 6.967260082215392e-05 - sys,detNP3-jes: 3.3962172209138806e-05 - sys,jetvxfrac: 0.00011804097063334578 - sys,ltrig-eff: 1.986848e-05 - sys,btag-jes: 6.452478155468641e-05 - sys,mup-scale: 4.301652103645761e-06 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 0.00011678014974699081 - sys,laltpar-ejet-fake: 0.0001490136 - sys,statNP1-jes: 8.14289601070774e-05 - sys,muid-res: 2.4835600000000003e-05 - sys,pdf: 0.00073016664 - sys,isr-fsr: 0.0016359931923457403 - sys,zjet-xsec: 0.00011921088 - sys,ps-model: 0.002235204 - sys,flavres-jes: 0.0001548594757312474 - sys,laltfakecr-mujet-fake: 5.4638319999999996e-05 - sys,mums-res: 2.4835600000000003e-05 - sys,mod-NP2-jes: 3.441321682916609e-05 - sys,lid-eff: 9.93424e-06 - sys,mixNP2-jes: 0.0001108458207827936 - sys,mixNP1-jes: 0.00015541609695079076 - sys,btag-eff: 4.4704079999999996e-05 - sys,pileoffrho-jes: 0.00010754130259114401 - sys,modNP4-jes: 3.547233197598376e-05 - sys,mcstat: 0.0007351337599999999 - sys,modNP3-jes: 5.59214773473949e-05 - sys,mod-NP1-jes: 0.00012533983798800443 + syst_singletop-xsec: 8.237036664335057e-06 + syst_wjet-scale: 9.195881874570595e-05 + syst_laltrealcr-mujet-fake: 6.457256e-05 + syst_eta-jes: 0.00023513949462510632 + syst_statNP3-jes: 4.7318173140103366e-05 + syst_laltrealcr-ejet-fake: 4.96712e-06 + syst_pileoffmu-jes: 9.345580510282708e-05 + syst_lstat-ejet-fake: 0.00013335121521301858 + syst_lstat-mujet-fake: 3.871486893281185e-05 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 3.4769839999999996e-05 + syst_statNP2-jes: 2.1508260518228807e-05 + syst_elen-scale: 8.603304207291522e-06 + syst_punch-jes: 1.7206608414583045e-05 + syst_pileoffnpv-jes: 0.0002451941699078084 + syst_lrec-eff: 4.96712e-06 + syst_pileoffpt-jes: 5.463832e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.00014404648 + syst_laltfakecr-ejet-fake: 0.00018378343999999998 + syst_laltpar-mujet-fake: 0.00017881631999999999 + syst_jetrec-eff: 5.4638319999999996e-05 + syst_c/tautag-eff: 6.218826305298453e-05 + syst_dibos-xsec: 3.4769839999999996e-05 + syst_elen-res: 4.96712e-06 + syst_flavcomp-jes: 0.00045697504 + syst_detNP2-jes: 6.967260082215392e-05 + syst_detNP3-jes: 3.3962172209138806e-05 + syst_jetvxfrac: 0.00011804097063334578 + syst_ltrig-eff: 1.986848e-05 + syst_btag-jes: 6.452478155468641e-05 + syst_mup-scale: 4.301652103645761e-06 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 0.00011678014974699081 + syst_laltpar-ejet-fake: 0.0001490136 + syst_statNP1-jes: 8.14289601070774e-05 + syst_muid-res: 2.4835600000000003e-05 + syst_pdf: 0.00073016664 + syst_isr-fsr: 0.0016359931923457403 + syst_zjet-xsec: 0.00011921088 + syst_ps-model: 0.002235204 + syst_flavres-jes: 0.0001548594757312474 + syst_laltfakecr-mujet-fake: 5.4638319999999996e-05 + syst_mums-res: 2.4835600000000003e-05 + syst_mod-NP2-jes: 3.441321682916609e-05 + syst_lid-eff: 9.93424e-06 + syst_mixNP2-jes: 0.0001108458207827936 + syst_mixNP1-jes: 0.00015541609695079076 + syst_btag-eff: 4.4704079999999996e-05 + syst_pileoffrho-jes: 0.00010754130259114401 + syst_modNP4-jes: 3.547233197598376e-05 + syst_mcstat: 0.0007351337599999999 + syst_modNP3-jes: 5.59214773473949e-05 + syst_mod-NP1-jes: 0.00012533983798800443 - ArtUnc_1: -0.0007279330405026796 ArtUnc_2: 0.0009953017792790255 ArtUnc_3: 0.0004323649802874068 @@ -585,61 +585,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 3.372873363856128e-14 - sys,singletop-xsec: 0.00012569750108611257 - sys,wjet-scale: 0.00019439585000000003 - sys,laltrealcr-mujet-fake: 5.6551520000000004e-05 - sys,eta-jes: 0.0018737716942308046 - sys,statNP3-jes: 2.9145986281153705e-05 - sys,laltrealcr-ejet-fake: 7.775833999999999e-05 - sys,pileoffmu-jes: 0.0003325784145336083 - sys,lstat-ejet-fake: 0.00032551681973500815 - sys,lstat-mujet-fake: 0.00023569244228637687 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.0028629207 - sys,statNP2-jes: 4.285317132479579e-05 - sys,elen-scale: 2.754846728022586e-05 - sys,punch-jes: 3.3344123291820104e-05 - sys,pileoffnpv-jes: 0.0001377423364011293 - sys,lrec-eff: 1.060341e-05 - sys,pileoffpt-jes: 6.12188161782797e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.0 - sys,laltfakecr-ejet-fake: 0.00022620608000000001 - sys,laltpar-mujet-fake: 8.129281000000001e-05 - sys,jetrec-eff: 4.9482580000000005e-05 - sys,c/tautag-eff: 0.0002527269629845547 - sys,dibos-xsec: 4.241364e-05 - sys,elen-res: 8.65764801117486e-06 - sys,flavcomp-jes: 0.0007964487206289928 - sys,detNP2-jes: 4.591411213370977e-05 - sys,detNP3-jes: 3.673128970696782e-05 - sys,jetvxfrac: 7.040163860502165e-05 - sys,ltrig-eff: 2.8275760000000002e-05 - sys,btag-jes: 0.00013162045478330134 - sys,mup-scale: 6.12188161782797e-06 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 6.352220440468592e-05 - sys,laltpar-ejet-fake: 3.8879169999999997e-05 - sys,statNP1-jes: 0.0002330272077093726 - sys,muid-res: 1.060341e-05 - sys,pdf: 0.00040999852000000005 - sys,isr-fsr: 0.0029024046705965812 - sys,zjet-xsec: 0.00059379096 - sys,ps-model: 9.896516000000001e-05 - sys,flavres-jes: 0.000527145790703046 - sys,laltfakecr-mujet-fake: 3.8879169999999997e-05 - sys,mums-res: 3.181023e-05 - sys,mod-NP2-jes: 4.072316881720374e-05 - sys,lid-eff: 7.0689400000000005e-06 - sys,mixNP2-jes: 0.00011259103975924672 - sys,mixNP1-jes: 0.00012499986833897336 - sys,btag-eff: 0.0001838603980130452 - sys,pileoffrho-jes: 0.00038094881008866546 - sys,modNP4-jes: 3.2152022789701346e-05 - sys,mcstat: 0.00066448036 - sys,modNP3-jes: 5.922215167728774e-05 - sys,mod-NP1-jes: 0.0004279044901580858 + syst_singletop-xsec: 0.00012569750108611257 + syst_wjet-scale: 0.00019439585000000003 + syst_laltrealcr-mujet-fake: 5.6551520000000004e-05 + syst_eta-jes: 0.0018737716942308046 + syst_statNP3-jes: 2.9145986281153705e-05 + syst_laltrealcr-ejet-fake: 7.775833999999999e-05 + syst_pileoffmu-jes: 0.0003325784145336083 + syst_lstat-ejet-fake: 0.00032551681973500815 + syst_lstat-mujet-fake: 0.00023569244228637687 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.0028629207 + syst_statNP2-jes: 4.285317132479579e-05 + syst_elen-scale: 2.754846728022586e-05 + syst_punch-jes: 3.3344123291820104e-05 + syst_pileoffnpv-jes: 0.0001377423364011293 + syst_lrec-eff: 1.060341e-05 + syst_pileoffpt-jes: 6.12188161782797e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.0 + syst_laltfakecr-ejet-fake: 0.00022620608000000001 + syst_laltpar-mujet-fake: 8.129281000000001e-05 + syst_jetrec-eff: 4.9482580000000005e-05 + syst_c/tautag-eff: 0.0002527269629845547 + syst_dibos-xsec: 4.241364e-05 + syst_elen-res: 8.65764801117486e-06 + syst_flavcomp-jes: 0.0007964487206289928 + syst_detNP2-jes: 4.591411213370977e-05 + syst_detNP3-jes: 3.673128970696782e-05 + syst_jetvxfrac: 7.040163860502165e-05 + syst_ltrig-eff: 2.8275760000000002e-05 + syst_btag-jes: 0.00013162045478330134 + syst_mup-scale: 6.12188161782797e-06 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 6.352220440468592e-05 + syst_laltpar-ejet-fake: 3.8879169999999997e-05 + syst_statNP1-jes: 0.0002330272077093726 + syst_muid-res: 1.060341e-05 + syst_pdf: 0.00040999852000000005 + syst_isr-fsr: 0.0029024046705965812 + syst_zjet-xsec: 0.00059379096 + syst_ps-model: 9.896516000000001e-05 + syst_flavres-jes: 0.000527145790703046 + syst_laltfakecr-mujet-fake: 3.8879169999999997e-05 + syst_mums-res: 3.181023e-05 + syst_mod-NP2-jes: 4.072316881720374e-05 + syst_lid-eff: 7.0689400000000005e-06 + syst_mixNP2-jes: 0.00011259103975924672 + syst_mixNP1-jes: 0.00012499986833897336 + syst_btag-eff: 0.0001838603980130452 + syst_pileoffrho-jes: 0.00038094881008866546 + syst_modNP4-jes: 3.2152022789701346e-05 + syst_mcstat: 0.00066448036 + syst_modNP3-jes: 5.922215167728774e-05 + syst_mod-NP1-jes: 0.0004279044901580858 - ArtUnc_1: -0.00012223409172037826 ArtUnc_2: 0.0002535024566737831 ArtUnc_3: 0.00016124329449965463 @@ -665,58 +665,58 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 4.9400587559166885e-14 - sys,singletop-xsec: 7.862817936792636e-05 - sys,wjet-scale: 0.00012348589752541423 - sys,laltrealcr-mujet-fake: 7.394100000000002e-06 - sys,eta-jes: 0.001445552979637875 - sys,statNP3-jes: 2.232969231913418e-05 - sys,laltrealcr-ejet-fake: 6.358926e-05 - sys,pileoffmu-jes: 0.0004476179527230197 - sys,lstat-ejet-fake: 0.00037524383647397964 - sys,lstat-mujet-fake: 6.915756713172319e-05 - sys,etmsoft-scale: 0.0 - sys,hardscat-model: 0.00104700456 - sys,statNP2-jes: 2.5613913752490073e-06 - sys,elen-scale: 1.6649043939118544e-05 - sys,punch-jes: 2.5613913752490073e-06 - sys,pileoffnpv-jes: 9.020498958024607e-05 - sys,lrec-eff: 5.91528e-06 - sys,pileoffpt-jes: 4.5073696124675644e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.00014418874179629527 - sys,laltfakecr-ejet-fake: 0.00030611574000000006 - sys,laltpar-mujet-fake: 5.0279880000000005e-05 - sys,jetrec-eff: 1.774584e-05 - sys,c/tautag-eff: 0.00021371251536848724 - sys,dibos-xsec: 4.43646e-06 - sys,elen-res: 1.408765256386954e-05 - sys,flavcomp-jes: 0.0003630698865190374 - sys,detNP2-jes: 2.689460944011458e-05 - sys,detNP3-jes: 2.5613913752490073e-06 - sys,jetvxfrac: 9.0277691578411e-05 - sys,ltrig-eff: 1.114033494194407e-05 - sys,btag-jes: 5.9023261411863714e-05 - sys,mup-scale: 1.2806956876245036e-06 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0 - sys,detNP1-jes: 7.854121473476395e-05 - sys,laltpar-ejet-fake: 0.00026470878000000003 - sys,statNP1-jes: 8.875384357775612e-05 - sys,muid-res: 8.87292e-06 - sys,pdf: 0.00202154694 - sys,isr-fsr: 0.0018467631815545344 - sys,zjet-xsec: 0.00027949698000000005 - sys,ps-model: 0.0008118721800000002 - sys,flavres-jes: 0.0003772817335389182 - sys,laltfakecr-mujet-fake: 0.0 - sys,mums-res: 5.91528e-06 - sys,mod-NP2-jes: 2.5613913752490073e-06 - sys,lid-eff: 4.43646e-06 - sys,mixNP2-jes: 4.7414576165850094e-05 - sys,mixNP1-jes: 7.354063467910241e-05 - sys,btag-eff: 0.00015972625219086062 - sys,pileoffrho-jes: 0.00021247072114944474 - sys,modNP4-jes: 1.0245565500996029e-05 - sys,mcstat: 0.00036970500000000007 - sys,modNP3-jes: 1.729334409565426e-05 - sys,mod-NP1-jes: 0.00021883038511089542 + syst_singletop-xsec: 7.862817936792636e-05 + syst_wjet-scale: 0.00012348589752541423 + syst_laltrealcr-mujet-fake: 7.394100000000002e-06 + syst_eta-jes: 0.001445552979637875 + syst_statNP3-jes: 2.232969231913418e-05 + syst_laltrealcr-ejet-fake: 6.358926e-05 + syst_pileoffmu-jes: 0.0004476179527230197 + syst_lstat-ejet-fake: 0.00037524383647397964 + syst_lstat-mujet-fake: 6.915756713172319e-05 + syst_etmsoft-scale: 0.0 + syst_hardscat-model: 0.00104700456 + syst_statNP2-jes: 2.5613913752490073e-06 + syst_elen-scale: 1.6649043939118544e-05 + syst_punch-jes: 2.5613913752490073e-06 + syst_pileoffnpv-jes: 9.020498958024607e-05 + syst_lrec-eff: 5.91528e-06 + syst_pileoffpt-jes: 4.5073696124675644e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.00014418874179629527 + syst_laltfakecr-ejet-fake: 0.00030611574000000006 + syst_laltpar-mujet-fake: 5.0279880000000005e-05 + syst_jetrec-eff: 1.774584e-05 + syst_c/tautag-eff: 0.00021371251536848724 + syst_dibos-xsec: 4.43646e-06 + syst_elen-res: 1.408765256386954e-05 + syst_flavcomp-jes: 0.0003630698865190374 + syst_detNP2-jes: 2.689460944011458e-05 + syst_detNP3-jes: 2.5613913752490073e-06 + syst_jetvxfrac: 9.0277691578411e-05 + syst_ltrig-eff: 1.114033494194407e-05 + syst_btag-jes: 5.9023261411863714e-05 + syst_mup-scale: 1.2806956876245036e-06 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0 + syst_detNP1-jes: 7.854121473476395e-05 + syst_laltpar-ejet-fake: 0.00026470878000000003 + syst_statNP1-jes: 8.875384357775612e-05 + syst_muid-res: 8.87292e-06 + syst_pdf: 0.00202154694 + syst_isr-fsr: 0.0018467631815545344 + syst_zjet-xsec: 0.00027949698000000005 + syst_ps-model: 0.0008118721800000002 + syst_flavres-jes: 0.0003772817335389182 + syst_laltfakecr-mujet-fake: 0.0 + syst_mums-res: 5.91528e-06 + syst_mod-NP2-jes: 2.5613913752490073e-06 + syst_lid-eff: 4.43646e-06 + syst_mixNP2-jes: 4.7414576165850094e-05 + syst_mixNP1-jes: 7.354063467910241e-05 + syst_btag-eff: 0.00015972625219086062 + syst_pileoffrho-jes: 0.00021247072114944474 + syst_modNP4-jes: 1.0245565500996029e-05 + syst_mcstat: 0.00036970500000000007 + syst_modNP3-jes: 1.729334409565426e-05 + syst_mod-NP1-jes: 0.00021883038511089542 diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyttBar.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyttBar.yaml index 5312498dc3..b27e1b8b32 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyttBar.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyttBar.yaml @@ -99,223 +99,223 @@ definitions: definition: artificial uncertainty 25 treatment: ADD type: ATLAS8TEVTTB151104716unc25 - sys,singletop-xsec: + syst_singletop-xsec: definition: '' treatment: MULT type: CORR - sys,wjet-scale: + syst_wjet-scale: definition: '' treatment: MULT type: CORR - sys,laltrealcr-mujet-fake: + syst_laltrealcr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,eta-jes: + syst_eta-jes: definition: '' treatment: MULT type: CORR - sys,statNP3-jes: + syst_statNP3-jes: definition: '' treatment: MULT type: CORR - sys,laltrealcr-ejet-fake: + syst_laltrealcr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,pileoffmu-jes: + syst_pileoffmu-jes: definition: '' treatment: MULT type: CORR - sys,lstat-ejet-fake: + syst_lstat-ejet-fake: definition: '' treatment: MULT type: CORR - sys,lstat-mujet-fake: + syst_lstat-mujet-fake: definition: '' treatment: MULT type: CORR - sys,etmsoft-scale: + syst_etmsoft-scale: definition: '' treatment: MULT type: CORR - sys,hardscat-model: + syst_hardscat-model: definition: '' treatment: MULT type: CORR - sys,statNP2-jes: + syst_statNP2-jes: definition: '' treatment: MULT type: CORR - sys,elen-scale: + syst_elen-scale: definition: '' treatment: MULT type: CORR - sys,punch-jes: + syst_punch-jes: definition: '' treatment: MULT type: CORR - sys,pileoffnpv-jes: + syst_pileoffnpv-jes: definition: '' treatment: MULT type: CORR - sys,lrec-eff: + syst_lrec-eff: definition: '' treatment: MULT type: CORR - sys,pileoffpt-jes: + syst_pileoffpt-jes: definition: '' treatment: MULT type: CORR - sys,jeten-res: + syst_jeten-res: definition: '' treatment: MULT type: CORR - sys,lighttag-eff: + syst_lighttag-eff: definition: '' treatment: MULT type: CORR - sys,laltfakecr-ejet-fake: + syst_laltfakecr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,laltpar-mujet-fake: + syst_laltpar-mujet-fake: definition: '' treatment: MULT type: CORR - sys,jetrec-eff: + syst_jetrec-eff: definition: '' treatment: MULT type: CORR - sys,c/tautag-eff: + syst_c/tautag-eff: definition: '' treatment: MULT type: CORR - sys,dibos-xsec: + syst_dibos-xsec: definition: '' treatment: MULT type: CORR - sys,elen-res: + syst_elen-res: definition: '' treatment: MULT type: CORR - sys,flavcomp-jes: + syst_flavcomp-jes: definition: '' treatment: MULT type: CORR - sys,detNP2-jes: + syst_detNP2-jes: definition: '' treatment: MULT type: CORR - sys,detNP3-jes: + syst_detNP3-jes: definition: '' treatment: MULT type: CORR - sys,jetvxfrac: + syst_jetvxfrac: definition: '' treatment: MULT type: CORR - sys,ltrig-eff: + syst_ltrig-eff: definition: '' treatment: MULT type: CORR - sys,btag-jes: + syst_btag-jes: definition: '' treatment: MULT type: CORR - sys,mup-scale: + syst_mup-scale: definition: '' treatment: MULT type: CORR - sys,singlephpt-jes: + syst_singlephpt-jes: definition: '' treatment: MULT type: CORR - sys,etmsoft-res: + syst_etmsoft-res: definition: '' treatment: MULT type: CORR - sys,detNP1-jes: + syst_detNP1-jes: definition: '' treatment: MULT type: CORR - sys,laltpar-ejet-fake: + syst_laltpar-ejet-fake: definition: '' treatment: MULT type: CORR - sys,statNP1-jes: + syst_statNP1-jes: definition: '' treatment: MULT type: CORR - sys,muid-res: + syst_muid-res: definition: '' treatment: MULT type: CORR - sys,pdf: + syst_pdf: definition: '' treatment: MULT type: CORR - sys,isr-fsr: + syst_isr-fsr: definition: '' treatment: MULT type: CORR - sys,zjet-xsec: + syst_zjet-xsec: definition: '' treatment: MULT type: CORR - sys,ps-model: + syst_ps-model: definition: '' treatment: MULT type: CORR - sys,flavres-jes: + syst_flavres-jes: definition: '' treatment: MULT type: CORR - sys,laltfakecr-mujet-fake: + syst_laltfakecr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,mums-res: + syst_mums-res: definition: '' treatment: MULT type: CORR - sys,mod-NP2-jes: + syst_mod-NP2-jes: definition: '' treatment: MULT type: CORR - sys,lid-eff: + syst_lid-eff: definition: '' treatment: MULT type: CORR - sys,mixNP2-jes: + syst_mixNP2-jes: definition: '' treatment: MULT type: CORR - sys,mixNP1-jes: + syst_mixNP1-jes: definition: '' treatment: MULT type: CORR - sys,btag-eff: + syst_btag-eff: definition: '' treatment: MULT type: CORR - sys,pileoffrho-jes: + syst_pileoffrho-jes: definition: '' treatment: MULT type: CORR - sys,modNP4-jes: + syst_modNP4-jes: definition: '' treatment: MULT type: CORR - sys,mcstat: + syst_mcstat: definition: '' treatment: MULT type: CORR - sys,modNP3-jes: + syst_modNP3-jes: definition: '' treatment: MULT type: CORR - sys,mod-NP1-jes: + syst_mod-NP1-jes: definition: '' treatment: MULT type: CORR @@ -349,61 +349,61 @@ bins: ArtUnc_23: 3.436337624116301e-07 ArtUnc_24: 6.280876920921697e-07 ArtUnc_25: 7.897498186072514e-05 - sys,singletop-xsec: 0.6465201649775901 - sys,wjet-scale: 0.92026665 - sys,laltrealcr-mujet-fake: 0.08204787 - sys,eta-jes: 0.3021677778034197 - sys,statNP3-jes: 0.059749447994710374 - sys,laltrealcr-ejet-fake: 0.13748562 - sys,pileoffmu-jes: 0.30360254883848464 - sys,lstat-ejet-fake: 0.929329842035627 - sys,lstat-mujet-fake: 0.011522519958876183 - sys,etmsoft-scale: 0.041957258190160614 - sys,hardscat-model: 7.821157770000001 - sys,statNP2-jes: 0.16737427033080593 - sys,elen-scale: 0.13895757075072473 - sys,punch-jes: 0.008870040000000001 - sys,pileoffnpv-jes: 1.3483167430433844 - sys,lrec-eff: 0.5166798300000001 - sys,pileoffpt-jes: 0.018652146122017033 - sys,jeten-res: 0.0 - sys,lighttag-eff: 1.1231699095256267 - sys,laltfakecr-ejet-fake: 0.9091790999999999 - sys,laltpar-mujet-fake: 0.30823389 - sys,jetrec-eff: 0.10644048 - sys,c/tautag-eff: 2.135464432709039 - sys,dibos-xsec: 0.1995759 - sys,elen-res: 0.06060743269826482 - sys,flavcomp-jes: 3.990461309010172 - sys,detNP2-jes: 0.42569694892944127 - sys,detNP3-jes: 0.06881430540153916 - sys,jetvxfrac: 1.5079899543322046 - sys,ltrig-eff: 2.6743170600000004 - sys,btag-jes: 1.1608929592683197 - sys,mup-scale: 0.025931596452275645 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.051851339814942825 - sys,detNP1-jes: 0.4023604525883382 - sys,laltpar-ejet-fake: 0.7805635200000001 - sys,statNP1-jes: 1.9688950861068713 - sys,muid-res: 0.017740080000000002 - sys,pdf: 0.30379887000000005 - sys,isr-fsr: 13.329490561425136 - sys,zjet-xsec: 2.21085747 - sys,ps-model: 7.21356003 - sys,flavres-jes: 2.515847676101649 - sys,laltfakecr-mujet-fake: 0.60316272 - sys,mums-res: 0.015522570000000001 - sys,mod-NP2-jes: 0.1601527418751287 - sys,lid-eff: 2.9781159300000004 - sys,mixNP2-jes: 0.854066714793191 - sys,mixNP1-jes: 0.658659264989245 - sys,btag-eff: 9.368774988924878 - sys,pileoffrho-jes: 3.7496925332059003 - sys,modNP4-jes: 0.06279900621252059 - sys,mcstat: 0.34149654 - sys,modNP3-jes: 0.3073212059693745 - sys,mod-NP1-jes: 5.02721352710252 + syst_singletop-xsec: 0.6465201649775901 + syst_wjet-scale: 0.92026665 + syst_laltrealcr-mujet-fake: 0.08204787 + syst_eta-jes: 0.3021677778034197 + syst_statNP3-jes: 0.059749447994710374 + syst_laltrealcr-ejet-fake: 0.13748562 + syst_pileoffmu-jes: 0.30360254883848464 + syst_lstat-ejet-fake: 0.929329842035627 + syst_lstat-mujet-fake: 0.011522519958876183 + syst_etmsoft-scale: 0.041957258190160614 + syst_hardscat-model: 7.821157770000001 + syst_statNP2-jes: 0.16737427033080593 + syst_elen-scale: 0.13895757075072473 + syst_punch-jes: 0.008870040000000001 + syst_pileoffnpv-jes: 1.3483167430433844 + syst_lrec-eff: 0.5166798300000001 + syst_pileoffpt-jes: 0.018652146122017033 + syst_jeten-res: 0.0 + syst_lighttag-eff: 1.1231699095256267 + syst_laltfakecr-ejet-fake: 0.9091790999999999 + syst_laltpar-mujet-fake: 0.30823389 + syst_jetrec-eff: 0.10644048 + syst_c/tautag-eff: 2.135464432709039 + syst_dibos-xsec: 0.1995759 + syst_elen-res: 0.06060743269826482 + syst_flavcomp-jes: 3.990461309010172 + syst_detNP2-jes: 0.42569694892944127 + syst_detNP3-jes: 0.06881430540153916 + syst_jetvxfrac: 1.5079899543322046 + syst_ltrig-eff: 2.6743170600000004 + syst_btag-jes: 1.1608929592683197 + syst_mup-scale: 0.025931596452275645 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.051851339814942825 + syst_detNP1-jes: 0.4023604525883382 + syst_laltpar-ejet-fake: 0.7805635200000001 + syst_statNP1-jes: 1.9688950861068713 + syst_muid-res: 0.017740080000000002 + syst_pdf: 0.30379887000000005 + syst_isr-fsr: 13.329490561425136 + syst_zjet-xsec: 2.21085747 + syst_ps-model: 7.21356003 + syst_flavres-jes: 2.515847676101649 + syst_laltfakecr-mujet-fake: 0.60316272 + syst_mums-res: 0.015522570000000001 + syst_mod-NP2-jes: 0.1601527418751287 + syst_lid-eff: 2.9781159300000004 + syst_mixNP2-jes: 0.854066714793191 + syst_mixNP1-jes: 0.658659264989245 + syst_btag-eff: 9.368774988924878 + syst_pileoffrho-jes: 3.7496925332059003 + syst_modNP4-jes: 0.06279900621252059 + syst_mcstat: 0.34149654 + syst_modNP3-jes: 0.3073212059693745 + syst_mod-NP1-jes: 5.02721352710252 lumi: 6.209028 - ArtUnc_1: -0.2705759243522331 ArtUnc_2: 0.9235362730417213 @@ -430,61 +430,61 @@ bins: ArtUnc_23: 3.269639826816714e-07 ArtUnc_24: 5.961267146031061e-07 ArtUnc_25: 7.513941463893221e-05 - sys,singletop-xsec: 0.5643446861334569 - sys,wjet-scale: 0.874534 - sys,laltrealcr-mujet-fake: 0.1057576 - sys,eta-jes: 0.4800974339928928 - sys,statNP3-jes: 0.06379781313618829 - sys,laltrealcr-ejet-fake: 0.1362646 - sys,pileoffmu-jes: 0.25999251557396796 - sys,lstat-ejet-fake: 0.7898551051378158 - sys,lstat-mujet-fake: 0.01761322466216791 - sys,etmsoft-scale: 0.05550262184969284 - sys,hardscat-model: 7.9033468000000004 - sys,statNP2-jes: 0.15726153836447104 - sys,elen-scale: 0.13294193153595293 - sys,punch-jes: 0.0020338 - sys,pileoffnpv-jes: 1.2356347673725718 - sys,lrec-eff: 0.47794299999999995 - sys,pileoffpt-jes: 0.021786355706955668 - sys,jeten-res: 0.0 - sys,lighttag-eff: 1.0291028 - sys,laltfakecr-ejet-fake: 0.8155538 - sys,laltpar-mujet-fake: 0.3437122 - sys,jetrec-eff: 0.1077914 - sys,c/tautag-eff: 1.947364031018204 - sys,dibos-xsec: 0.1850758 - sys,elen-res: 0.08515285254951828 - sys,flavcomp-jes: 3.779962021496337 - sys,detNP2-jes: 0.37937866511210144 - sys,detNP3-jes: 0.059050289388960654 - sys,jetvxfrac: 1.3399933271705347 - sys,ltrig-eff: 2.4751346 - sys,btag-jes: 1.0220097957068461 - sys,mup-scale: 0.0223718 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0792595109797556 - sys,detNP1-jes: 0.3413937915836783 - sys,laltpar-ejet-fake: 0.5816667999999999 - sys,statNP1-jes: 1.8233022671499397 - sys,muid-res: 0.0020338 - sys,pdf: 0.16677160000000002 - sys,isr-fsr: 12.498723608736798 - sys,zjet-xsec: 2.0622732 - sys,ps-model: 7.7833526 - sys,flavres-jes: 2.311575199373386 - sys,laltfakecr-mujet-fake: 0.42913179999999995 - sys,mums-res: 0.018304199999999996 - sys,mod-NP2-jes: 0.15409642391188055 - sys,lid-eff: 2.7171568 - sys,mixNP2-jes: 0.7822195480499001 - sys,mixNP1-jes: 0.5777021340498839 - sys,btag-eff: 8.602785222343691 - sys,pileoffrho-jes: 3.3945596442512715 - sys,modNP4-jes: 0.031556686176308184 - sys,mcstat: 0.29083339999999996 - sys,modNP3-jes: 0.2688368973346293 - sys,mod-NP1-jes: 4.57809193157874 + syst_singletop-xsec: 0.5643446861334569 + syst_wjet-scale: 0.874534 + syst_laltrealcr-mujet-fake: 0.1057576 + syst_eta-jes: 0.4800974339928928 + syst_statNP3-jes: 0.06379781313618829 + syst_laltrealcr-ejet-fake: 0.1362646 + syst_pileoffmu-jes: 0.25999251557396796 + syst_lstat-ejet-fake: 0.7898551051378158 + syst_lstat-mujet-fake: 0.01761322466216791 + syst_etmsoft-scale: 0.05550262184969284 + syst_hardscat-model: 7.9033468000000004 + syst_statNP2-jes: 0.15726153836447104 + syst_elen-scale: 0.13294193153595293 + syst_punch-jes: 0.0020338 + syst_pileoffnpv-jes: 1.2356347673725718 + syst_lrec-eff: 0.47794299999999995 + syst_pileoffpt-jes: 0.021786355706955668 + syst_jeten-res: 0.0 + syst_lighttag-eff: 1.0291028 + syst_laltfakecr-ejet-fake: 0.8155538 + syst_laltpar-mujet-fake: 0.3437122 + syst_jetrec-eff: 0.1077914 + syst_c/tautag-eff: 1.947364031018204 + syst_dibos-xsec: 0.1850758 + syst_elen-res: 0.08515285254951828 + syst_flavcomp-jes: 3.779962021496337 + syst_detNP2-jes: 0.37937866511210144 + syst_detNP3-jes: 0.059050289388960654 + syst_jetvxfrac: 1.3399933271705347 + syst_ltrig-eff: 2.4751346 + syst_btag-jes: 1.0220097957068461 + syst_mup-scale: 0.0223718 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0792595109797556 + syst_detNP1-jes: 0.3413937915836783 + syst_laltpar-ejet-fake: 0.5816667999999999 + syst_statNP1-jes: 1.8233022671499397 + syst_muid-res: 0.0020338 + syst_pdf: 0.16677160000000002 + syst_isr-fsr: 12.498723608736798 + syst_zjet-xsec: 2.0622732 + syst_ps-model: 7.7833526 + syst_flavres-jes: 2.311575199373386 + syst_laltfakecr-mujet-fake: 0.42913179999999995 + syst_mums-res: 0.018304199999999996 + syst_mod-NP2-jes: 0.15409642391188055 + syst_lid-eff: 2.7171568 + syst_mixNP2-jes: 0.7822195480499001 + syst_mixNP1-jes: 0.5777021340498839 + syst_btag-eff: 8.602785222343691 + syst_pileoffrho-jes: 3.3945596442512715 + syst_modNP4-jes: 0.031556686176308184 + syst_mcstat: 0.29083339999999996 + syst_modNP3-jes: 0.2688368973346293 + syst_mod-NP1-jes: 4.57809193157874 lumi: 5.69464 - ArtUnc_1: 0.3343208362749091 ArtUnc_2: 0.8386185770033565 @@ -511,61 +511,61 @@ bins: ArtUnc_23: 3.1023831837540187e-07 ArtUnc_24: 5.631215939812193e-07 ArtUnc_25: 7.132485580736777e-05 - sys,singletop-xsec: 0.4483914562400547 - sys,wjet-scale: 0.6521277000000001 - sys,laltrealcr-mujet-fake: 0.22131795 - sys,eta-jes: 0.7322961901819527 - sys,statNP3-jes: 0.038820605452774694 - sys,laltrealcr-ejet-fake: 0.06926745000000001 - sys,pileoffmu-jes: 0.2766613009598865 - sys,lstat-ejet-fake: 0.5486649819088574 - sys,lstat-mujet-fake: 0.019020386039507054 - sys,etmsoft-scale: 0.03365145222374325 - sys,hardscat-model: 6.1411507499999995 - sys,statNP2-jes: 0.11747145669166563 - sys,elen-scale: 0.20775427065744043 - sys,punch-jes: 0.005852426473694479 - sys,pileoffnpv-jes: 1.056986944967185 - sys,lrec-eff: 0.40377854999999996 - sys,pileoffpt-jes: 0.024872812513201538 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.8649984 - sys,laltfakecr-ejet-fake: 0.5710341000000001 - sys,laltpar-mujet-fake: 0.3142377 - sys,jetrec-eff: 0.0810936 - sys,c/tautag-eff: 1.5703442293974599 - sys,dibos-xsec: 0.11319314999999999 - sys,elen-res: 0.04985309031696907 - sys,flavcomp-jes: 3.170263953897682 - sys,detNP2-jes: 0.2729742920570395 - sys,detNP3-jes: 0.04922652603853434 - sys,jetvxfrac: 1.1046212730247185 - sys,ltrig-eff: 2.1151914 - sys,btag-jes: 0.870096273868017 - sys,mup-scale: 0.03634281445804487 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.06291358459221566 - sys,detNP1-jes: 0.28407889500433675 - sys,laltpar-ejet-fake: 0.45108315 - sys,statNP1-jes: 1.5130727310397962 - sys,muid-res: 0.0 - sys,pdf: 0.04899405 - sys,isr-fsr: 11.165689443251958 - sys,zjet-xsec: 1.7215495499999998 - sys,ps-model: 7.21901985 - sys,flavres-jes: 1.959223595907605 - sys,laltfakecr-mujet-fake: 0.3750579 - sys,mums-res: 0.04223625 - sys,mod-NP2-jes: 0.10221870595995076 - sys,lid-eff: 2.2199373000000002 - sys,mixNP2-jes: 0.5997595089952035 - sys,mixNP1-jes: 0.46121985000000004 - sys,btag-eff: 7.1411488232046825 - sys,pileoffrho-jes: 2.8639057892304884 - sys,modNP4-jes: 0.035114558842166875 - sys,mcstat: 0.28720650000000003 - sys,modNP3-jes: 0.2326707571315997 - sys,mod-NP1-jes: 3.8291922797999702 + syst_singletop-xsec: 0.4483914562400547 + syst_wjet-scale: 0.6521277000000001 + syst_laltrealcr-mujet-fake: 0.22131795 + syst_eta-jes: 0.7322961901819527 + syst_statNP3-jes: 0.038820605452774694 + syst_laltrealcr-ejet-fake: 0.06926745000000001 + syst_pileoffmu-jes: 0.2766613009598865 + syst_lstat-ejet-fake: 0.5486649819088574 + syst_lstat-mujet-fake: 0.019020386039507054 + syst_etmsoft-scale: 0.03365145222374325 + syst_hardscat-model: 6.1411507499999995 + syst_statNP2-jes: 0.11747145669166563 + syst_elen-scale: 0.20775427065744043 + syst_punch-jes: 0.005852426473694479 + syst_pileoffnpv-jes: 1.056986944967185 + syst_lrec-eff: 0.40377854999999996 + syst_pileoffpt-jes: 0.024872812513201538 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.8649984 + syst_laltfakecr-ejet-fake: 0.5710341000000001 + syst_laltpar-mujet-fake: 0.3142377 + syst_jetrec-eff: 0.0810936 + syst_c/tautag-eff: 1.5703442293974599 + syst_dibos-xsec: 0.11319314999999999 + syst_elen-res: 0.04985309031696907 + syst_flavcomp-jes: 3.170263953897682 + syst_detNP2-jes: 0.2729742920570395 + syst_detNP3-jes: 0.04922652603853434 + syst_jetvxfrac: 1.1046212730247185 + syst_ltrig-eff: 2.1151914 + syst_btag-jes: 0.870096273868017 + syst_mup-scale: 0.03634281445804487 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.06291358459221566 + syst_detNP1-jes: 0.28407889500433675 + syst_laltpar-ejet-fake: 0.45108315 + syst_statNP1-jes: 1.5130727310397962 + syst_muid-res: 0.0 + syst_pdf: 0.04899405 + syst_isr-fsr: 11.165689443251958 + syst_zjet-xsec: 1.7215495499999998 + syst_ps-model: 7.21901985 + syst_flavres-jes: 1.959223595907605 + syst_laltfakecr-mujet-fake: 0.3750579 + syst_mums-res: 0.04223625 + syst_mod-NP2-jes: 0.10221870595995076 + syst_lid-eff: 2.2199373000000002 + syst_mixNP2-jes: 0.5997595089952035 + syst_mixNP1-jes: 0.46121985000000004 + syst_btag-eff: 7.1411488232046825 + syst_pileoffrho-jes: 2.8639057892304884 + syst_modNP4-jes: 0.035114558842166875 + syst_mcstat: 0.28720650000000003 + syst_modNP3-jes: 0.2326707571315997 + syst_mod-NP1-jes: 3.8291922797999702 lumi: 4.73046 - ArtUnc_1: 0.20774308609350942 ArtUnc_2: 0.04129425277120698 @@ -592,61 +592,61 @@ bins: ArtUnc_23: 3.4661933070274466e-07 ArtUnc_24: 6.241136601870737e-07 ArtUnc_25: 7.971300588685219e-05 - sys,singletop-xsec: 0.2968835668462463 - sys,wjet-scale: 0.42046742 - sys,laltrealcr-mujet-fake: 0.60956031 - sys,eta-jes: 0.8826390028268558 - sys,statNP3-jes: 0.055599418786387494 - sys,laltrealcr-ejet-fake: 0.0117449 - sys,pileoffmu-jes: 0.2995133728059289 - sys,lstat-ejet-fake: 0.33056990735950526 - sys,lstat-mujet-fake: 0.042719803412612986 - sys,etmsoft-scale: 0.03158046148415662 - sys,hardscat-model: 4.30802932 - sys,statNP2-jes: 0.08730814553243357 - sys,elen-scale: 0.196649052356332 - sys,punch-jes: 0.0030514145294723563 - sys,pileoffnpv-jes: 0.7670570721744071 - sys,lrec-eff: 0.29949495 - sys,pileoffpt-jes: 0.020770549324013437 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.6324634102597463 - sys,laltfakecr-ejet-fake: 0.34530006 - sys,laltpar-mujet-fake: 0.32650822000000007 - sys,jetrec-eff: 0.06342246 - sys,c/tautag-eff: 1.070547957131054 - sys,dibos-xsec: 0.10452961000000001 - sys,elen-res: 0.022377039882797278 - sys,flavcomp-jes: 2.301596708569426 - sys,detNP2-jes: 0.22139292267356533 - sys,detNP3-jes: 0.04280045253747675 - sys,jetvxfrac: 0.7693931507380046 - sys,ltrig-eff: 1.56676966 - sys,btag-jes: 0.6456178580782034 - sys,mup-scale: 0.027469008678412475 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.03559983617717749 - sys,detNP1-jes: 0.2671977656452345 - sys,laltpar-ejet-fake: 0.34060209999999996 - sys,statNP1-jes: 1.0682144740170072 - sys,muid-res: 0.01644286 - sys,pdf: 0.09748267000000001 - sys,isr-fsr: 8.778926986372753 - sys,zjet-xsec: 1.14982571 - sys,ps-model: 4.743765109999999 - sys,flavres-jes: 1.4636558278449832 - sys,laltfakecr-mujet-fake: 0.19966330000000004 - sys,mums-res: 0.00822143 - sys,mod-NP2-jes: 0.07261682757579178 - sys,lid-eff: 1.50569618 - sys,mixNP2-jes: 0.47159356801588453 - sys,mixNP1-jes: 0.4160381124414298 - sys,btag-eff: 4.970931211744019 - sys,pileoffrho-jes: 2.075454103240018 - sys,modNP4-jes: 0.03722873939862153 - sys,mcstat: 0.22550208000000002 - sys,modNP3-jes: 0.1386296275033342 - sys,mod-NP1-jes: 2.7988219915213457 + syst_singletop-xsec: 0.2968835668462463 + syst_wjet-scale: 0.42046742 + syst_laltrealcr-mujet-fake: 0.60956031 + syst_eta-jes: 0.8826390028268558 + syst_statNP3-jes: 0.055599418786387494 + syst_laltrealcr-ejet-fake: 0.0117449 + syst_pileoffmu-jes: 0.2995133728059289 + syst_lstat-ejet-fake: 0.33056990735950526 + syst_lstat-mujet-fake: 0.042719803412612986 + syst_etmsoft-scale: 0.03158046148415662 + syst_hardscat-model: 4.30802932 + syst_statNP2-jes: 0.08730814553243357 + syst_elen-scale: 0.196649052356332 + syst_punch-jes: 0.0030514145294723563 + syst_pileoffnpv-jes: 0.7670570721744071 + syst_lrec-eff: 0.29949495 + syst_pileoffpt-jes: 0.020770549324013437 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.6324634102597463 + syst_laltfakecr-ejet-fake: 0.34530006 + syst_laltpar-mujet-fake: 0.32650822000000007 + syst_jetrec-eff: 0.06342246 + syst_c/tautag-eff: 1.070547957131054 + syst_dibos-xsec: 0.10452961000000001 + syst_elen-res: 0.022377039882797278 + syst_flavcomp-jes: 2.301596708569426 + syst_detNP2-jes: 0.22139292267356533 + syst_detNP3-jes: 0.04280045253747675 + syst_jetvxfrac: 0.7693931507380046 + syst_ltrig-eff: 1.56676966 + syst_btag-jes: 0.6456178580782034 + syst_mup-scale: 0.027469008678412475 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.03559983617717749 + syst_detNP1-jes: 0.2671977656452345 + syst_laltpar-ejet-fake: 0.34060209999999996 + syst_statNP1-jes: 1.0682144740170072 + syst_muid-res: 0.01644286 + syst_pdf: 0.09748267000000001 + syst_isr-fsr: 8.778926986372753 + syst_zjet-xsec: 1.14982571 + syst_ps-model: 4.743765109999999 + syst_flavres-jes: 1.4636558278449832 + syst_laltfakecr-mujet-fake: 0.19966330000000004 + syst_mums-res: 0.00822143 + syst_mod-NP2-jes: 0.07261682757579178 + syst_lid-eff: 1.50569618 + syst_mixNP2-jes: 0.47159356801588453 + syst_mixNP1-jes: 0.4160381124414298 + syst_btag-eff: 4.970931211744019 + syst_pileoffrho-jes: 2.075454103240018 + syst_modNP4-jes: 0.03722873939862153 + syst_mcstat: 0.22550208000000002 + syst_modNP3-jes: 0.1386296275033342 + syst_mod-NP1-jes: 2.7988219915213457 lumi: 3.288572 - ArtUnc_1: 0.00678491890284974 ArtUnc_2: -0.05413053897544706 @@ -673,59 +673,59 @@ bins: ArtUnc_23: 5.716637545573051e-07 ArtUnc_24: 1.0077391641701514e-06 ArtUnc_25: 0.0001315846277340393 - sys,singletop-xsec: 0.06792676511515891 - sys,wjet-scale: 0.12224527199999999 - sys,laltrealcr-mujet-fake: 0.423717352 - sys,eta-jes: 0.3949289236353195 - sys,statNP3-jes: 0.025092195766552434 - sys,laltrealcr-ejet-fake: 0.01159508 - sys,pileoffmu-jes: 0.12167293172442757 - sys,lstat-ejet-fake: 0.0608236106814151 - sys,lstat-mujet-fake: 0.1380007393290597 - sys,etmsoft-scale: 0.02941568815843845 - sys,hardscat-model: 1.9979979279999998 - sys,statNP2-jes: 0.022309116929549316 - sys,elen-scale: 0.1082580805538335 - sys,punch-jes: 0.003049829483020977 - sys,pileoffnpv-jes: 0.18157544405082834 - sys,lrec-eff: 0.105680872 - sys,pileoffpt-jes: 0.01004163383891287 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.18535578402853783 - sys,laltfakecr-ejet-fake: 0.07785268 - sys,laltpar-mujet-fake: 0.270331008 - sys,jetrec-eff: 0.016895688 - sys,c/tautag-eff: 0.2936869054258171 - sys,dibos-xsec: 0.025840464 - sys,elen-res: 0.01441007599154314 - sys,flavcomp-jes: 0.697912309892471 - sys,detNP2-jes: 0.06000886856533444 - sys,detNP3-jes: 0.01573792352446383 - sys,jetvxfrac: 0.20941647107540798 - sys,ltrig-eff: 0.5088583680000001 - sys,btag-jes: 0.18490678793994703 - sys,mup-scale: 0.013678423774864704 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.01922255620591892 - sys,detNP1-jes: 0.08998540741447679 - sys,laltpar-ejet-fake: 0.061288279999999994 - sys,statNP1-jes: 0.30031485610604075 - sys,muid-res: 0.001325152 - sys,pdf: 0.7818396799999999 - sys,isr-fsr: 3.1070164144188883 - sys,zjet-xsec: 0.310416856 - sys,ps-model: 1.1048454799999998 - sys,flavres-jes: 0.4409644885669146 - sys,laltfakecr-mujet-fake: 0.109656328 - sys,mums-res: 0.024184024 - sys,mod-NP2-jes: 0.025182246675355083 - sys,lid-eff: 0.4223922 - sys,mixNP2-jes: 0.12919159516769682 - sys,mixNP1-jes: 0.123901712 - sys,btag-eff: 1.405136836207867 - sys,pileoffrho-jes: 0.6014818754420922 - sys,modNP4-jes: 0.012928723773509588 - sys,mcstat: 0.09706738399999999 - sys,modNP3-jes: 0.030768791008600648 - sys,mod-NP1-jes: 0.8140959578713352 + syst_singletop-xsec: 0.06792676511515891 + syst_wjet-scale: 0.12224527199999999 + syst_laltrealcr-mujet-fake: 0.423717352 + syst_eta-jes: 0.3949289236353195 + syst_statNP3-jes: 0.025092195766552434 + syst_laltrealcr-ejet-fake: 0.01159508 + syst_pileoffmu-jes: 0.12167293172442757 + syst_lstat-ejet-fake: 0.0608236106814151 + syst_lstat-mujet-fake: 0.1380007393290597 + syst_etmsoft-scale: 0.02941568815843845 + syst_hardscat-model: 1.9979979279999998 + syst_statNP2-jes: 0.022309116929549316 + syst_elen-scale: 0.1082580805538335 + syst_punch-jes: 0.003049829483020977 + syst_pileoffnpv-jes: 0.18157544405082834 + syst_lrec-eff: 0.105680872 + syst_pileoffpt-jes: 0.01004163383891287 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.18535578402853783 + syst_laltfakecr-ejet-fake: 0.07785268 + syst_laltpar-mujet-fake: 0.270331008 + syst_jetrec-eff: 0.016895688 + syst_c/tautag-eff: 0.2936869054258171 + syst_dibos-xsec: 0.025840464 + syst_elen-res: 0.01441007599154314 + syst_flavcomp-jes: 0.697912309892471 + syst_detNP2-jes: 0.06000886856533444 + syst_detNP3-jes: 0.01573792352446383 + syst_jetvxfrac: 0.20941647107540798 + syst_ltrig-eff: 0.5088583680000001 + syst_btag-jes: 0.18490678793994703 + syst_mup-scale: 0.013678423774864704 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.01922255620591892 + syst_detNP1-jes: 0.08998540741447679 + syst_laltpar-ejet-fake: 0.061288279999999994 + syst_statNP1-jes: 0.30031485610604075 + syst_muid-res: 0.001325152 + syst_pdf: 0.7818396799999999 + syst_isr-fsr: 3.1070164144188883 + syst_zjet-xsec: 0.310416856 + syst_ps-model: 1.1048454799999998 + syst_flavres-jes: 0.4409644885669146 + syst_laltfakecr-mujet-fake: 0.109656328 + syst_mums-res: 0.024184024 + syst_mod-NP2-jes: 0.025182246675355083 + syst_lid-eff: 0.4223922 + syst_mixNP2-jes: 0.12919159516769682 + syst_mixNP1-jes: 0.123901712 + syst_btag-eff: 1.405136836207867 + syst_pileoffrho-jes: 0.6014818754420922 + syst_modNP4-jes: 0.012928723773509588 + syst_mcstat: 0.09706738399999999 + syst_modNP3-jes: 0.030768791008600648 + syst_mod-NP1-jes: 0.8140959578713352 lumi: 0.9276063999999999 diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyttBar_norm.yaml b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyttBar_norm.yaml index 5dd88befda..34043646a9 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyttBar_norm.yaml +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/uncertainties_dSig_dyttBar_norm.yaml @@ -99,223 +99,223 @@ definitions: definition: artificial uncertainty 25 treatment: ADD type: ATLAS8TEVTTB151104716unc25 - sys,singletop-xsec: + syst_singletop-xsec: definition: '' treatment: MULT type: CORR - sys,wjet-scale: + syst_wjet-scale: definition: '' treatment: MULT type: CORR - sys,laltrealcr-mujet-fake: + syst_laltrealcr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,eta-jes: + syst_eta-jes: definition: '' treatment: MULT type: CORR - sys,statNP3-jes: + syst_statNP3-jes: definition: '' treatment: MULT type: CORR - sys,laltrealcr-ejet-fake: + syst_laltrealcr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,pileoffmu-jes: + syst_pileoffmu-jes: definition: '' treatment: MULT type: CORR - sys,lstat-ejet-fake: + syst_lstat-ejet-fake: definition: '' treatment: MULT type: CORR - sys,lstat-mujet-fake: + syst_lstat-mujet-fake: definition: '' treatment: MULT type: CORR - sys,etmsoft-scale: + syst_etmsoft-scale: definition: '' treatment: MULT type: CORR - sys,hardscat-model: + syst_hardscat-model: definition: '' treatment: MULT type: CORR - sys,statNP2-jes: + syst_statNP2-jes: definition: '' treatment: MULT type: CORR - sys,elen-scale: + syst_elen-scale: definition: '' treatment: MULT type: CORR - sys,punch-jes: + syst_punch-jes: definition: '' treatment: MULT type: CORR - sys,pileoffnpv-jes: + syst_pileoffnpv-jes: definition: '' treatment: MULT type: CORR - sys,lrec-eff: + syst_lrec-eff: definition: '' treatment: MULT type: CORR - sys,pileoffpt-jes: + syst_pileoffpt-jes: definition: '' treatment: MULT type: CORR - sys,jeten-res: + syst_jeten-res: definition: '' treatment: MULT type: CORR - sys,lighttag-eff: + syst_lighttag-eff: definition: '' treatment: MULT type: CORR - sys,laltfakecr-ejet-fake: + syst_laltfakecr-ejet-fake: definition: '' treatment: MULT type: CORR - sys,laltpar-mujet-fake: + syst_laltpar-mujet-fake: definition: '' treatment: MULT type: CORR - sys,jetrec-eff: + syst_jetrec-eff: definition: '' treatment: MULT type: CORR - sys,c/tautag-eff: + syst_c/tautag-eff: definition: '' treatment: MULT type: CORR - sys,dibos-xsec: + syst_dibos-xsec: definition: '' treatment: MULT type: CORR - sys,elen-res: + syst_elen-res: definition: '' treatment: MULT type: CORR - sys,flavcomp-jes: + syst_flavcomp-jes: definition: '' treatment: MULT type: CORR - sys,detNP2-jes: + syst_detNP2-jes: definition: '' treatment: MULT type: CORR - sys,detNP3-jes: + syst_detNP3-jes: definition: '' treatment: MULT type: CORR - sys,jetvxfrac: + syst_jetvxfrac: definition: '' treatment: MULT type: CORR - sys,ltrig-eff: + syst_ltrig-eff: definition: '' treatment: MULT type: CORR - sys,btag-jes: + syst_btag-jes: definition: '' treatment: MULT type: CORR - sys,mup-scale: + syst_mup-scale: definition: '' treatment: MULT type: CORR - sys,singlephpt-jes: + syst_singlephpt-jes: definition: '' treatment: MULT type: CORR - sys,etmsoft-res: + syst_etmsoft-res: definition: '' treatment: MULT type: CORR - sys,detNP1-jes: + syst_detNP1-jes: definition: '' treatment: MULT type: CORR - sys,laltpar-ejet-fake: + syst_laltpar-ejet-fake: definition: '' treatment: MULT type: CORR - sys,statNP1-jes: + syst_statNP1-jes: definition: '' treatment: MULT type: CORR - sys,muid-res: + syst_muid-res: definition: '' treatment: MULT type: CORR - sys,pdf: + syst_pdf: definition: '' treatment: MULT type: CORR - sys,isr-fsr: + syst_isr-fsr: definition: '' treatment: MULT type: CORR - sys,zjet-xsec: + syst_zjet-xsec: definition: '' treatment: MULT type: CORR - sys,ps-model: + syst_ps-model: definition: '' treatment: MULT type: CORR - sys,flavres-jes: + syst_flavres-jes: definition: '' treatment: MULT type: CORR - sys,laltfakecr-mujet-fake: + syst_laltfakecr-mujet-fake: definition: '' treatment: MULT type: CORR - sys,mums-res: + syst_mums-res: definition: '' treatment: MULT type: CORR - sys,mod-NP2-jes: + syst_mod-NP2-jes: definition: '' treatment: MULT type: CORR - sys,lid-eff: + syst_lid-eff: definition: '' treatment: MULT type: CORR - sys,mixNP2-jes: + syst_mixNP2-jes: definition: '' treatment: MULT type: CORR - sys,mixNP1-jes: + syst_mixNP1-jes: definition: '' treatment: MULT type: CORR - sys,btag-eff: + syst_btag-eff: definition: '' treatment: MULT type: CORR - sys,pileoffrho-jes: + syst_pileoffrho-jes: definition: '' treatment: MULT type: CORR - sys,modNP4-jes: + syst_modNP4-jes: definition: '' treatment: MULT type: CORR - sys,mcstat: + syst_mcstat: definition: '' treatment: MULT type: CORR - sys,modNP3-jes: + syst_modNP3-jes: definition: '' treatment: MULT type: CORR - sys,mod-NP1-jes: + syst_mod-NP1-jes: definition: '' treatment: MULT type: CORR @@ -345,61 +345,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 1.1460699348648485e-13 - sys,singletop-xsec: 0.000252192739164907 - sys,wjet-scale: 0.0001673862 - sys,laltrealcr-mujet-fake: 0.0031301219399999997 - sys,eta-jes: 0.00308505016065823 - sys,statNP3-jes: 0.00010275863221073971 - sys,laltrealcr-ejet-fake: 0.00026781792 - sys,pileoffmu-jes: 0.000523918137524334 - sys,lstat-ejet-fake: 0.000902052835577376 - sys,lstat-mujet-fake: 0.0006813152967818311 - sys,etmsoft-scale: 0.00020341900215885947 - sys,hardscat-model: 0.0047705067 - sys,statNP2-jes: 2.744061790791299e-05 - sys,elen-scale: 0.0007452446438369059 - sys,punch-jes: 3.8124021429134085e-05 - sys,pileoffnpv-jes: 0.00016670480665938422 - sys,lrec-eff: 0.0001673862 - sys,pileoffpt-jes: 4.348821043288284e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.00012985922371041292 - sys,laltfakecr-ejet-fake: 0.00055237446 - sys,laltpar-mujet-fake: 0.0012972430499999998 - sys,jetrec-eff: 2.510793e-05 - sys,c/tautag-eff: 0.00024696556117121695 - sys,dibos-xsec: 5.021586e-05 - sys,elen-res: 4.348821043288284e-05 - sys,flavcomp-jes: 0.0009080894189196659 - sys,detNP2-jes: 9.569968388819304e-05 - sys,detNP3-jes: 2.1744105216441422e-05 - sys,jetvxfrac: 0.00024400522001523866 - sys,ltrig-eff: 0.0007576306031947036 - sys,btag-jes: 7.24803507214714e-05 - sys,mup-scale: 8.327358307250806e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0001449607014429428 - sys,detNP1-jes: 0.00016796060449581404 - sys,laltpar-ejet-fake: 0.0005858517000000001 - sys,statNP1-jes: 0.00016670480665938422 - sys,muid-res: 8.36931e-05 - sys,pdf: 0.00423487086 - sys,isr-fsr: 0.008293782464204051 - sys,zjet-xsec: 5.021586e-05 - sys,ps-model: 0.00414280845 - sys,flavres-jes: 0.000505131367330483 - sys,laltfakecr-mujet-fake: 0.00026781792 - sys,mums-res: 0.00010880102999999999 - sys,mod-NP2-jes: 2.744061790791299e-05 - sys,lid-eff: 0.00024696556117121695 - sys,mixNP2-jes: 0.00010147249101005997 - sys,mixNP1-jes: 0.00014800906449859844 - sys,btag-eff: 4.64100690442287e-05 - sys,pileoffrho-jes: 0.0003211841979768405 - sys,modNP4-jes: 2.899214028858856e-05 - sys,mcstat: 0.0010880103 - sys,modNP3-jes: 0.00010944292955149318 - sys,mod-NP1-jes: 0.0004186328527361442 + syst_singletop-xsec: 0.000252192739164907 + syst_wjet-scale: 0.0001673862 + syst_laltrealcr-mujet-fake: 0.0031301219399999997 + syst_eta-jes: 0.00308505016065823 + syst_statNP3-jes: 0.00010275863221073971 + syst_laltrealcr-ejet-fake: 0.00026781792 + syst_pileoffmu-jes: 0.000523918137524334 + syst_lstat-ejet-fake: 0.000902052835577376 + syst_lstat-mujet-fake: 0.0006813152967818311 + syst_etmsoft-scale: 0.00020341900215885947 + syst_hardscat-model: 0.0047705067 + syst_statNP2-jes: 2.744061790791299e-05 + syst_elen-scale: 0.0007452446438369059 + syst_punch-jes: 3.8124021429134085e-05 + syst_pileoffnpv-jes: 0.00016670480665938422 + syst_lrec-eff: 0.0001673862 + syst_pileoffpt-jes: 4.348821043288284e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.00012985922371041292 + syst_laltfakecr-ejet-fake: 0.00055237446 + syst_laltpar-mujet-fake: 0.0012972430499999998 + syst_jetrec-eff: 2.510793e-05 + syst_c/tautag-eff: 0.00024696556117121695 + syst_dibos-xsec: 5.021586e-05 + syst_elen-res: 4.348821043288284e-05 + syst_flavcomp-jes: 0.0009080894189196659 + syst_detNP2-jes: 9.569968388819304e-05 + syst_detNP3-jes: 2.1744105216441422e-05 + syst_jetvxfrac: 0.00024400522001523866 + syst_ltrig-eff: 0.0007576306031947036 + syst_btag-jes: 7.24803507214714e-05 + syst_mup-scale: 8.327358307250806e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0001449607014429428 + syst_detNP1-jes: 0.00016796060449581404 + syst_laltpar-ejet-fake: 0.0005858517000000001 + syst_statNP1-jes: 0.00016670480665938422 + syst_muid-res: 8.36931e-05 + syst_pdf: 0.00423487086 + syst_isr-fsr: 0.008293782464204051 + syst_zjet-xsec: 5.021586e-05 + syst_ps-model: 0.00414280845 + syst_flavres-jes: 0.000505131367330483 + syst_laltfakecr-mujet-fake: 0.00026781792 + syst_mums-res: 0.00010880102999999999 + syst_mod-NP2-jes: 2.744061790791299e-05 + syst_lid-eff: 0.00024696556117121695 + syst_mixNP2-jes: 0.00010147249101005997 + syst_mixNP1-jes: 0.00014800906449859844 + syst_btag-eff: 4.64100690442287e-05 + syst_pileoffrho-jes: 0.0003211841979768405 + syst_modNP4-jes: 2.899214028858856e-05 + syst_mcstat: 0.0010880103 + syst_modNP3-jes: 0.00010944292955149318 + syst_mod-NP1-jes: 0.0004186328527361442 - ArtUnc_1: -0.0008173194033852813 ArtUnc_2: -0.003309945472255771 ArtUnc_3: -0.0008202592626190879 @@ -425,61 +425,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 1.0717999421711153e-13 - sys,singletop-xsec: 0.00012008646293530175 - sys,wjet-scale: 0.0002686586 - sys,laltrealcr-mujet-fake: 0.0021876485999999997 - sys,eta-jes: 0.0020862236923265796 - sys,statNP3-jes: 0.000132951527168664 - sys,laltrealcr-ejet-fake: 0.00028401051999999994 - sys,pileoffmu-jes: 0.0006390323140406354 - sys,lstat-ejet-fake: 0.0005654609394627031 - sys,lstat-mujet-fake: 0.0005916342959005547 - sys,etmsoft-scale: 0.0002768404969825065 - sys,hardscat-model: 0.0015044881599999998 - sys,statNP2-jes: 3.2566340208700146e-05 - sys,elen-scale: 0.0005409646084302621 - sys,punch-jes: 1.2729139612888217e-05 - sys,pileoffnpv-jes: 3.98854581505992e-05 - sys,lrec-eff: 0.00013816727999999999 - sys,pileoffpt-jes: 0.00010496717429434404 - sys,jeten-res: 0.0 - sys,lighttag-eff: 0.00012281536 - sys,laltfakecr-ejet-fake: 0.00043752972000000003 - sys,laltpar-mujet-fake: 0.000959495 - sys,jetrec-eff: 1.535192e-05 - sys,c/tautag-eff: 0.00018422304000000002 - sys,dibos-xsec: 5.373172e-05 - sys,elen-res: 0.0001066378443652571 - sys,flavcomp-jes: 0.0004258601577920676 - sys,detNP2-jes: 5.982818722589879e-05 - sys,detNP3-jes: 3.496566186808423e-05 - sys,jetvxfrac: 0.00014624667988553038 - sys,ltrig-eff: 0.0006064979962675464 - sys,btag-jes: 0.00020464028742106768 - sys,mup-scale: 7.471749655918351e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0003456739706385263 - sys,detNP1-jes: 0.0002659861360623166 - sys,laltpar-ejet-fake: 3.070384e-05 - sys,statNP1-jes: 4.65330345090324e-05 - sys,muid-res: 1.535192e-05 - sys,pdf: 0.003454182 - sys,isr-fsr: 0.007055517418901417 - sys,zjet-xsec: 0.00017654708 - sys,ps-model: 0.0007829479199999999 - sys,flavres-jes: 0.00046317279470684105 - sys,laltfakecr-mujet-fake: 0.00022260284 - sys,mums-res: 1.535192e-05 - sys,mod-NP2-jes: 5.091655845155287e-05 - sys,lid-eff: 0.00017654708 - sys,mixNP2-jes: 2.6590305433732798e-05 - sys,mixNP1-jes: 0.00022912451303198792 - sys,btag-eff: 7.67596e-06 - sys,pileoffrho-jes: 0.00040697068366359946 - sys,modNP4-jes: 8.078013420895758e-05 - sys,mcstat: 0.0009287911599999999 - sys,modNP3-jes: 8.641849265963158e-05 - sys,mod-NP1-jes: 0.0005159478608946729 + syst_singletop-xsec: 0.00012008646293530175 + syst_wjet-scale: 0.0002686586 + syst_laltrealcr-mujet-fake: 0.0021876485999999997 + syst_eta-jes: 0.0020862236923265796 + syst_statNP3-jes: 0.000132951527168664 + syst_laltrealcr-ejet-fake: 0.00028401051999999994 + syst_pileoffmu-jes: 0.0006390323140406354 + syst_lstat-ejet-fake: 0.0005654609394627031 + syst_lstat-mujet-fake: 0.0005916342959005547 + syst_etmsoft-scale: 0.0002768404969825065 + syst_hardscat-model: 0.0015044881599999998 + syst_statNP2-jes: 3.2566340208700146e-05 + syst_elen-scale: 0.0005409646084302621 + syst_punch-jes: 1.2729139612888217e-05 + syst_pileoffnpv-jes: 3.98854581505992e-05 + syst_lrec-eff: 0.00013816727999999999 + syst_pileoffpt-jes: 0.00010496717429434404 + syst_jeten-res: 0.0 + syst_lighttag-eff: 0.00012281536 + syst_laltfakecr-ejet-fake: 0.00043752972000000003 + syst_laltpar-mujet-fake: 0.000959495 + syst_jetrec-eff: 1.535192e-05 + syst_c/tautag-eff: 0.00018422304000000002 + syst_dibos-xsec: 5.373172e-05 + syst_elen-res: 0.0001066378443652571 + syst_flavcomp-jes: 0.0004258601577920676 + syst_detNP2-jes: 5.982818722589879e-05 + syst_detNP3-jes: 3.496566186808423e-05 + syst_jetvxfrac: 0.00014624667988553038 + syst_ltrig-eff: 0.0006064979962675464 + syst_btag-jes: 0.00020464028742106768 + syst_mup-scale: 7.471749655918351e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0003456739706385263 + syst_detNP1-jes: 0.0002659861360623166 + syst_laltpar-ejet-fake: 3.070384e-05 + syst_statNP1-jes: 4.65330345090324e-05 + syst_muid-res: 1.535192e-05 + syst_pdf: 0.003454182 + syst_isr-fsr: 0.007055517418901417 + syst_zjet-xsec: 0.00017654708 + syst_ps-model: 0.0007829479199999999 + syst_flavres-jes: 0.00046317279470684105 + syst_laltfakecr-mujet-fake: 0.00022260284 + syst_mums-res: 1.535192e-05 + syst_mod-NP2-jes: 5.091655845155287e-05 + syst_lid-eff: 0.00017654708 + syst_mixNP2-jes: 2.6590305433732798e-05 + syst_mixNP1-jes: 0.00022912451303198792 + syst_btag-eff: 7.67596e-06 + syst_pileoffrho-jes: 0.00040697068366359946 + syst_modNP4-jes: 8.078013420895758e-05 + syst_mcstat: 0.0009287911599999999 + syst_modNP3-jes: 8.641849265963158e-05 + syst_mod-NP1-jes: 0.0005159478608946729 - ArtUnc_1: -0.002611086587337312 ArtUnc_2: -0.00025714944685773064 ArtUnc_3: 0.0013733410798208627 @@ -505,61 +505,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 1.0408067927972082e-13 - sys,singletop-xsec: 2.276801645874317e-05 - sys,wjet-scale: 5.101056e-05 - sys,laltrealcr-mujet-fake: 0.0013135219199999998 - sys,eta-jes: 0.00047685112057754207 - sys,statNP3-jes: 0.00013541238123163774 - sys,laltrealcr-ejet-fake: 7.651584e-05 - sys,pileoffmu-jes: 0.00024898284512788106 - sys,lstat-ejet-fake: 0.00019327192859305774 - sys,lstat-mujet-fake: 0.0004307202980073858 - sys,etmsoft-scale: 0.00018774987349039893 - sys,hardscat-model: 0.0029203545600000004 - sys,statNP2-jes: 4.9698495923929124e-05 - sys,elen-scale: 0.00011596315715583464 - sys,punch-jes: 2.2088220410635167e-05 - sys,pileoffnpv-jes: 0.0001415413784368995 - sys,lrec-eff: 8.926847999999999e-05 - sys,pileoffpt-jes: 0.00011975774657984176 - sys,jeten-res: 0.0 - sys,lighttag-eff: 6.376319999999999e-05 - sys,laltfakecr-ejet-fake: 3.825792e-05 - sys,laltpar-mujet-fake: 0.00068864256 - sys,jetrec-eff: 1.275264e-05 - sys,c/tautag-eff: 2.550528e-05 - sys,dibos-xsec: 0.00010839744 - sys,elen-res: 5.522055102658792e-06 - sys,flavcomp-jes: 0.000204837729332652 - sys,detNP2-jes: 0.00015152986073797603 - sys,detNP3-jes: 3.535840486827425e-05 - sys,jetvxfrac: 3.865438571861154e-05 - sys,ltrig-eff: 0.00027740656309679766 - sys,btag-jes: 8.289215999999999e-05 - sys,mup-scale: 2.2088220410635167e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.0002705807000302808 - sys,detNP1-jes: 0.00020130901621136398 - sys,laltpar-ejet-fake: 9.564479999999998e-05 - sys,statNP1-jes: 3.865438571861154e-05 - sys,muid-res: 6.37632e-06 - sys,pdf: 0.00216157248 - sys,isr-fsr: 0.0030792872696603513 - sys,zjet-xsec: 0.00017853695999999999 - sys,ps-model: 0.0036026207999999994 - sys,flavres-jes: 0.00023840957305837197 - sys,laltfakecr-mujet-fake: 0.00012115008000000001 - sys,mums-res: 0.00020404224 - sys,mod-NP2-jes: 5.870025092721835e-05 - sys,lid-eff: 6.37632e-06 - sys,mixNP2-jes: 0.00018793926025437047 - sys,mixNP1-jes: 0.0002627477146400996 - sys,btag-eff: 1.912896e-05 - sys,pileoffrho-jes: 0.00017796673510049455 - sys,modNP4-jes: 5.800348735579267e-05 - sys,mcstat: 0.0009245664 - sys,modNP3-jes: 8.115730399827731e-05 - sys,mod-NP1-jes: 0.0003235471940034319 + syst_singletop-xsec: 2.276801645874317e-05 + syst_wjet-scale: 5.101056e-05 + syst_laltrealcr-mujet-fake: 0.0013135219199999998 + syst_eta-jes: 0.00047685112057754207 + syst_statNP3-jes: 0.00013541238123163774 + syst_laltrealcr-ejet-fake: 7.651584e-05 + syst_pileoffmu-jes: 0.00024898284512788106 + syst_lstat-ejet-fake: 0.00019327192859305774 + syst_lstat-mujet-fake: 0.0004307202980073858 + syst_etmsoft-scale: 0.00018774987349039893 + syst_hardscat-model: 0.0029203545600000004 + syst_statNP2-jes: 4.9698495923929124e-05 + syst_elen-scale: 0.00011596315715583464 + syst_punch-jes: 2.2088220410635167e-05 + syst_pileoffnpv-jes: 0.0001415413784368995 + syst_lrec-eff: 8.926847999999999e-05 + syst_pileoffpt-jes: 0.00011975774657984176 + syst_jeten-res: 0.0 + syst_lighttag-eff: 6.376319999999999e-05 + syst_laltfakecr-ejet-fake: 3.825792e-05 + syst_laltpar-mujet-fake: 0.00068864256 + syst_jetrec-eff: 1.275264e-05 + syst_c/tautag-eff: 2.550528e-05 + syst_dibos-xsec: 0.00010839744 + syst_elen-res: 5.522055102658792e-06 + syst_flavcomp-jes: 0.000204837729332652 + syst_detNP2-jes: 0.00015152986073797603 + syst_detNP3-jes: 3.535840486827425e-05 + syst_jetvxfrac: 3.865438571861154e-05 + syst_ltrig-eff: 0.00027740656309679766 + syst_btag-jes: 8.289215999999999e-05 + syst_mup-scale: 2.2088220410635167e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.0002705807000302808 + syst_detNP1-jes: 0.00020130901621136398 + syst_laltpar-ejet-fake: 9.564479999999998e-05 + syst_statNP1-jes: 3.865438571861154e-05 + syst_muid-res: 6.37632e-06 + syst_pdf: 0.00216157248 + syst_isr-fsr: 0.0030792872696603513 + syst_zjet-xsec: 0.00017853695999999999 + syst_ps-model: 0.0036026207999999994 + syst_flavres-jes: 0.00023840957305837197 + syst_laltfakecr-mujet-fake: 0.00012115008000000001 + syst_mums-res: 0.00020404224 + syst_mod-NP2-jes: 5.870025092721835e-05 + syst_lid-eff: 6.37632e-06 + syst_mixNP2-jes: 0.00018793926025437047 + syst_mixNP1-jes: 0.0002627477146400996 + syst_btag-eff: 1.912896e-05 + syst_pileoffrho-jes: 0.00017796673510049455 + syst_modNP4-jes: 5.800348735579267e-05 + syst_mcstat: 0.0009245664 + syst_modNP3-jes: 8.115730399827731e-05 + syst_mod-NP1-jes: 0.0003235471940034319 - ArtUnc_1: -0.0008721521638405283 ArtUnc_2: 0.0019857021980425565 ArtUnc_3: 0.00033708349493200425 @@ -585,61 +585,61 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 1.2378988365098798e-13 - sys,singletop-xsec: 4.038445665662347e-05 - sys,wjet-scale: 0.00016401249 - sys,laltrealcr-mujet-fake: 0.0007978985999999999 - sys,eta-jes: 0.0010953427590436708 - sys,statNP3-jes: 4.815220747797757e-05 - sys,laltrealcr-ejet-fake: 0.0001773108 - sys,pileoffmu-jes: 0.00024894651011638343 - sys,lstat-ejet-fake: 0.0005621409546154574 - sys,lstat-mujet-fake: 0.00018185165420223895 - sys,etmsoft-scale: 7.636081619379847e-05 - sys,hardscat-model: 0.0018750617099999997 - sys,statNP2-jes: 7.350917435971852e-06 - sys,elen-scale: 0.0001403866027173487 - sys,punch-jes: 1.1516674287400637e-05 - sys,pileoffnpv-jes: 0.00022533126385740083 - sys,lrec-eff: 8.86554e-06 - sys,pileoffpt-jes: 7.23981405396304e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 7.092432e-05 - sys,laltfakecr-ejet-fake: 0.00021720573 - sys,laltpar-mujet-fake: 7.092432e-05 - sys,jetrec-eff: 1.3298309999999998e-05 - sys,c/tautag-eff: 9.535608490644147e-05 - sys,dibos-xsec: 2.6596619999999997e-05 - sys,elen-res: 0.0001088513586197191 - sys,flavcomp-jes: 0.00026510595963292685 - sys,detNP2-jes: 3.655355784813839e-05 - sys,detNP3-jes: 1.9194457145667732e-05 - sys,jetvxfrac: 4.9905588578736094e-05 - sys,ltrig-eff: 0.00016625842417364024 - sys,btag-jes: 0.00010966067354441518 - sys,mup-scale: 1.8276778924069195e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 0.00010748896001573928 - sys,detNP1-jes: 0.0001255342933148779 - sys,laltpar-ejet-fake: 3.546216e-05 - sys,statNP1-jes: 7.839238131189457e-05 - sys,muid-res: 5.7626009999999994e-05 - sys,pdf: 0.0012633394499999998 - sys,isr-fsr: 0.002620723203769138 - sys,zjet-xsec: 5.7626009999999994e-05 - sys,ps-model: 0.00142735194 - sys,flavres-jes: 0.00024867010023325344 - sys,laltfakecr-mujet-fake: 0.0003102939 - sys,mums-res: 5.7626009999999994e-05 - sys,mod-NP2-jes: 3.7613300717283506e-05 - sys,lid-eff: 0.00013966743128346522 - sys,mixNP2-jes: 0.00010918930217920652 - sys,mixNP1-jes: 0.00018420012912055754 - sys,btag-eff: 8.86554e-06 - sys,pileoffrho-jes: 0.0002017153894087104 - sys,modNP4-jes: 2.5464324962694373e-05 - sys,mcstat: 0.00071367597 - sys,modNP3-jes: 3.1656309695191885e-05 - sys,mod-NP1-jes: 0.0002961259570187734 + syst_singletop-xsec: 4.038445665662347e-05 + syst_wjet-scale: 0.00016401249 + syst_laltrealcr-mujet-fake: 0.0007978985999999999 + syst_eta-jes: 0.0010953427590436708 + syst_statNP3-jes: 4.815220747797757e-05 + syst_laltrealcr-ejet-fake: 0.0001773108 + syst_pileoffmu-jes: 0.00024894651011638343 + syst_lstat-ejet-fake: 0.0005621409546154574 + syst_lstat-mujet-fake: 0.00018185165420223895 + syst_etmsoft-scale: 7.636081619379847e-05 + syst_hardscat-model: 0.0018750617099999997 + syst_statNP2-jes: 7.350917435971852e-06 + syst_elen-scale: 0.0001403866027173487 + syst_punch-jes: 1.1516674287400637e-05 + syst_pileoffnpv-jes: 0.00022533126385740083 + syst_lrec-eff: 8.86554e-06 + syst_pileoffpt-jes: 7.23981405396304e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 7.092432e-05 + syst_laltfakecr-ejet-fake: 0.00021720573 + syst_laltpar-mujet-fake: 7.092432e-05 + syst_jetrec-eff: 1.3298309999999998e-05 + syst_c/tautag-eff: 9.535608490644147e-05 + syst_dibos-xsec: 2.6596619999999997e-05 + syst_elen-res: 0.0001088513586197191 + syst_flavcomp-jes: 0.00026510595963292685 + syst_detNP2-jes: 3.655355784813839e-05 + syst_detNP3-jes: 1.9194457145667732e-05 + syst_jetvxfrac: 4.9905588578736094e-05 + syst_ltrig-eff: 0.00016625842417364024 + syst_btag-jes: 0.00010966067354441518 + syst_mup-scale: 1.8276778924069195e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 0.00010748896001573928 + syst_detNP1-jes: 0.0001255342933148779 + syst_laltpar-ejet-fake: 3.546216e-05 + syst_statNP1-jes: 7.839238131189457e-05 + syst_muid-res: 5.7626009999999994e-05 + syst_pdf: 0.0012633394499999998 + syst_isr-fsr: 0.002620723203769138 + syst_zjet-xsec: 5.7626009999999994e-05 + syst_ps-model: 0.00142735194 + syst_flavres-jes: 0.00024867010023325344 + syst_laltfakecr-mujet-fake: 0.0003102939 + syst_mums-res: 5.7626009999999994e-05 + syst_mod-NP2-jes: 3.7613300717283506e-05 + syst_lid-eff: 0.00013966743128346522 + syst_mixNP2-jes: 0.00010918930217920652 + syst_mixNP1-jes: 0.00018420012912055754 + syst_btag-eff: 8.86554e-06 + syst_pileoffrho-jes: 0.0002017153894087104 + syst_modNP4-jes: 2.5464324962694373e-05 + syst_mcstat: 0.00071367597 + syst_modNP3-jes: 3.1656309695191885e-05 + syst_mod-NP1-jes: 0.0002961259570187734 - ArtUnc_1: 1.5013172367939154e-05 ArtUnc_2: 0.00024219763161619434 ArtUnc_3: -9.628002367410729e-05 @@ -665,58 +665,58 @@ bins: ArtUnc_23: 0.0 ArtUnc_24: 0.0 ArtUnc_25: 2.176596120038148e-13 - sys,singletop-xsec: 7.109092829753948e-05 - sys,wjet-scale: 3.250884e-05 - sys,laltrealcr-mujet-fake: 0.00117281892 - sys,eta-jes: 0.0008600455760057767 - sys,statNP3-jes: 5.1260127846062784e-05 - sys,laltrealcr-ejet-fake: 8.002176e-05 - sys,pileoffmu-jes: 0.0002112177346042427 - sys,lstat-ejet-fake: 0.0001880535884461094 - sys,lstat-mujet-fake: 0.0004223022193134557 - sys,etmsoft-scale: 9.86026083271452e-05 - sys,hardscat-model: 0.0025506936000000003 - sys,statNP2-jes: 9.188085014974557e-06 - sys,elen-scale: 0.0002413253374727387 - sys,punch-jes: 1.0609486713465456e-05 - sys,pileoffnpv-jes: 8.134900761124748e-05 - sys,lrec-eff: 8.190204215103981e-05 - sys,pileoffpt-jes: 3.248478610103505e-05 - sys,jeten-res: 0.0 - sys,lighttag-eff: 4.6262580000000005e-05 - sys,laltfakecr-ejet-fake: 0.00013628706000000002 - sys,laltpar-mujet-fake: 0.0006514271400000001 - sys,jetrec-eff: 0.0 - sys,c/tautag-eff: 5.814753186949468e-05 - sys,dibos-xsec: 7.502040000000001e-06 - sys,elen-res: 2.080041212313593e-05 - sys,flavcomp-jes: 0.0002625914939811271 - sys,detNP2-jes: 2.0734543201864853e-06 - sys,detNP3-jes: 1.7448851968341642e-05 - sys,jetvxfrac: 3.1705427363951426e-05 - sys,ltrig-eff: 0.0002995203758447447 - sys,btag-jes: 3.9829756838387806e-05 - sys,mup-scale: 2.7902479416152256e-05 - sys,singlephpt-jes: 0.0 - sys,etmsoft-res: 6.49695722020701e-05 - sys,detNP1-jes: 9.491060723695324e-05 - sys,laltpar-ejet-fake: 0.00012128298000000001 - sys,statNP1-jes: 1.2298541526713647e-05 - sys,muid-res: 3.7510200000000003e-06 - sys,pdf: 0.0025006800000000004 - sys,isr-fsr: 0.003045396736912885 - sys,zjet-xsec: 6.87687e-05 - sys,ps-model: 0.0005126394 - sys,flavres-jes: 0.00017299031161546359 - sys,laltfakecr-mujet-fake: 0.00011378094 - sys,mums-res: 8.377278e-05 - sys,mod-NP2-jes: 1.125306e-05 - sys,lid-eff: 4.8146208406331434e-05 - sys,mixNP2-jes: 1.2674155765884369e-05 - sys,mixNP1-jes: 7.803113393212418e-05 - sys,btag-eff: 1.3158306323486316e-05 - sys,pileoffrho-jes: 0.0001271095299621437 - sys,modNP4-jes: 1.625442e-05 - sys,mcstat: 0.00029132922000000004 - sys,modNP3-jes: 4.639755499141308e-05 - sys,mod-NP1-jes: 0.00018012166474779735 + syst_singletop-xsec: 7.109092829753948e-05 + syst_wjet-scale: 3.250884e-05 + syst_laltrealcr-mujet-fake: 0.00117281892 + syst_eta-jes: 0.0008600455760057767 + syst_statNP3-jes: 5.1260127846062784e-05 + syst_laltrealcr-ejet-fake: 8.002176e-05 + syst_pileoffmu-jes: 0.0002112177346042427 + syst_lstat-ejet-fake: 0.0001880535884461094 + syst_lstat-mujet-fake: 0.0004223022193134557 + syst_etmsoft-scale: 9.86026083271452e-05 + syst_hardscat-model: 0.0025506936000000003 + syst_statNP2-jes: 9.188085014974557e-06 + syst_elen-scale: 0.0002413253374727387 + syst_punch-jes: 1.0609486713465456e-05 + syst_pileoffnpv-jes: 8.134900761124748e-05 + syst_lrec-eff: 8.190204215103981e-05 + syst_pileoffpt-jes: 3.248478610103505e-05 + syst_jeten-res: 0.0 + syst_lighttag-eff: 4.6262580000000005e-05 + syst_laltfakecr-ejet-fake: 0.00013628706000000002 + syst_laltpar-mujet-fake: 0.0006514271400000001 + syst_jetrec-eff: 0.0 + syst_c/tautag-eff: 5.814753186949468e-05 + syst_dibos-xsec: 7.502040000000001e-06 + syst_elen-res: 2.080041212313593e-05 + syst_flavcomp-jes: 0.0002625914939811271 + syst_detNP2-jes: 2.0734543201864853e-06 + syst_detNP3-jes: 1.7448851968341642e-05 + syst_jetvxfrac: 3.1705427363951426e-05 + syst_ltrig-eff: 0.0002995203758447447 + syst_btag-jes: 3.9829756838387806e-05 + syst_mup-scale: 2.7902479416152256e-05 + syst_singlephpt-jes: 0.0 + syst_etmsoft-res: 6.49695722020701e-05 + syst_detNP1-jes: 9.491060723695324e-05 + syst_laltpar-ejet-fake: 0.00012128298000000001 + syst_statNP1-jes: 1.2298541526713647e-05 + syst_muid-res: 3.7510200000000003e-06 + syst_pdf: 0.0025006800000000004 + syst_isr-fsr: 0.003045396736912885 + syst_zjet-xsec: 6.87687e-05 + syst_ps-model: 0.0005126394 + syst_flavres-jes: 0.00017299031161546359 + syst_laltfakecr-mujet-fake: 0.00011378094 + syst_mums-res: 8.377278e-05 + syst_mod-NP2-jes: 1.125306e-05 + syst_lid-eff: 4.8146208406331434e-05 + syst_mixNP2-jes: 1.2674155765884369e-05 + syst_mixNP1-jes: 7.803113393212418e-05 + syst_btag-eff: 1.3158306323486316e-05 + syst_pileoffrho-jes: 0.0001271095299621437 + syst_modNP4-jes: 1.625442e-05 + syst_mcstat: 0.00029132922000000004 + syst_modNP3-jes: 4.639755499141308e-05 + syst_mod-NP1-jes: 0.00018012166474779735 From 78dcc453a66dc8399132323b35dc897444e9cf67 Mon Sep 17 00:00:00 2001 From: t7phy Date: Mon, 25 Mar 2024 22:35:03 +0100 Subject: [PATCH 3/5] utils now depend on ATLAS_TTBAR_13TEV_HADR_DIF --- .../new_commondata/ATLAS_1JET_13TEV_DIF/filter.py | 2 +- .../new_commondata/ATLAS_2JET_13TEV_DIF/filter.py | 2 +- .../new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py | 6 +++--- .../new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py | 2 +- .../new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py | 10 +++++----- .../new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py | 4 ++-- .../new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py | 2 +- .../new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py | 2 +- .../new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py | 10 +++++----- .../new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py | 4 ++-- .../H1_1JET_319GEV_290PB-1_DIF/artUnc.py | 4 ++-- .../H1_1JET_319GEV_290PB-1_DIF/filter.py | 4 ++-- .../H1_1JET_319GEV_351PB-1_DIF/filter.py | 2 +- .../H1_1JET_319GEV_351PB-1_DIF/manual_impl.py | 4 ++-- .../H1_2JET_319GEV_290PB-1_DIF/artUnc.py | 4 ++-- .../H1_2JET_319GEV_290PB-1_DIF/filter.py | 4 ++-- .../H1_2JET_319GEV_351PB-1_DIF/filter.py | 2 +- .../H1_2JET_319GEV_351PB-1_DIF/manual_impl.py | 4 ++-- .../ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py | 2 +- .../ZEUS_1JET_319GEV_82PB-1_DIF/filter.py | 2 +- .../ZEUS_2JET_319GEV_374PB-1_DIF/filter.py | 2 +- 21 files changed, 39 insertions(+), 39 deletions(-) diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py index 14d0a7e653..b1fb4dcd4b 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py @@ -1,5 +1,5 @@ import yaml -from validphys.commondata_utils import symmetrize_errors as se +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se def processData(): with open('metadata.yaml', 'r') as file: diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py index 53b4ac9af3..52df10bb73 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py @@ -1,5 +1,5 @@ import yaml -from validphys.commondata_utils import symmetrize_errors as se +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se def processData(): with open('metadata.yaml', 'r') as file: diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py index 93bb0c7d8c..71ded50a8a 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py @@ -1,7 +1,7 @@ import yaml -from validphys.commondata_utils import symmetrize_errors as se -from validphys.commondata_utils import percentage_to_absolute as pta -from validphys.commondata_utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta def processData(): with open('metadata.yaml', 'r') as file: diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py index 45b1e78238..a10f4cf044 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py @@ -1,5 +1,5 @@ import yaml -from validphys.commondata_utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta def processData(): with open('metadata.yaml', 'r') as file: diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py index c174a979ea..9beff3dd11 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py @@ -3,11 +3,11 @@ import numpy as np from numpy.linalg import eig # use #1693 -from validphys.commondata_utils import cormat_to_covmat as ctc -from validphys.commondata_utils import covmat_to_artunc as cta -from validphys.commondata_utils import percentage_to_absolute as pta -from validphys.commondata_utils import concat_matrices as cm -from validphys.commondata_utils import matlist_to_matrix as mtm +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import concat_matrices as cm +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import matlist_to_matrix as mtm def artunc(): statArr = [] diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py index e0f9b2b344..b3c39246d5 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py @@ -4,8 +4,8 @@ from pathlib import Path # use #1693 -from validphys.commondata_utils import percentage_to_absolute as pta -from validphys.commondata_utils import symmetrize_errors as se +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se def processData(): with open('metadata.yaml', 'r') as file: diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py index 8ede78ce29..a46f21ad9a 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py @@ -1,6 +1,6 @@ import yaml -from validphys.commondata_utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta def processData(): with open('metadata.yaml', 'r') as file: diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py index 8b2218ec9d..75eebeeddb 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py @@ -1,5 +1,5 @@ import yaml -from validphys.commondata_utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta def processData(): with open('metadata.yaml', 'r') as file: diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py index 735b919ac3..4eb8975a11 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py @@ -1,9 +1,9 @@ import yaml -from validphys.commondata_utils import percentage_to_absolute as pta -from validphys.commondata_utils import symmetrize_errors as se -from validphys.commondata_utils import cormat_to_covmat as ctc -from validphys.commondata_utils import covmat_to_artunc as cta -from validphys.commondata_utils import trimat_to_fullmat as ttf +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import trimat_to_fullmat as ttf def processData(): with open('metadata.yaml', 'r') as file: diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py index 587e3e645b..850103d456 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py @@ -1,6 +1,6 @@ import yaml -from validphys.commondata_utils import covmat_to_artunc as cta -from validphys.commondata_utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta def processData(): with open('metadata.yaml', 'r') as file: diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py index 62c14fd524..79e6f4eaba 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py @@ -1,8 +1,8 @@ import yaml import numpy # use #1693 -from validphys.commondata_utils import covmat_to_artunc as cta -from validphys.commondata_utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta def artunc(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py index e672cc68cf..f08b857e32 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py @@ -1,8 +1,8 @@ import artUnc import yaml # use #1693 -from validphys.commondata_utils import percentage_to_absolute as pta -from validphys.commondata_utils import symmetrize_errors as se +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se from math import sqrt def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py index 25ac3c201f..ae18d9d818 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py @@ -1,6 +1,6 @@ import yaml # use #1693 -from validphys.commondata_utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta from manual_impl import jet_data, jet_sys, artunc diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py index ca27217dfb..d1554b3822 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py @@ -1,7 +1,7 @@ from math import sqrt # use #1693 -from validphys.commondata_utils import cormat_to_covmat as ctc -from validphys.commondata_utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta jet_old_impl_list = [['1', 'DIS_1JET', '1.750000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '7.042365878823e+01', '1.901438787282e+00', '7.042365878823e-01', '1.000000000000e+00', '7.130250486484e-01', '1.010961455291e+00', '6.718827732504e-01', '9.531044964111e-01', '2.517144109385e-01', '3.572500464504e-01', '2.515885537330e-01', '3.572500464504e-01', '3.521182939412e-01', '5.000000000000e-01', '4.225419527294e-01', '6.000000000000e-01', '2.042286104859e+00', '2.900000000000e+00'], ['2', 'DIS_1JET', '1.750000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '3.095350776162e+01', '1.269093818226e+00', '8.666982173254e-01', '2.800000000000e+00', '7.598162606841e-01', '2.452246318915e+00', '1.718173641914e-01', '5.542497004702e-01', '1.910967863178e-01', '6.170584587557e-01', '8.045980207445e-02', '2.599375899303e-01', '1.547675388081e-01', '5.000000000000e-01', '1.857210465697e-01', '6.000000000000e-01', '8.976517250870e-01', '2.900000000000e+00'], ['3', 'DIS_1JET', '1.750000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '8.082109035000e+00', '5.172549782400e-01', '2.828738162250e-01', '3.500000000000e+00', '2.743800000000e-01', '3.400000000000e+00', '1.976738222426e-02', '2.447042700083e-01', '3.679736009134e-02', '4.552940319412e-01', '8.082109035000e-03', '1.000000000000e-01', '4.041054517500e-02', '5.000000000000e-01', '4.849265421000e-02', '6.000000000000e-01', '2.343811620150e-01', '2.900000000000e+00'], ['4', 'DIS_1JET', '1.750000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '9.125014074304e-01', '1.396127153368e-01', '1.067626646694e-01', '1.170000000000e+01', '4.688994472166e-02', '5.118073262173e+00', '1.519286117216e-03', '1.657483653351e-01', '4.299338659215e-03', '4.704529347867e-01', '1.991738317891e-03', '2.182723557106e-01', '4.562507037152e-03', '5.000000000000e-01', '5.475008444582e-03', '6.000000000000e-01', '2.646254081548e-02', '2.900000000000e+00'], ['5', 'DIS_1JET', '2.350000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '5.493706846573e+01', '1.648112053972e+00', '-3.296224107944e-01', '-6.000000000000e-01', '5.220401134013e-01', '9.531044964111e-01', '6.074575198510e-01', '1.107945704936e+00', '4.273370774492e-01', '7.782554801857e-01', '1.960665379920e-01', '3.568929749397e-01', '2.746853423286e-01', '5.000000000000e-01', '3.296224107944e-01', '6.000000000000e-01', '1.593174985506e+00', '2.900000000000e+00'], ['6', 'DIS_1JET', '2.350000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '2.680000000000e+01', '1.098800000000e+00', '9.112000000000e-01', '3.400000000000e+00', '6.432000000000e-01', '2.400000000000e+00', '1.072000000000e-01', '4.000000000000e-01', '1.608000000000e-01', '6.000000000000e-01', '8.040000000000e-02', '3.000000000000e-01', '1.340000000000e-01', '5.000000000000e-01', '1.608000000000e-01', '6.000000000000e-01', '7.772000000000e-01', '2.900000000000e+00'], ['7', 'DIS_1JET', '2.350000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '7.013496240129e+00', '4.628907518485e-01', '3.366478195262e-01', '4.800000000000e+00', '2.492988998672e-01', '3.551005871609e+00', '1.404103000000e-02', '2.000000000000e-01', '3.893063895065e-02', '5.548042274342e-01', '2.505571857565e-02', '3.572500464504e-01', '3.506748120064e-02', '5.000000000000e-01', '4.208097744077e-02', '6.000000000000e-01', '2.033913909637e-01', '2.900000000000e+00'], ['8', 'DIS_1JET', '2.350000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '8.549775241245e-01', '1.299565836669e-01', '3.932896610973e-02', '4.600000000000e+00', '4.505743015308e-02', '5.264739441654e+00', '1.865246959223e-03', '2.182723557106e-01', '7.400622244443e-04', '8.655926074807e-02', '2.564932572373e-03', '3.000000000000e-01', '4.274887620623e-03', '5.000000000000e-01', '5.129865144747e-03', '6.000000000000e-01', '2.479434819961e-02', '2.900000000000e+00'], ['9', 'DIS_1JET', '3.450000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '5.209563915000e+01', '1.562869174500e+00', '7.814345872500e-01', '1.500000000000e+00', '4.972717868530e-01', '9.531044964111e-01', '5.217390000000e-01', '1.000000000000e+00', '4.570802892413e-01', '8.773868536773e-01', '1.562869174500e-01', '3.000000000000e-01', '2.604781957500e-01', '5.000000000000e-01', '3.125738349000e-01', '6.000000000000e-01', '1.510773535350e+00', '2.900000000000e+00'], ['10', 'DIS_1JET', '3.450000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '2.785563475695e+01', '1.114225390278e+00', '8.635246774655e-01', '3.100000000000e+00', '6.258088126577e-01', '2.249985843976e+00', '1.112556000000e-01', '4.000000000000e-01', '2.259611917827e-01', '8.115922482154e-01', '7.233489456689e-02', '2.596777822442e-01', '1.114225390278e-01', '4.000000000000e-01', '1.671338085417e-01', '6.000000000000e-01', '8.078134079515e-01', '2.900000000000e+00'], ['11', 'DIS_1JET', '3.450000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '6.962069709237e+00', '4.734207402281e-01', '1.322793244755e-01', '1.900000000000e+00', '2.518340918144e-01', '3.606383090020e+00', '1.158001203865e-02', '1.657483653351e-01', '5.928243109147e-02', '8.502285946131e-01', '1.811516043400e-02', '2.601979180124e-01', '2.784827883695e-02', '4.000000000000e-01', '4.177241825542e-02', '6.000000000000e-01', '2.019000215679e-01', '2.900000000000e+00'], ['12', 'DIS_1JET', '3.450000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '8.702993693220e-01', '1.314152047676e-01', '-2.610898107966e-02', '-3.000000000000e+00', '4.826471687216e-02', '5.562396168725e+00', '1.502894423550e-03', '1.733784592161e-01', '5.667628733710e-03', '6.522043307043e-01', '2.849193515760e-03', '3.273808549327e-01', '3.481197477288e-03', '4.000000000000e-01', '5.221796215932e-03', '6.000000000000e-01', '2.523868171034e-02', '2.900000000000e+00'], ['13', 'DIS_1JET', '5.500000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '4.877557560000e+01', '1.560818419200e+00', '7.316336340000e-01', '1.500000000000e+00', '6.381428053344e-01', '1.308978661724e+00', '3.412584000000e-01', '7.000000000000e-01', '5.616976088752e-01', '1.151596064148e+00', '9.755115120000e-02', '2.000000000000e-01', '1.951023024000e-01', '4.000000000000e-01', '2.926534536000e-01', '6.000000000000e-01', '1.414491692400e+00', '2.900000000000e+00'], ['14', 'DIS_1JET', '5.500000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '2.690000000000e+01', '1.102900000000e+00', '3.228000000000e-01', '1.200000000000e+00', '5.380000000000e-01', '2.000000000000e+00', '1.076000000000e-01', '4.000000000000e-01', '1.883000000000e-01', '7.000000000000e-01', '2.690000000000e-02', '1.000000000000e-01', '1.076000000000e-01', '4.000000000000e-01', '1.614000000000e-01', '6.000000000000e-01', '7.801000000000e-01', '2.900000000000e+00'], ['15', 'DIS_1JET', '5.500000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '7.949992050000e+00', '4.849495150500e-01', '2.782497217500e-01', '3.500000000000e+00', '2.943647864470e-01', '3.699002713601e+00', '2.639353425041e-02', '3.319944735090e-01', '6.359993640000e-02', '8.000000000000e-01', '7.949992050000e-03', '1.000000000000e-01', '2.384997615000e-02', '3.000000000000e-01', '4.769995230000e-02', '6.000000000000e-01', '2.305497694500e-01', '2.900000000000e+00'], ['16', 'DIS_1JET', '5.500000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '8.561421438570e-01', '1.412634537364e-01', '-7.619665080327e-02', '-8.900000000000e+00', '4.800730113222e-02', '5.596189240424e+00', '1.213193003977e-03', '1.415629191565e-01', '1.211979810973e-03', '1.415629191565e-01', '8.561421438570e-04', '1.000000000000e-01', '1.712284287714e-03', '2.000000000000e-01', '5.136852863142e-03', '6.000000000000e-01', '2.482812217185e-02', '2.900000000000e+00'], ['17', 'DIS_1JET', '2.850000000000e+03', '9.000000000000e+00', '3.190000000000e+02', '4.329996751417e+01', '1.515498862996e+00', '9.525992853119e-01', '2.200000000000e+00', '4.802202307275e-01', '1.110163814455e+00', '1.970436461015e-01', '4.552940319412e-01', '1.971421679245e-01', '4.552940319412e-01', '2.164998375709e-01', '5.000000000000e-01', '4.762996426559e-01', '1.100000000000e+00', '2.597998050850e-01', '6.000000000000e-01', '1.255699057911e+00', '2.900000000000e+00'], ['18', 'DIS_1JET', '2.850000000000e+03', '1.450000000000e+01', '3.190000000000e+02', '2.852850712500e+01', '1.141140285000e+00', '3.993990997500e-01', '1.400000000000e+00', '4.422094385017e-01', '1.550836646595e+00', '2.851425000000e-02', '1.000000000000e-01', '1.581191652889e-01', '5.542497004702e-01', '1.711710427500e-01', '6.000000000000e-01', '3.138135783750e-01', '1.100000000000e+00', '1.711710427500e-01', '6.000000000000e-01', '8.273267066250e-01', '2.900000000000e+00'], ['19', 'DIS_1JET', '2.850000000000e+03', '2.400000000000e+01', '3.190000000000e+02', '1.069999732500e+01', '5.242998689250e-01', '2.888999277750e-01', '2.700000000000e+00', '2.943472566544e-01', '2.752285083237e+00', '1.069465000000e-02', '1.000000000000e-01', '5.930470312414e-02', '5.542497004702e-01', '4.279998930000e-02', '4.000000000000e-01', '1.176999705750e-01', '1.100000000000e+00', '6.419998395000e-02', '6.000000000000e-01', '3.102999224250e-01', '2.900000000000e+00'], ['20', 'DIS_1JET', '2.850000000000e+03', '4.000000000000e+01', '3.190000000000e+02', '2.044081530000e+00', '1.737469300500e-01', '4.292571213000e-02', '2.100000000000e+00', '9.495865837300e-02', '4.647864398158e+00', '1.769341861456e-03', '8.655926074807e-02', '6.132244590000e-03', '3.000000000000e-01', '4.088163060000e-03', '2.000000000000e-01', '2.044081530000e-02', '1.000000000000e+00', '1.226448918000e-02', '6.000000000000e-01', '5.927836437000e-02', '2.900000000000e+00'], ['21', 'DIS_1JET', '1.000000000000e+04', '9.000000000000e+00', '3.190000000000e+02', '2.571395501979e+00', '3.779951387908e-01', '-7.714186505936e-02', '-3.000000000000e+00', '2.217633874200e-02', '8.533627868550e-01', '1.102535035362e-02', '4.246887574694e-01', '4.242064477684e-02', '1.652187526630e+00', '1.277337104494e-02', '4.967485956598e-01', '4.885651453759e-02', '1.900000000000e+00', '1.542837301187e-02', '6.000000000000e-01', '7.457046955738e-02', '2.900000000000e+00'], ['22', 'DIS_1JET', '1.000000000000e+04', '1.450000000000e+01', '3.190000000000e+02', '1.760953607685e+00', '2.887963916604e-01', '1.937048968454e-02', '1.100000000000e+00', '2.485217093133e-02', '1.425434816076e+00', '1.509897970990e-03', '8.655926074807e-02', '1.934579665249e-02', '1.101344240954e+00', '1.299737337559e-02', '7.380872113191e-01', '3.169716493834e-02', '1.800000000000e+00', '1.056572164611e-02', '6.000000000000e-01', '5.106765462288e-02', '2.900000000000e+00'], ['23', 'DIS_1JET', '1.000000000000e+04', '2.400000000000e+01', '3.190000000000e+02', '6.709991612502e-01', '1.449358188300e-01', '-8.655889180127e-02', '-1.290000000000e+01', '1.412291623568e-02', '2.102653864121e+00', '1.745052446956e-03', '2.599375899303e-01', '1.162786613822e-03', '1.733784592161e-01', '3.719010841387e-03', '5.542497004702e-01', '1.207798490250e-02', '1.800000000000e+00', '4.025994967501e-03', '6.000000000000e-01', '1.945897567625e-02', '2.900000000000e+00'], ['24', 'DIS_1JET', '1.000000000000e+04', '4.000000000000e+01', '3.190000000000e+02', '3.085353405933e-01', '6.078146209687e-02', '-6.016439141569e-02', '-1.950000000000e+01', '8.809210109312e-03', '2.849452331864e+00', '2.677356506943e-04', '8.655926074807e-02', '2.272948236221e-03', '7.370580971263e-01', '2.670659099641e-04', '8.655926074807e-02', '5.553636130679e-03', '1.800000000000e+00', '1.851212043560e-03', '6.000000000000e-01', '8.947524877205e-03', '2.900000000000e+00']] dijet_old_impl_list = [['1', 'DIS_2JET', '1.750000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '2.332986433245e+01', '8.398751159682e-01', '4.899271509815e-01', '2.100000000000e+00', '5.731805998113e-02', '2.451941684468e-01', '3.038958000000e-01', '1.300000000000e+00', '9.567285880348e-02', '4.098824622871e-01', '8.334595116449e-02', '3.572500464504e-01', '1.166493216623e-01', '5.000000000000e-01', '1.399791859947e-01', '6.000000000000e-01', '6.765660656411e-01', '2.900000000000e+00'], ['2', 'DIS_2JET', '1.750000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '1.359998980340e+01', '7.887994085972e-01', '4.759996431190e-01', '3.500000000000e+00', '2.517837167094e-01', '1.852276996656e+00', '3.531616955617e-02', '2.599375899303e-01', '2.717280680000e-02', '2.000000000000e-01', '4.506100232821e-02', '3.313311478877e-01', '6.799994901700e-02', '5.000000000000e-01', '8.159993882040e-02', '6.000000000000e-01', '3.943997042986e-01', '2.900000000000e+00'], ['3', 'DIS_2JET', '1.750000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '3.569995537501e+00', '2.391897010126e-01', '1.427998215000e-01', '4.000000000000e+00', '1.410375931268e-01', '3.948658531429e+00', '6.186513093712e-03', '1.730320487082e-01', '1.185811694750e-02', '3.319944735090e-01', '5.923129415274e-03', '1.659141966161e-01', '1.784997768750e-02', '5.000000000000e-01', '2.141997322501e-02', '6.000000000000e-01', '1.035298705875e-01', '2.900000000000e+00'], ['4', 'DIS_2JET', '1.750000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '4.187346492079e-01', '6.867248247009e-02', '3.266130263821e-02', '7.800000000000e+00', '2.168091095872e-02', '5.149248535500e+00', '5.954546204372e-04', '1.412800761611e-01', '3.009905338278e-03', '7.177315003561e-01', '9.139819830025e-04', '2.182723557106e-01', '2.093673246039e-03', '5.000000000000e-01', '2.512407895247e-03', '6.000000000000e-01', '1.214330482703e-02', '2.900000000000e+00'], ['5', 'DIS_2JET', '2.350000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '1.815435885215e+01', '7.443287129383e-01', '3.630871770431e-01', '2.000000000000e+00', '1.567505980850e-02', '8.655926074807e-02', '2.368065567406e-01', '1.306363319742e+00', '9.419148141445e-02', '5.190961461245e-01', '8.265571239106e-02', '4.552940319412e-01', '9.077179426077e-02', '5.000000000000e-01', '1.089261531129e-01', '6.000000000000e-01', '5.264764067125e-01', '2.900000000000e+00'], ['6', 'DIS_2JET', '2.350000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '1.238138760000e+01', '6.933577056000e-01', '2.723905272000e-01', '2.200000000000e+00', '2.750453053590e-01', '2.222552406094e+00', '4.950080000000e-02', '4.000000000000e-01', '8.091336930916e-02', '6.535080874874e-01', '3.714416280000e-02', '3.000000000000e-01', '6.190693800000e-02', '5.000000000000e-01', '7.428832560000e-02', '6.000000000000e-01', '3.590602404000e-01', '2.900000000000e+00'], ['7', 'DIS_2JET', '2.350000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '2.951471313606e+00', '2.184088772069e-01', '1.180588525442e-01', '4.000000000000e+00', '1.049118052223e-01', '3.551005871609e+00', '4.899359598123e-03', '1.659141966161e-01', '2.952947787500e-03', '1.000000000000e-01', '7.671983400072e-03', '2.599375899303e-01', '1.475735656803e-02', '5.000000000000e-01', '1.770882788164e-02', '6.000000000000e-01', '8.559266809458e-02', '2.900000000000e+00'], ['8', 'DIS_2JET', '2.350000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '3.848578386965e-01', '6.965926880406e-02', '4.772237199836e-02', '1.240000000000e+01', '2.070126341555e-02', '5.368181183352e+00', '8.404589203493e-04', '2.182723557106e-01', '3.334635636703e-04', '8.655926074807e-02', '1.277706755339e-03', '3.319944735090e-01', '1.924289193482e-03', '5.000000000000e-01', '2.309147032179e-03', '6.000000000000e-01', '1.116087732220e-02', '2.900000000000e+00'], ['9', 'DIS_2JET', '3.450000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '1.829085000000e+01', '7.133431500000e-01', '1.829085000000e-01', '1.000000000000e+00', '0.000000000000e+00', '0.000000000000e+00', '2.013000000000e-01', '1.100000000000e+00', '9.150000000000e-02', '5.000000000000e-01', '4.754479466777e-02', '2.599375899303e-01', '7.316340000000e-02', '4.000000000000e-01', '1.097451000000e-01', '6.000000000000e-01', '5.304346500000e-01', '2.900000000000e+00'], ['10', 'DIS_2JET', '3.450000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '1.129435000000e+01', '6.889553500000e-01', '4.178909500000e-01', '3.700000000000e+00', '2.486000000000e-01', '2.200000000000e+00', '3.390000000000e-02', '3.000000000000e-01', '6.266153126121e-02', '5.548042274342e-01', '3.388305000000e-02', '3.000000000000e-01', '4.517740000000e-02', '4.000000000000e-01', '6.776610000000e-02', '6.000000000000e-01', '3.275361500000e-01', '2.900000000000e+00'], ['11', 'DIS_2JET', '3.450000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '3.784819941450e+00', '2.270891964870e-01', '4.541783929740e-02', '1.200000000000e+00', '1.313475922886e-01', '3.461708148764e+00', '3.794300000000e-03', '1.000000000000e-01', '1.244044449638e-02', '3.283644729245e-01', '5.357901593933e-03', '1.415629191565e-01', '1.513927976580e-02', '4.000000000000e-01', '2.270891964870e-02', '6.000000000000e-01', '1.097597783020e-01', '2.900000000000e+00'], ['12', 'DIS_2JET', '3.450000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '3.388651500430e-01', '6.946735575881e-02', '-2.372056050301e-02', '-7.000000000000e+00', '1.977551253445e-02', '5.792136528160e+00', '8.870351800802e-04', '2.601979180124e-01', '2.045447220000e-03', '6.024096385542e-01', '9.603761342813e-04', '2.834095315377e-01', '1.355460600172e-03', '4.000000000000e-01', '2.033190900258e-03', '6.000000000000e-01', '9.827089351246e-03', '2.900000000000e+00'], ['13', 'DIS_2JET', '5.500000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '1.673342087918e+01', '6.860702560462e-01', '1.171339461542e-01', '7.000000000000e-01', '2.361736649163e-02', '1.412800761611e-01', '1.425827699417e-01', '8.525098505363e-01', '5.972040358486e-02', '3.568929749397e-01', '3.346684175835e-02', '2.000000000000e-01', '6.693368351670e-02', '4.000000000000e-01', '1.004005252750e-01', '6.000000000000e-01', '4.852692054961e-01', '2.900000000000e+00'], ['14', 'DIS_2JET', '5.500000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '1.078379730405e+01', '6.793792301552e-01', '3.774329056418e-01', '3.500000000000e+00', '2.225822095317e-01', '2.064042448225e+00', '3.850586794386e-02', '3.572500464504e-01', '5.976916425701e-02', '5.542497004702e-01', '1.078379730405e-02', '1.000000000000e-01', '4.313518921620e-02', '4.000000000000e-01', '6.470278382430e-02', '6.000000000000e-01', '3.127301218175e-01', '2.900000000000e+00'], ['15', 'DIS_2JET', '5.500000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '3.651824087044e+00', '2.264130933967e-01', '8.034012991496e-02', '2.200000000000e+00', '1.186530736012e-01', '3.252395337426e+00', '3.648175000000e-03', '1.000000000000e-01', '1.302659032865e-02', '3.568929749397e-01', '6.052838729189e-03', '1.657483653351e-01', '1.095547226113e-02', '3.000000000000e-01', '2.191094452226e-02', '6.000000000000e-01', '1.059028985243e-01', '2.900000000000e+00'], ['16', 'DIS_2JET', '5.500000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '3.780531631079e-01', '7.712284527401e-02', '-1.398796703499e-02', '-3.700000000000e+00', '2.142858760511e-02', '5.662474610362e+00', '3.277312925923e-04', '8.664586331010e-02', '1.134726852750e-03', '3.000000000000e-01', '3.275674269460e-04', '8.664586331010e-02', '7.561063262158e-04', '2.000000000000e-01', '2.268318978647e-03', '6.000000000000e-01', '1.096354173013e-02', '2.900000000000e+00'], ['17', 'DIS_2JET', '2.850000000000e+03', '9.000000000000e+00', '3.190000000000e+02', '1.492981862873e+01', '6.569120196639e-01', '1.492981862873e-01', '1.000000000000e+00', '6.787273016463e-02', '4.552940319412e-01', '8.266580922124e-02', '5.542497004702e-01', '1.065137174707e-01', '7.134294134408e-01', '5.971927451490e-02', '4.000000000000e-01', '1.791578235447e-01', '1.200000000000e+00', '8.957891177235e-02', '6.000000000000e-01', '4.329647402330e-01', '2.900000000000e+00'], ['18', 'DIS_2JET', '2.850000000000e+03', '1.450000000000e+01', '3.190000000000e+02', '1.320660000000e+01', '6.735366000000e-01', '2.773386000000e-01', '2.100000000000e+00', '1.980000000000e-01', '1.500000000000e+00', '2.188972361635e-02', '1.657483653351e-01', '3.961980000000e-02', '3.000000000000e-01', '6.603300000000e-02', '5.000000000000e-01', '1.452726000000e-01', '1.100000000000e+00', '7.923960000000e-02', '6.000000000000e-01', '3.829914000000e-01', '2.900000000000e+00'], ['19', 'DIS_2JET', '2.850000000000e+03', '2.400000000000e+01', '3.190000000000e+02', '4.769997615000e+00', '2.575798712100e-01', '2.384998807500e-01', '5.000000000000e+00', '1.216817557196e-01', '2.552256331931e+00', '7.906195049935e-03', '1.657483653351e-01', '1.239282042995e-02', '2.596777822442e-01', '1.704081869527e-02', '3.572500464504e-01', '5.246997376500e-02', '1.100000000000e+00', '2.861998569000e-02', '6.000000000000e-01', '1.383299308350e-01', '2.900000000000e+00'], ['20', 'DIS_2JET', '2.850000000000e+03', '4.000000000000e+01', '3.190000000000e+02', '9.574765845645e-01', '9.862008821014e-02', '1.914953169129e-02', '2.000000000000e+00', '4.404279943419e-02', '4.597575823778e+00', '1.659230195466e-03', '1.730320487082e-01', '3.144012940281e-03', '3.283644729245e-01', '9.574765845645e-04', '1.000000000000e-01', '9.574765845645e-03', '1.000000000000e+00', '5.744859507387e-03', '6.000000000000e-01', '2.776682095237e-02', '2.900000000000e+00'], ['21', 'DIS_2JET', '1.000000000000e+04', '9.000000000000e+00', '3.190000000000e+02', '7.304516141587e-01', '1.680038712565e-01', '-1.606993551149e-02', '-2.200000000000e+00', '4.766455994762e-03', '6.522043307043e-01', '3.013258361078e-03', '4.131368362342e-01', '9.247363761182e-03', '1.269776898659e+00', '4.246488757399e-03', '5.813511360763e-01', '1.533948389733e-02', '2.100000000000e+00', '4.382709684952e-03', '6.000000000000e-01', '2.118309681060e-02', '2.900000000000e+00'], ['22', 'DIS_2JET', '1.000000000000e+04', '1.450000000000e+01', '3.190000000000e+02', '8.706033123616e-01', '1.749912657847e-01', '8.270731467435e-02', '9.500000000000e+00', '1.947170447085e-02', '2.279271735273e+00', '1.416687987638e-03', '1.657483653351e-01', '1.753222436605e-02', '2.027898319008e+00', '1.279419312639e-02', '1.469577813997e+00', '1.567085962251e-02', '1.800000000000e+00', '5.223619874170e-03', '6.000000000000e-01', '2.524749605849e-02', '2.900000000000e+00'], ['23', 'DIS_2JET', '1.000000000000e+04', '2.400000000000e+01', '3.190000000000e+02', '3.432745801866e-01', '6.625199397602e-02', '-1.647717984896e-02', '-4.800000000000e+00', '7.582220782858e-03', '2.185670118954e+00', '1.150557039523e-03', '3.319944735090e-01', '3.213856823901e-03', '9.320219593463e-01', '3.391765881260e-03', '9.880620579059e-01', '6.522217023546e-03', '1.900000000000e+00', '2.059647481120e-03', '6.000000000000e-01', '9.954962825412e-03', '2.900000000000e+00'], ['24', 'DIS_2JET', '1.000000000000e+04', '4.000000000000e+01', '3.190000000000e+02', '1.496621703174e-01', '4.025912381538e-02', '-1.122466277381e-02', '-7.500000000000e+00', '3.816341965810e-03', '2.578104267278e+00', '2.563937489936e-04', '1.730320487082e-01', '2.067852779348e-03', '1.386516218276e+00', '1.218485308050e-03', '8.141571817822e-01', '2.693919065713e-03', '1.800000000000e+00', '8.979730219045e-04', '6.000000000000e-01', '4.340202939205e-03', '2.900000000000e+00']] diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py index 62c14fd524..79e6f4eaba 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py @@ -1,8 +1,8 @@ import yaml import numpy # use #1693 -from validphys.commondata_utils import covmat_to_artunc as cta -from validphys.commondata_utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta def artunc(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py index ef9efcca95..c5dbbd4e7f 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py @@ -1,8 +1,8 @@ import artUnc import yaml # use #1693 -from validphys.commondata_utils import percentage_to_absolute as pta -from validphys.commondata_utils import symmetrize_errors as se +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se from math import sqrt def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py index 830395fa83..c164efcc05 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py @@ -1,6 +1,6 @@ import yaml # use #1693 -from validphys.commondata_utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta from manual_impl import dijet_data, dijet_sys, artunc def processData(): diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py index ca27217dfb..d1554b3822 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py @@ -1,7 +1,7 @@ from math import sqrt # use #1693 -from validphys.commondata_utils import cormat_to_covmat as ctc -from validphys.commondata_utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta jet_old_impl_list = [['1', 'DIS_1JET', '1.750000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '7.042365878823e+01', '1.901438787282e+00', '7.042365878823e-01', '1.000000000000e+00', '7.130250486484e-01', '1.010961455291e+00', '6.718827732504e-01', '9.531044964111e-01', '2.517144109385e-01', '3.572500464504e-01', '2.515885537330e-01', '3.572500464504e-01', '3.521182939412e-01', '5.000000000000e-01', '4.225419527294e-01', '6.000000000000e-01', '2.042286104859e+00', '2.900000000000e+00'], ['2', 'DIS_1JET', '1.750000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '3.095350776162e+01', '1.269093818226e+00', '8.666982173254e-01', '2.800000000000e+00', '7.598162606841e-01', '2.452246318915e+00', '1.718173641914e-01', '5.542497004702e-01', '1.910967863178e-01', '6.170584587557e-01', '8.045980207445e-02', '2.599375899303e-01', '1.547675388081e-01', '5.000000000000e-01', '1.857210465697e-01', '6.000000000000e-01', '8.976517250870e-01', '2.900000000000e+00'], ['3', 'DIS_1JET', '1.750000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '8.082109035000e+00', '5.172549782400e-01', '2.828738162250e-01', '3.500000000000e+00', '2.743800000000e-01', '3.400000000000e+00', '1.976738222426e-02', '2.447042700083e-01', '3.679736009134e-02', '4.552940319412e-01', '8.082109035000e-03', '1.000000000000e-01', '4.041054517500e-02', '5.000000000000e-01', '4.849265421000e-02', '6.000000000000e-01', '2.343811620150e-01', '2.900000000000e+00'], ['4', 'DIS_1JET', '1.750000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '9.125014074304e-01', '1.396127153368e-01', '1.067626646694e-01', '1.170000000000e+01', '4.688994472166e-02', '5.118073262173e+00', '1.519286117216e-03', '1.657483653351e-01', '4.299338659215e-03', '4.704529347867e-01', '1.991738317891e-03', '2.182723557106e-01', '4.562507037152e-03', '5.000000000000e-01', '5.475008444582e-03', '6.000000000000e-01', '2.646254081548e-02', '2.900000000000e+00'], ['5', 'DIS_1JET', '2.350000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '5.493706846573e+01', '1.648112053972e+00', '-3.296224107944e-01', '-6.000000000000e-01', '5.220401134013e-01', '9.531044964111e-01', '6.074575198510e-01', '1.107945704936e+00', '4.273370774492e-01', '7.782554801857e-01', '1.960665379920e-01', '3.568929749397e-01', '2.746853423286e-01', '5.000000000000e-01', '3.296224107944e-01', '6.000000000000e-01', '1.593174985506e+00', '2.900000000000e+00'], ['6', 'DIS_1JET', '2.350000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '2.680000000000e+01', '1.098800000000e+00', '9.112000000000e-01', '3.400000000000e+00', '6.432000000000e-01', '2.400000000000e+00', '1.072000000000e-01', '4.000000000000e-01', '1.608000000000e-01', '6.000000000000e-01', '8.040000000000e-02', '3.000000000000e-01', '1.340000000000e-01', '5.000000000000e-01', '1.608000000000e-01', '6.000000000000e-01', '7.772000000000e-01', '2.900000000000e+00'], ['7', 'DIS_1JET', '2.350000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '7.013496240129e+00', '4.628907518485e-01', '3.366478195262e-01', '4.800000000000e+00', '2.492988998672e-01', '3.551005871609e+00', '1.404103000000e-02', '2.000000000000e-01', '3.893063895065e-02', '5.548042274342e-01', '2.505571857565e-02', '3.572500464504e-01', '3.506748120064e-02', '5.000000000000e-01', '4.208097744077e-02', '6.000000000000e-01', '2.033913909637e-01', '2.900000000000e+00'], ['8', 'DIS_1JET', '2.350000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '8.549775241245e-01', '1.299565836669e-01', '3.932896610973e-02', '4.600000000000e+00', '4.505743015308e-02', '5.264739441654e+00', '1.865246959223e-03', '2.182723557106e-01', '7.400622244443e-04', '8.655926074807e-02', '2.564932572373e-03', '3.000000000000e-01', '4.274887620623e-03', '5.000000000000e-01', '5.129865144747e-03', '6.000000000000e-01', '2.479434819961e-02', '2.900000000000e+00'], ['9', 'DIS_1JET', '3.450000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '5.209563915000e+01', '1.562869174500e+00', '7.814345872500e-01', '1.500000000000e+00', '4.972717868530e-01', '9.531044964111e-01', '5.217390000000e-01', '1.000000000000e+00', '4.570802892413e-01', '8.773868536773e-01', '1.562869174500e-01', '3.000000000000e-01', '2.604781957500e-01', '5.000000000000e-01', '3.125738349000e-01', '6.000000000000e-01', '1.510773535350e+00', '2.900000000000e+00'], ['10', 'DIS_1JET', '3.450000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '2.785563475695e+01', '1.114225390278e+00', '8.635246774655e-01', '3.100000000000e+00', '6.258088126577e-01', '2.249985843976e+00', '1.112556000000e-01', '4.000000000000e-01', '2.259611917827e-01', '8.115922482154e-01', '7.233489456689e-02', '2.596777822442e-01', '1.114225390278e-01', '4.000000000000e-01', '1.671338085417e-01', '6.000000000000e-01', '8.078134079515e-01', '2.900000000000e+00'], ['11', 'DIS_1JET', '3.450000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '6.962069709237e+00', '4.734207402281e-01', '1.322793244755e-01', '1.900000000000e+00', '2.518340918144e-01', '3.606383090020e+00', '1.158001203865e-02', '1.657483653351e-01', '5.928243109147e-02', '8.502285946131e-01', '1.811516043400e-02', '2.601979180124e-01', '2.784827883695e-02', '4.000000000000e-01', '4.177241825542e-02', '6.000000000000e-01', '2.019000215679e-01', '2.900000000000e+00'], ['12', 'DIS_1JET', '3.450000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '8.702993693220e-01', '1.314152047676e-01', '-2.610898107966e-02', '-3.000000000000e+00', '4.826471687216e-02', '5.562396168725e+00', '1.502894423550e-03', '1.733784592161e-01', '5.667628733710e-03', '6.522043307043e-01', '2.849193515760e-03', '3.273808549327e-01', '3.481197477288e-03', '4.000000000000e-01', '5.221796215932e-03', '6.000000000000e-01', '2.523868171034e-02', '2.900000000000e+00'], ['13', 'DIS_1JET', '5.500000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '4.877557560000e+01', '1.560818419200e+00', '7.316336340000e-01', '1.500000000000e+00', '6.381428053344e-01', '1.308978661724e+00', '3.412584000000e-01', '7.000000000000e-01', '5.616976088752e-01', '1.151596064148e+00', '9.755115120000e-02', '2.000000000000e-01', '1.951023024000e-01', '4.000000000000e-01', '2.926534536000e-01', '6.000000000000e-01', '1.414491692400e+00', '2.900000000000e+00'], ['14', 'DIS_1JET', '5.500000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '2.690000000000e+01', '1.102900000000e+00', '3.228000000000e-01', '1.200000000000e+00', '5.380000000000e-01', '2.000000000000e+00', '1.076000000000e-01', '4.000000000000e-01', '1.883000000000e-01', '7.000000000000e-01', '2.690000000000e-02', '1.000000000000e-01', '1.076000000000e-01', '4.000000000000e-01', '1.614000000000e-01', '6.000000000000e-01', '7.801000000000e-01', '2.900000000000e+00'], ['15', 'DIS_1JET', '5.500000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '7.949992050000e+00', '4.849495150500e-01', '2.782497217500e-01', '3.500000000000e+00', '2.943647864470e-01', '3.699002713601e+00', '2.639353425041e-02', '3.319944735090e-01', '6.359993640000e-02', '8.000000000000e-01', '7.949992050000e-03', '1.000000000000e-01', '2.384997615000e-02', '3.000000000000e-01', '4.769995230000e-02', '6.000000000000e-01', '2.305497694500e-01', '2.900000000000e+00'], ['16', 'DIS_1JET', '5.500000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '8.561421438570e-01', '1.412634537364e-01', '-7.619665080327e-02', '-8.900000000000e+00', '4.800730113222e-02', '5.596189240424e+00', '1.213193003977e-03', '1.415629191565e-01', '1.211979810973e-03', '1.415629191565e-01', '8.561421438570e-04', '1.000000000000e-01', '1.712284287714e-03', '2.000000000000e-01', '5.136852863142e-03', '6.000000000000e-01', '2.482812217185e-02', '2.900000000000e+00'], ['17', 'DIS_1JET', '2.850000000000e+03', '9.000000000000e+00', '3.190000000000e+02', '4.329996751417e+01', '1.515498862996e+00', '9.525992853119e-01', '2.200000000000e+00', '4.802202307275e-01', '1.110163814455e+00', '1.970436461015e-01', '4.552940319412e-01', '1.971421679245e-01', '4.552940319412e-01', '2.164998375709e-01', '5.000000000000e-01', '4.762996426559e-01', '1.100000000000e+00', '2.597998050850e-01', '6.000000000000e-01', '1.255699057911e+00', '2.900000000000e+00'], ['18', 'DIS_1JET', '2.850000000000e+03', '1.450000000000e+01', '3.190000000000e+02', '2.852850712500e+01', '1.141140285000e+00', '3.993990997500e-01', '1.400000000000e+00', '4.422094385017e-01', '1.550836646595e+00', '2.851425000000e-02', '1.000000000000e-01', '1.581191652889e-01', '5.542497004702e-01', '1.711710427500e-01', '6.000000000000e-01', '3.138135783750e-01', '1.100000000000e+00', '1.711710427500e-01', '6.000000000000e-01', '8.273267066250e-01', '2.900000000000e+00'], ['19', 'DIS_1JET', '2.850000000000e+03', '2.400000000000e+01', '3.190000000000e+02', '1.069999732500e+01', '5.242998689250e-01', '2.888999277750e-01', '2.700000000000e+00', '2.943472566544e-01', '2.752285083237e+00', '1.069465000000e-02', '1.000000000000e-01', '5.930470312414e-02', '5.542497004702e-01', '4.279998930000e-02', '4.000000000000e-01', '1.176999705750e-01', '1.100000000000e+00', '6.419998395000e-02', '6.000000000000e-01', '3.102999224250e-01', '2.900000000000e+00'], ['20', 'DIS_1JET', '2.850000000000e+03', '4.000000000000e+01', '3.190000000000e+02', '2.044081530000e+00', '1.737469300500e-01', '4.292571213000e-02', '2.100000000000e+00', '9.495865837300e-02', '4.647864398158e+00', '1.769341861456e-03', '8.655926074807e-02', '6.132244590000e-03', '3.000000000000e-01', '4.088163060000e-03', '2.000000000000e-01', '2.044081530000e-02', '1.000000000000e+00', '1.226448918000e-02', '6.000000000000e-01', '5.927836437000e-02', '2.900000000000e+00'], ['21', 'DIS_1JET', '1.000000000000e+04', '9.000000000000e+00', '3.190000000000e+02', '2.571395501979e+00', '3.779951387908e-01', '-7.714186505936e-02', '-3.000000000000e+00', '2.217633874200e-02', '8.533627868550e-01', '1.102535035362e-02', '4.246887574694e-01', '4.242064477684e-02', '1.652187526630e+00', '1.277337104494e-02', '4.967485956598e-01', '4.885651453759e-02', '1.900000000000e+00', '1.542837301187e-02', '6.000000000000e-01', '7.457046955738e-02', '2.900000000000e+00'], ['22', 'DIS_1JET', '1.000000000000e+04', '1.450000000000e+01', '3.190000000000e+02', '1.760953607685e+00', '2.887963916604e-01', '1.937048968454e-02', '1.100000000000e+00', '2.485217093133e-02', '1.425434816076e+00', '1.509897970990e-03', '8.655926074807e-02', '1.934579665249e-02', '1.101344240954e+00', '1.299737337559e-02', '7.380872113191e-01', '3.169716493834e-02', '1.800000000000e+00', '1.056572164611e-02', '6.000000000000e-01', '5.106765462288e-02', '2.900000000000e+00'], ['23', 'DIS_1JET', '1.000000000000e+04', '2.400000000000e+01', '3.190000000000e+02', '6.709991612502e-01', '1.449358188300e-01', '-8.655889180127e-02', '-1.290000000000e+01', '1.412291623568e-02', '2.102653864121e+00', '1.745052446956e-03', '2.599375899303e-01', '1.162786613822e-03', '1.733784592161e-01', '3.719010841387e-03', '5.542497004702e-01', '1.207798490250e-02', '1.800000000000e+00', '4.025994967501e-03', '6.000000000000e-01', '1.945897567625e-02', '2.900000000000e+00'], ['24', 'DIS_1JET', '1.000000000000e+04', '4.000000000000e+01', '3.190000000000e+02', '3.085353405933e-01', '6.078146209687e-02', '-6.016439141569e-02', '-1.950000000000e+01', '8.809210109312e-03', '2.849452331864e+00', '2.677356506943e-04', '8.655926074807e-02', '2.272948236221e-03', '7.370580971263e-01', '2.670659099641e-04', '8.655926074807e-02', '5.553636130679e-03', '1.800000000000e+00', '1.851212043560e-03', '6.000000000000e-01', '8.947524877205e-03', '2.900000000000e+00']] dijet_old_impl_list = [['1', 'DIS_2JET', '1.750000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '2.332986433245e+01', '8.398751159682e-01', '4.899271509815e-01', '2.100000000000e+00', '5.731805998113e-02', '2.451941684468e-01', '3.038958000000e-01', '1.300000000000e+00', '9.567285880348e-02', '4.098824622871e-01', '8.334595116449e-02', '3.572500464504e-01', '1.166493216623e-01', '5.000000000000e-01', '1.399791859947e-01', '6.000000000000e-01', '6.765660656411e-01', '2.900000000000e+00'], ['2', 'DIS_2JET', '1.750000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '1.359998980340e+01', '7.887994085972e-01', '4.759996431190e-01', '3.500000000000e+00', '2.517837167094e-01', '1.852276996656e+00', '3.531616955617e-02', '2.599375899303e-01', '2.717280680000e-02', '2.000000000000e-01', '4.506100232821e-02', '3.313311478877e-01', '6.799994901700e-02', '5.000000000000e-01', '8.159993882040e-02', '6.000000000000e-01', '3.943997042986e-01', '2.900000000000e+00'], ['3', 'DIS_2JET', '1.750000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '3.569995537501e+00', '2.391897010126e-01', '1.427998215000e-01', '4.000000000000e+00', '1.410375931268e-01', '3.948658531429e+00', '6.186513093712e-03', '1.730320487082e-01', '1.185811694750e-02', '3.319944735090e-01', '5.923129415274e-03', '1.659141966161e-01', '1.784997768750e-02', '5.000000000000e-01', '2.141997322501e-02', '6.000000000000e-01', '1.035298705875e-01', '2.900000000000e+00'], ['4', 'DIS_2JET', '1.750000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '4.187346492079e-01', '6.867248247009e-02', '3.266130263821e-02', '7.800000000000e+00', '2.168091095872e-02', '5.149248535500e+00', '5.954546204372e-04', '1.412800761611e-01', '3.009905338278e-03', '7.177315003561e-01', '9.139819830025e-04', '2.182723557106e-01', '2.093673246039e-03', '5.000000000000e-01', '2.512407895247e-03', '6.000000000000e-01', '1.214330482703e-02', '2.900000000000e+00'], ['5', 'DIS_2JET', '2.350000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '1.815435885215e+01', '7.443287129383e-01', '3.630871770431e-01', '2.000000000000e+00', '1.567505980850e-02', '8.655926074807e-02', '2.368065567406e-01', '1.306363319742e+00', '9.419148141445e-02', '5.190961461245e-01', '8.265571239106e-02', '4.552940319412e-01', '9.077179426077e-02', '5.000000000000e-01', '1.089261531129e-01', '6.000000000000e-01', '5.264764067125e-01', '2.900000000000e+00'], ['6', 'DIS_2JET', '2.350000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '1.238138760000e+01', '6.933577056000e-01', '2.723905272000e-01', '2.200000000000e+00', '2.750453053590e-01', '2.222552406094e+00', '4.950080000000e-02', '4.000000000000e-01', '8.091336930916e-02', '6.535080874874e-01', '3.714416280000e-02', '3.000000000000e-01', '6.190693800000e-02', '5.000000000000e-01', '7.428832560000e-02', '6.000000000000e-01', '3.590602404000e-01', '2.900000000000e+00'], ['7', 'DIS_2JET', '2.350000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '2.951471313606e+00', '2.184088772069e-01', '1.180588525442e-01', '4.000000000000e+00', '1.049118052223e-01', '3.551005871609e+00', '4.899359598123e-03', '1.659141966161e-01', '2.952947787500e-03', '1.000000000000e-01', '7.671983400072e-03', '2.599375899303e-01', '1.475735656803e-02', '5.000000000000e-01', '1.770882788164e-02', '6.000000000000e-01', '8.559266809458e-02', '2.900000000000e+00'], ['8', 'DIS_2JET', '2.350000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '3.848578386965e-01', '6.965926880406e-02', '4.772237199836e-02', '1.240000000000e+01', '2.070126341555e-02', '5.368181183352e+00', '8.404589203493e-04', '2.182723557106e-01', '3.334635636703e-04', '8.655926074807e-02', '1.277706755339e-03', '3.319944735090e-01', '1.924289193482e-03', '5.000000000000e-01', '2.309147032179e-03', '6.000000000000e-01', '1.116087732220e-02', '2.900000000000e+00'], ['9', 'DIS_2JET', '3.450000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '1.829085000000e+01', '7.133431500000e-01', '1.829085000000e-01', '1.000000000000e+00', '0.000000000000e+00', '0.000000000000e+00', '2.013000000000e-01', '1.100000000000e+00', '9.150000000000e-02', '5.000000000000e-01', '4.754479466777e-02', '2.599375899303e-01', '7.316340000000e-02', '4.000000000000e-01', '1.097451000000e-01', '6.000000000000e-01', '5.304346500000e-01', '2.900000000000e+00'], ['10', 'DIS_2JET', '3.450000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '1.129435000000e+01', '6.889553500000e-01', '4.178909500000e-01', '3.700000000000e+00', '2.486000000000e-01', '2.200000000000e+00', '3.390000000000e-02', '3.000000000000e-01', '6.266153126121e-02', '5.548042274342e-01', '3.388305000000e-02', '3.000000000000e-01', '4.517740000000e-02', '4.000000000000e-01', '6.776610000000e-02', '6.000000000000e-01', '3.275361500000e-01', '2.900000000000e+00'], ['11', 'DIS_2JET', '3.450000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '3.784819941450e+00', '2.270891964870e-01', '4.541783929740e-02', '1.200000000000e+00', '1.313475922886e-01', '3.461708148764e+00', '3.794300000000e-03', '1.000000000000e-01', '1.244044449638e-02', '3.283644729245e-01', '5.357901593933e-03', '1.415629191565e-01', '1.513927976580e-02', '4.000000000000e-01', '2.270891964870e-02', '6.000000000000e-01', '1.097597783020e-01', '2.900000000000e+00'], ['12', 'DIS_2JET', '3.450000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '3.388651500430e-01', '6.946735575881e-02', '-2.372056050301e-02', '-7.000000000000e+00', '1.977551253445e-02', '5.792136528160e+00', '8.870351800802e-04', '2.601979180124e-01', '2.045447220000e-03', '6.024096385542e-01', '9.603761342813e-04', '2.834095315377e-01', '1.355460600172e-03', '4.000000000000e-01', '2.033190900258e-03', '6.000000000000e-01', '9.827089351246e-03', '2.900000000000e+00'], ['13', 'DIS_2JET', '5.500000000000e+02', '9.000000000000e+00', '3.190000000000e+02', '1.673342087918e+01', '6.860702560462e-01', '1.171339461542e-01', '7.000000000000e-01', '2.361736649163e-02', '1.412800761611e-01', '1.425827699417e-01', '8.525098505363e-01', '5.972040358486e-02', '3.568929749397e-01', '3.346684175835e-02', '2.000000000000e-01', '6.693368351670e-02', '4.000000000000e-01', '1.004005252750e-01', '6.000000000000e-01', '4.852692054961e-01', '2.900000000000e+00'], ['14', 'DIS_2JET', '5.500000000000e+02', '1.450000000000e+01', '3.190000000000e+02', '1.078379730405e+01', '6.793792301552e-01', '3.774329056418e-01', '3.500000000000e+00', '2.225822095317e-01', '2.064042448225e+00', '3.850586794386e-02', '3.572500464504e-01', '5.976916425701e-02', '5.542497004702e-01', '1.078379730405e-02', '1.000000000000e-01', '4.313518921620e-02', '4.000000000000e-01', '6.470278382430e-02', '6.000000000000e-01', '3.127301218175e-01', '2.900000000000e+00'], ['15', 'DIS_2JET', '5.500000000000e+02', '2.400000000000e+01', '3.190000000000e+02', '3.651824087044e+00', '2.264130933967e-01', '8.034012991496e-02', '2.200000000000e+00', '1.186530736012e-01', '3.252395337426e+00', '3.648175000000e-03', '1.000000000000e-01', '1.302659032865e-02', '3.568929749397e-01', '6.052838729189e-03', '1.657483653351e-01', '1.095547226113e-02', '3.000000000000e-01', '2.191094452226e-02', '6.000000000000e-01', '1.059028985243e-01', '2.900000000000e+00'], ['16', 'DIS_2JET', '5.500000000000e+02', '4.000000000000e+01', '3.190000000000e+02', '3.780531631079e-01', '7.712284527401e-02', '-1.398796703499e-02', '-3.700000000000e+00', '2.142858760511e-02', '5.662474610362e+00', '3.277312925923e-04', '8.664586331010e-02', '1.134726852750e-03', '3.000000000000e-01', '3.275674269460e-04', '8.664586331010e-02', '7.561063262158e-04', '2.000000000000e-01', '2.268318978647e-03', '6.000000000000e-01', '1.096354173013e-02', '2.900000000000e+00'], ['17', 'DIS_2JET', '2.850000000000e+03', '9.000000000000e+00', '3.190000000000e+02', '1.492981862873e+01', '6.569120196639e-01', '1.492981862873e-01', '1.000000000000e+00', '6.787273016463e-02', '4.552940319412e-01', '8.266580922124e-02', '5.542497004702e-01', '1.065137174707e-01', '7.134294134408e-01', '5.971927451490e-02', '4.000000000000e-01', '1.791578235447e-01', '1.200000000000e+00', '8.957891177235e-02', '6.000000000000e-01', '4.329647402330e-01', '2.900000000000e+00'], ['18', 'DIS_2JET', '2.850000000000e+03', '1.450000000000e+01', '3.190000000000e+02', '1.320660000000e+01', '6.735366000000e-01', '2.773386000000e-01', '2.100000000000e+00', '1.980000000000e-01', '1.500000000000e+00', '2.188972361635e-02', '1.657483653351e-01', '3.961980000000e-02', '3.000000000000e-01', '6.603300000000e-02', '5.000000000000e-01', '1.452726000000e-01', '1.100000000000e+00', '7.923960000000e-02', '6.000000000000e-01', '3.829914000000e-01', '2.900000000000e+00'], ['19', 'DIS_2JET', '2.850000000000e+03', '2.400000000000e+01', '3.190000000000e+02', '4.769997615000e+00', '2.575798712100e-01', '2.384998807500e-01', '5.000000000000e+00', '1.216817557196e-01', '2.552256331931e+00', '7.906195049935e-03', '1.657483653351e-01', '1.239282042995e-02', '2.596777822442e-01', '1.704081869527e-02', '3.572500464504e-01', '5.246997376500e-02', '1.100000000000e+00', '2.861998569000e-02', '6.000000000000e-01', '1.383299308350e-01', '2.900000000000e+00'], ['20', 'DIS_2JET', '2.850000000000e+03', '4.000000000000e+01', '3.190000000000e+02', '9.574765845645e-01', '9.862008821014e-02', '1.914953169129e-02', '2.000000000000e+00', '4.404279943419e-02', '4.597575823778e+00', '1.659230195466e-03', '1.730320487082e-01', '3.144012940281e-03', '3.283644729245e-01', '9.574765845645e-04', '1.000000000000e-01', '9.574765845645e-03', '1.000000000000e+00', '5.744859507387e-03', '6.000000000000e-01', '2.776682095237e-02', '2.900000000000e+00'], ['21', 'DIS_2JET', '1.000000000000e+04', '9.000000000000e+00', '3.190000000000e+02', '7.304516141587e-01', '1.680038712565e-01', '-1.606993551149e-02', '-2.200000000000e+00', '4.766455994762e-03', '6.522043307043e-01', '3.013258361078e-03', '4.131368362342e-01', '9.247363761182e-03', '1.269776898659e+00', '4.246488757399e-03', '5.813511360763e-01', '1.533948389733e-02', '2.100000000000e+00', '4.382709684952e-03', '6.000000000000e-01', '2.118309681060e-02', '2.900000000000e+00'], ['22', 'DIS_2JET', '1.000000000000e+04', '1.450000000000e+01', '3.190000000000e+02', '8.706033123616e-01', '1.749912657847e-01', '8.270731467435e-02', '9.500000000000e+00', '1.947170447085e-02', '2.279271735273e+00', '1.416687987638e-03', '1.657483653351e-01', '1.753222436605e-02', '2.027898319008e+00', '1.279419312639e-02', '1.469577813997e+00', '1.567085962251e-02', '1.800000000000e+00', '5.223619874170e-03', '6.000000000000e-01', '2.524749605849e-02', '2.900000000000e+00'], ['23', 'DIS_2JET', '1.000000000000e+04', '2.400000000000e+01', '3.190000000000e+02', '3.432745801866e-01', '6.625199397602e-02', '-1.647717984896e-02', '-4.800000000000e+00', '7.582220782858e-03', '2.185670118954e+00', '1.150557039523e-03', '3.319944735090e-01', '3.213856823901e-03', '9.320219593463e-01', '3.391765881260e-03', '9.880620579059e-01', '6.522217023546e-03', '1.900000000000e+00', '2.059647481120e-03', '6.000000000000e-01', '9.954962825412e-03', '2.900000000000e+00'], ['24', 'DIS_2JET', '1.000000000000e+04', '4.000000000000e+01', '3.190000000000e+02', '1.496621703174e-01', '4.025912381538e-02', '-1.122466277381e-02', '-7.500000000000e+00', '3.816341965810e-03', '2.578104267278e+00', '2.563937489936e-04', '1.730320487082e-01', '2.067852779348e-03', '1.386516218276e+00', '1.218485308050e-03', '8.141571817822e-01', '2.693919065713e-03', '1.800000000000e+00', '8.979730219045e-04', '6.000000000000e-01', '4.340202939205e-03', '2.900000000000e+00']] diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py index 6532b07c03..3c50ac6a81 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py @@ -1,5 +1,5 @@ import yaml -from validphys.commondata_utils import symmetrize_errors as se +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se def processData(): with open('metadata.yaml', 'r') as file: diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py index b23fb6cf8f..810355dc2d 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py @@ -1,5 +1,5 @@ import yaml -from validphys.commondata_utils import symmetrize_errors as se +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se def processData(): with open('metadata.yaml', 'r') as file: diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py index 372e8c209b..1939fce20c 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py @@ -1,5 +1,5 @@ import yaml -from validphys.commondata_utils import symmetrize_errors as se +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se def processData(): with open('metadata.yaml', 'r') as file: From 28658dda8d7b0a077fc640d7a509614e0625108d Mon Sep 17 00:00:00 2001 From: t7phy Date: Wed, 27 Mar 2024 23:26:07 +0100 Subject: [PATCH 4/5] change utils import for DY datasets --- .../nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py | 2 +- .../nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py | 2 +- .../nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py | 2 +- .../nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py | 2 +- .../nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py | 2 +- .../nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py | 2 +- .../nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py | 2 +- .../nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py | 2 +- nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py | 2 +- .../nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py | 2 +- .../nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py | 2 +- .../nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py | 2 +- .../nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py | 2 +- 13 files changed, 13 insertions(+), 13 deletions(-) diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py index ca28a692e9..2df2cee9f2 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py index c92c8b3a29..bcedf2f0e2 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py index b2b8a29c33..802b2e6905 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc MW_VALUE = 80.398 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py index 69e7812ecc..2a7a15d6e4 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc # MZ_VALUE = 91.1876 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py index 28aa733f60..dd25eb259c 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py index 9593ea9f3d..a93b4056aa 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py index fc3708cfb3..4ec8dc0723 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py index b433f9bfbe..6306ce22ac 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py index cee4816cfc..61afd3dfca 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py index a011518d89..1a8d0c467a 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc, percentage_to_absolute +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc, percentage_to_absolute MZ_VALUE = 91.1876 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py index 97ca45b44d..786ef157e6 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py index fcacadfe03..7e94621771 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py index 23b3c32fda..14b17b6780 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py @@ -3,7 +3,7 @@ import pathlib import yaml -from validphys.commondata_utils import covmat_to_artunc +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc MZ_VALUE = 91.1876 # GeV From 810f7be5a72cedd125202c8303aa9eff35c97f33 Mon Sep 17 00:00:00 2001 From: t7phy Date: Wed, 27 Mar 2024 23:59:02 +0100 Subject: [PATCH 5/5] commondata filters formatting --- .../ATHENA_NC_105GEV_EP/filter.py | 32 +- .../ATHENA_NC_140GEV_EP/filter.py | 30 +- .../ATHENA_NC_29GEV_EP/filter.py | 32 +- .../ATHENA_NC_45GEV_EP/filter.py | 32 +- .../ATHENA_NC_63GEV_EP/filter.py | 32 +- .../ATLAS_1JET_13TEV_DIF/filter.py | 63 +- .../ATLAS_1JET_8TEV_R06/filter.py | 22 +- .../ATLAS_1JET_8TEV_R06/filter_utils.py | 3 +- .../ATLAS_2JET_13TEV_DIF/filter.py | 28 +- .../ATLAS_2JET_7TEV_R06/filter.py | 11 +- .../ATLAS_2JET_7TEV_R06/filter_bugged.py | 12 +- .../ATLAS_2JET_7TEV_R06/filter_utils.py | 8 +- .../ATLAS_TTBAR_13TEV_HADR_DIF/utils.py | 120 ++-- .../ATLAS_TTBAR_13TEV_LJ_DIF/filter.py | 270 ++++++--- .../ATLAS_TTBAR_8TEV_2L_DIF/filter.py | 149 +++-- .../ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py | 10 +- .../ATLAS_TTBAR_8TEV_LJ_DIF/filter.py | 552 ++++++++++++++---- .../CMS_1JET_13TEV_DIF/filter.py | 54 +- .../new_commondata/CMS_1JET_8TEV/filter.py | 67 ++- .../CMS_1JET_8TEV/filter_bugged.py | 12 +- .../CMS_1JET_8TEV/filter_utils.py | 82 +-- .../new_commondata/CMS_2JET_7TEV/filter.py | 21 +- .../CMS_2JET_7TEV/filter_utils.py | 273 +++++---- .../CMS_TTBAR_13TEV_2L_DIF/filter.py | 237 +++++--- .../CMS_TTBAR_13TEV_LJ_DIF/filter.py | 513 +++++++++++----- .../CMS_TTBAR_8TEV_2L_DIF/filter.py | 148 +++-- .../CMS_TTBAR_8TEV_LJ_DIF/filter.py | 203 +++++-- .../CMS_WPWM_13TEV_ETA/filter.py | 6 +- .../CMS_WPWM_13TEV_ETA/filter_utils.py | 16 +- .../CMS_WPWM_7TEV_ELECTRON/filter.py | 26 +- .../CMS_WPWM_7TEV_MUON/filter.py | 26 +- .../CMS_WPWM_8TEV_MUON/filter.py | 44 +- .../CMS_Z0_7TEV_DIMUON/filter.py | 30 +- .../COMPASS15_NC_NOTFIXED_MUD/filter.py | 20 +- .../COMPASS15_NC_NOTFIXED_MUP/filter.py | 20 +- .../E142_NC_NOTFIXED_EN/filter.py | 20 +- .../E143_NC_NOTFIXED_ED/filter.py | 15 +- .../E143_NC_NOTFIXED_EP/filter.py | 15 +- .../new_commondata/E154_NC_9GEV_EN/filter.py | 20 +- .../new_commondata/E155_NC_9GEV_EN/filter.py | 15 +- .../new_commondata/E155_NC_9GEV_EP/filter.py | 15 +- .../new_commondata/EIC_NC_211GEV_EP/filter.py | 31 +- .../new_commondata/EIC_NC_43GEV_EP/filter.py | 31 +- .../new_commondata/EIC_NC_67GEV_EP/filter.py | 31 +- .../new_commondata/EIcC_NC_15GEV_EP/filter.py | 31 +- .../new_commondata/EIcC_NC_22GEV_EP/filter.py | 31 +- .../EMC_NC_NOTFIXED_MUP/filter.py | 15 +- .../H1_1JET_319GEV_290PB-1_DIF/artUnc.py | 23 +- .../H1_1JET_319GEV_290PB-1_DIF/filter.py | 382 ++++++++---- .../H1_1JET_319GEV_351PB-1_DIF/filter.py | 78 ++- .../H1_1JET_319GEV_351PB-1_DIF/manual_impl.py | 2 +- .../H1_2JET_319GEV_290PB-1_DIF/artUnc.py | 23 +- .../H1_2JET_319GEV_290PB-1_DIF/filter.py | 207 +++++-- .../H1_2JET_319GEV_351PB-1_DIF/filter.py | 78 ++- .../H1_2JET_319GEV_351PB-1_DIF/manual_impl.py | 2 +- .../HERMES97_NC_7GEV_EN/filter.py | 20 +- .../HERMES_NC_7GEV_ED/filter.py | 17 +- .../HERMES_NC_7GEV_EP/filter.py | 16 +- .../JLABE06_NC_3GEV_EN/filter.py | 22 +- .../JLABE97_NC_NOTFIXED_EN/filter.py | 26 +- .../JLABE99_NC_3GEV_EN/filter.py | 26 +- .../JLABEG1B_NC_NOTFIXED_ED/filter.py | 26 +- .../JLABEG1B_NC_NOTFIXED_EP/filter.py | 26 +- .../JLABEG1DVCS_NC_3GEV_EP/filter.py | 20 +- .../JLABEG1DVCS_NC_5GEV_ED/filter.py | 20 +- .../LHCB_DY_7TEV_MUON/filter.py | 29 +- .../LHCB_DY_8TEV_MUON/filter.py | 29 +- .../LHCB_WPWM_7TEV_MUON/filter.py | 29 +- .../LHCB_WPWM_8TEV_MUON/filter.py | 33 +- .../new_commondata/LHCB_Z0_13TEV/filter.py | 26 +- .../LHCB_Z0_7TEV_DIELECTRON/filter.py | 33 +- .../LHCB_Z0_7TEV_MUON/filter.py | 33 +- .../LHCB_Z0_8TEV_DIELECTRON/filter.py | 23 +- .../LHCB_Z0_8TEV_MUON/filter.py | 33 +- .../SMCSX_NC_17GEV_MUP/filter.py | 26 +- .../SMCSX_NC_24GEV_MUD/filter.py | 26 +- .../SMC_NC_NOTFIXED_MUD/filter.py | 20 +- .../SMC_NC_NOTFIXED_MUP/filter.py | 20 +- .../new_commondata/STAR_WM_510GEV/filter.py | 32 +- .../new_commondata/STAR_WP_510GEV/filter.py | 32 +- .../ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py | 34 +- .../ZEUS_1JET_319GEV_82PB-1_DIF/filter.py | 34 +- .../ZEUS_2JET_319GEV_374PB-1_DIF/filter.py | 34 +- 83 files changed, 2908 insertions(+), 2106 deletions(-) diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_105GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_105GEV_EP/filter.py index 39fc481a25..bef9c94e74 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_105GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_105GEV_EP/filter.py @@ -1,10 +1,10 @@ -import yaml -import numpy as np -import pandas as pd - from pathlib import Path from typing import Optional, Union +import numpy as np +import pandas as pd +import yaml + np.random.seed(1234567890) @@ -57,9 +57,7 @@ def fluctuate_data(central: np.ndarray, abserr: np.ndarray) -> np.ndarray: def write_data( - df: pd.DataFrame, - abserr: Optional[Union[np.ndarray, None]] = None, - add_fluctuate: bool = False, + df: pd.DataFrame, abserr: Optional[Union[np.ndarray, None]] = None, add_fluctuate: bool = False ) -> None: """Write the input kinematics, central values, and uncertainties into the new commondata format. @@ -106,9 +104,7 @@ def write_data( errors = [] for idx, (_, d) in enumerate(df.iterrows()): if not add_fluctuate: - errors.append( - {"stat": None, "sys": None, "shift_lumi": None, "norm": None} - ) + errors.append({"stat": None, "sys": None, "shift_lumi": None, "norm": None}) else: errors.append( { @@ -120,25 +116,21 @@ def write_data( ) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, "sys": { "description": "systematic uncertainty", - "treatment": "MULT", # TODO: to check + "treatment": "MULT", # TODO: to check "type": "UNCORR", }, "shift_lumi": { "description": "uncertainty on the precision of the relative luminosity", "treatment": "ADD", - "type": "UNCORR", # TODO: to check + "type": "UNCORR", # TODO: to check }, "norm": { "description": "relative (percent) normalization uncertainty (beam pol)", - "treatment": "MULT", # TODO: to check - "type": "CORR", # TODO: to check + "treatment": "MULT", # TODO: to check + "type": "CORR", # TODO: to check }, } @@ -153,4 +145,4 @@ def write_data( xdf = read_excel(input_xlsx, beams=BEAMS) cv_preds = read_cvs() fluctuated_cv = fluctuate_data(cv_preds, xdf["delta_ALL"].values) - write_data(xdf, abserr=fluctuated_cv, add_fluctuate=True) \ No newline at end of file + write_data(xdf, abserr=fluctuated_cv, add_fluctuate=True) diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_140GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_140GEV_EP/filter.py index 95bf3ae0fb..013bb52179 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_140GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_140GEV_EP/filter.py @@ -1,10 +1,10 @@ -import yaml -import numpy as np -import pandas as pd - from pathlib import Path from typing import Optional, Union +import numpy as np +import pandas as pd +import yaml + np.random.seed(1234567890) @@ -51,9 +51,7 @@ def fluctuate_data(central: np.ndarray, abserr: np.ndarray) -> np.ndarray: def write_data( - df: pd.DataFrame, - abserr: Optional[Union[np.ndarray, None]] = None, - add_fluctuate: bool = False, + df: pd.DataFrame, abserr: Optional[Union[np.ndarray, None]] = None, add_fluctuate: bool = False ) -> None: """Write the input kinematics, central values, and uncertainties into the new commondata format. @@ -102,9 +100,7 @@ def write_data( errors = [] for idx, (_, d) in enumerate(df.iterrows()): if not add_fluctuate: - errors.append( - {"stat": None, "sys": None, "shift_lumi": None, "norm": None} - ) + errors.append({"stat": None, "sys": None, "shift_lumi": None, "norm": None}) else: errors.append( { @@ -117,25 +113,21 @@ def write_data( print(f"[+] The number of uncertainty points is {len(errors)}") error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, "sys": { "description": "systematic uncertainty", - "treatment": "MULT", # TODO: to check + "treatment": "MULT", # TODO: to check "type": "UNCORR", }, "shift_lumi": { "description": "uncertainty on the precision of the relative luminosity", "treatment": "ADD", - "type": "UNCORR", # TODO: to check + "type": "UNCORR", # TODO: to check }, "norm": { "description": "relative (percent) normalization uncertainty (beam pol)", - "treatment": "MULT", # TODO: to check - "type": "CORR", # TODO: to check + "treatment": "MULT", # TODO: to check + "type": "CORR", # TODO: to check }, } diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_29GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_29GEV_EP/filter.py index b074598e43..5f3c7ada2a 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_29GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_29GEV_EP/filter.py @@ -1,10 +1,10 @@ -import yaml -import numpy as np -import pandas as pd - from pathlib import Path from typing import Optional, Union +import numpy as np +import pandas as pd +import yaml + np.random.seed(1234567890) @@ -57,9 +57,7 @@ def fluctuate_data(central: np.ndarray, abserr: np.ndarray) -> np.ndarray: def write_data( - df: pd.DataFrame, - abserr: Optional[Union[np.ndarray, None]] = None, - add_fluctuate: bool = False, + df: pd.DataFrame, abserr: Optional[Union[np.ndarray, None]] = None, add_fluctuate: bool = False ) -> None: """Write the input kinematics, central values, and uncertainties into the new commondata format. @@ -106,9 +104,7 @@ def write_data( errors = [] for idx, (_, d) in enumerate(df.iterrows()): if not add_fluctuate: - errors.append( - {"stat": None, "sys": None, "shift_lumi": None, "norm": None} - ) + errors.append({"stat": None, "sys": None, "shift_lumi": None, "norm": None}) else: errors.append( { @@ -120,25 +116,21 @@ def write_data( ) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, "sys": { "description": "systematic uncertainty", - "treatment": "MULT", # TODO: to check + "treatment": "MULT", # TODO: to check "type": "UNCORR", }, "shift_lumi": { "description": "uncertainty on the precision of the relative luminosity", "treatment": "ADD", - "type": "UNCORR", # TODO: to check + "type": "UNCORR", # TODO: to check }, "norm": { "description": "relative (percent) normalization uncertainty (beam pol)", - "treatment": "MULT", # TODO: to check - "type": "CORR", # TODO: to check + "treatment": "MULT", # TODO: to check + "type": "CORR", # TODO: to check }, } @@ -153,4 +145,4 @@ def write_data( xdf = read_excel(input_xlsx, beams=BEAMS) cv_preds = read_cvs() fluctuated_cv = fluctuate_data(cv_preds, xdf["delta_ALL"].values) - write_data(xdf, abserr=fluctuated_cv, add_fluctuate=True) \ No newline at end of file + write_data(xdf, abserr=fluctuated_cv, add_fluctuate=True) diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_45GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_45GEV_EP/filter.py index 866d10cd47..d1ef41d2d5 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_45GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_45GEV_EP/filter.py @@ -1,10 +1,10 @@ -import yaml -import numpy as np -import pandas as pd - from pathlib import Path from typing import Optional, Union +import numpy as np +import pandas as pd +import yaml + np.random.seed(1234567890) @@ -57,9 +57,7 @@ def fluctuate_data(central: np.ndarray, abserr: np.ndarray) -> np.ndarray: def write_data( - df: pd.DataFrame, - abserr: Optional[Union[np.ndarray, None]] = None, - add_fluctuate: bool = False, + df: pd.DataFrame, abserr: Optional[Union[np.ndarray, None]] = None, add_fluctuate: bool = False ) -> None: """Write the input kinematics, central values, and uncertainties into the new commondata format. @@ -106,9 +104,7 @@ def write_data( errors = [] for idx, (_, d) in enumerate(df.iterrows()): if not add_fluctuate: - errors.append( - {"stat": None, "sys": None, "shift_lumi": None, "norm": None} - ) + errors.append({"stat": None, "sys": None, "shift_lumi": None, "norm": None}) else: errors.append( { @@ -120,25 +116,21 @@ def write_data( ) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, "sys": { "description": "systematic uncertainty", - "treatment": "MULT", # TODO: to check + "treatment": "MULT", # TODO: to check "type": "UNCORR", }, "shift_lumi": { "description": "uncertainty on the precision of the relative luminosity", "treatment": "ADD", - "type": "UNCORR", # TODO: to check + "type": "UNCORR", # TODO: to check }, "norm": { "description": "relative (percent) normalization uncertainty (beam pol)", - "treatment": "MULT", # TODO: to check - "type": "CORR", # TODO: to check + "treatment": "MULT", # TODO: to check + "type": "CORR", # TODO: to check }, } @@ -153,4 +145,4 @@ def write_data( xdf = read_excel(input_xlsx, beams=BEAMS) cv_preds = read_cvs() fluctuated_cv = fluctuate_data(cv_preds, xdf["delta_ALL"].values) - write_data(xdf, abserr=fluctuated_cv, add_fluctuate=True) \ No newline at end of file + write_data(xdf, abserr=fluctuated_cv, add_fluctuate=True) diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_63GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_63GEV_EP/filter.py index 784e09c031..6495bbf890 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_63GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATHENA_NC_63GEV_EP/filter.py @@ -1,10 +1,10 @@ -import yaml -import numpy as np -import pandas as pd - from pathlib import Path from typing import Optional, Union +import numpy as np +import pandas as pd +import yaml + np.random.seed(1234567890) @@ -57,9 +57,7 @@ def fluctuate_data(central: np.ndarray, abserr: np.ndarray) -> np.ndarray: def write_data( - df: pd.DataFrame, - abserr: Optional[Union[np.ndarray, None]] = None, - add_fluctuate: bool = False, + df: pd.DataFrame, abserr: Optional[Union[np.ndarray, None]] = None, add_fluctuate: bool = False ) -> None: """Write the input kinematics, central values, and uncertainties into the new commondata format. @@ -106,9 +104,7 @@ def write_data( errors = [] for idx, (_, d) in enumerate(df.iterrows()): if not add_fluctuate: - errors.append( - {"stat": None, "sys": None, "shift_lumi": None, "norm": None} - ) + errors.append({"stat": None, "sys": None, "shift_lumi": None, "norm": None}) else: errors.append( { @@ -120,25 +116,21 @@ def write_data( ) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, "sys": { "description": "systematic uncertainty", - "treatment": "MULT", # TODO: to check + "treatment": "MULT", # TODO: to check "type": "UNCORR", }, "shift_lumi": { "description": "uncertainty on the precision of the relative luminosity", "treatment": "ADD", - "type": "UNCORR", # TODO: to check + "type": "UNCORR", # TODO: to check }, "norm": { "description": "relative (percent) normalization uncertainty (beam pol)", - "treatment": "MULT", # TODO: to check - "type": "CORR", # TODO: to check + "treatment": "MULT", # TODO: to check + "type": "CORR", # TODO: to check }, } @@ -153,4 +145,4 @@ def write_data( xdf = read_excel(input_xlsx, beams=BEAMS) cv_preds = read_cvs() fluctuated_cv = fluctuate_data(cv_preds, xdf["delta_ALL"].values) - write_data(xdf, abserr=fluctuated_cv, add_fluctuate=True) \ No newline at end of file + write_data(xdf, abserr=fluctuated_cv, add_fluctuate=True) diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py index b1fb4dcd4b..ff222f5fea 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_13TEV_DIF/filter.py @@ -1,6 +1,8 @@ import yaml + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -15,7 +17,7 @@ def processData(): kin_altcorr1 = [] error_altcorr1 = [] -# jet data + # jet data for i in tables: if i == 1: @@ -37,7 +39,7 @@ def processData(): y_min = 2.5 y_max = 3 y_central = None - hepdata_tables="rawdata/atlas_inclusive_jet2015_r04_eta"+str(i)+".yaml" + hepdata_tables = "rawdata/atlas_inclusive_jet2015_r04_eta" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -50,21 +52,32 @@ def processData(): value_delta = 0 error_value = {} for k in range(len(values[j]['errors'])): - se_delta, se_sigma = se(values[j]['errors'][k]['asymerror']['plus'], values[j]['errors'][k]['asymerror']['minus']) + se_delta, se_sigma = se( + values[j]['errors'][k]['asymerror']['plus'], + values[j]['errors'][k]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_label = str(values[j]['errors'][k]['label']) error_value[error_label] = se_sigma data_central_value = values[j]['value'] + value_delta data_central.append(data_central_value) error.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, 'y': {'min': y_min, 'mid': y_central, 'max': y_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + 'y': {'min': y_min, 'mid': y_central, 'max': y_max}, + } kin.append(kin_value) - hepdata_tables="rawdata/atlas_inclusive_jet2015_r04_eta1.yaml" + hepdata_tables = "rawdata/atlas_inclusive_jet2015_r04_eta1.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) error_definition = {} - error_definition['stat'] = {'description': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} + error_definition['stat'] = { + 'description': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } for i in range(1, len(input['dependent_variables'][0]['values'][0]['errors'])): error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] error_definition[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} @@ -74,15 +87,15 @@ def processData(): uncertainties_yaml = {'definitions': error_definition, 'bins': error} with open('data.yaml', 'w') as file: - yaml.dump(data_central_yaml, file, sort_keys=False) + yaml.dump(data_central_yaml, file, sort_keys=False) with open('kinematics.yaml', 'w') as file: - yaml.dump(kinematics_yaml, file, sort_keys=False) + yaml.dump(kinematics_yaml, file, sort_keys=False) with open('uncertainties.yaml', 'w') as file: yaml.dump(uncertainties_yaml, file, sort_keys=False) -# jet altcorr1 data + # jet altcorr1 data for i in tables_altcorr1: if i == 1: @@ -104,7 +117,7 @@ def processData(): y_min = 2.5 y_max = 3 y_central = None - hepdata_tables="rawdata/atlas_inclusive_jet2015_r04_altcorr1_eta"+str(i)+".yaml" + hepdata_tables = "rawdata/atlas_inclusive_jet2015_r04_altcorr1_eta" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -117,36 +130,52 @@ def processData(): value_delta = 0 error_value = {} for k in range(len(values[j]['errors'])): - se_delta, se_sigma = se(values[j]['errors'][k]['asymerror']['plus'], values[j]['errors'][k]['asymerror']['minus']) + se_delta, se_sigma = se( + values[j]['errors'][k]['asymerror']['plus'], + values[j]['errors'][k]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_label = str(values[j]['errors'][k]['label']) error_value[error_label] = se_sigma data_central_value = values[j]['value'] + value_delta data_central_altcorr1.append(data_central_value) error_altcorr1.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, 'y': {'min': y_min, 'mid': y_central, 'max': y_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + 'y': {'min': y_min, 'mid': y_central, 'max': y_max}, + } kin_altcorr1.append(kin_value) - hepdata_tables="rawdata/atlas_inclusive_jet2015_r04_altcorr1_eta1.yaml" + hepdata_tables = "rawdata/atlas_inclusive_jet2015_r04_altcorr1_eta1.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) error_definition_altcorr1 = {} - error_definition_altcorr1['stat'] = {'description': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} + error_definition_altcorr1['stat'] = { + 'description': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } for i in range(1, len(input['dependent_variables'][0]['values'][0]['errors'])): error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] - error_definition_altcorr1[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_altcorr1[error_name] = { + 'description': '', + 'treatment': 'MULT', + 'type': 'CORR', + } data_central_altcorr1_yaml = {'data_central': data_central_altcorr1} kinematics_altcorr1_yaml = {'bins': kin_altcorr1} uncertainties_altcorr1_yaml = {'definitions': error_definition_altcorr1, 'bins': error_altcorr1} with open('data_altcorr1.yaml', 'w') as file: - yaml.dump(data_central_altcorr1_yaml, file, sort_keys=False) + yaml.dump(data_central_altcorr1_yaml, file, sort_keys=False) with open('kinematics_altcorr1.yaml', 'w') as file: - yaml.dump(kinematics_altcorr1_yaml, file, sort_keys=False) + yaml.dump(kinematics_altcorr1_yaml, file, sort_keys=False) with open('uncertainties_altcorr1.yaml', 'w') as file: yaml.dump(uncertainties_altcorr1_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_8TEV_R06/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_8TEV_R06/filter.py index 1d4ac614be..8efe9a222c 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_8TEV_R06/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_8TEV_R06/filter.py @@ -1,11 +1,11 @@ -import yaml -import numpy as np -import pandas as pd -from filter_utils import get_data_values, get_kinematics, fill_df - # ignore pandas warning import warnings +from filter_utils import fill_df, get_data_values, get_kinematics +import numpy as np +import pandas as pd +import yaml + warnings.filterwarnings("ignore") @@ -43,11 +43,11 @@ def filter_ATLAS_1JET_8TEV_uncertainties(variant='nominal'): """ Writes the uncertainties to a .yaml file. Two possible variants are implemented: nominal and decorrelated - + There are three types of uncertainties: 1. Statistical Uncertainties: ADD, UNCORR - + 2. Systematic Uncertainties: ADD, CORR Constructed following the exp. prescription: @@ -114,7 +114,9 @@ def filter_ATLAS_1JET_8TEV_uncertainties(variant='nominal'): error = [] for n in range(A_corr.shape[0]): error_value = {} - for col, m in zip(df_unc.drop(["stat", "syst_lumi"], axis=1).columns, range(A_corr.shape[1])): + for col, m in zip( + df_unc.drop(["stat", "syst_lumi"], axis=1).columns, range(A_corr.shape[1]) + ): error_value[f"{col}"] = float(A_corr[n, m]) error_value["luminosity_uncertainty"] = float(lum_errors[n]) @@ -124,7 +126,7 @@ def filter_ATLAS_1JET_8TEV_uncertainties(variant='nominal'): uncertainties_yaml = {"definitions": error_definition, "bins": error} # write uncertainties to file - if variant=='nominal': + if variant == 'nominal': with open(f"uncertainties.yaml", "w") as file: yaml.dump(uncertainties_yaml, file, sort_keys=False) else: @@ -150,7 +152,7 @@ def filter_ATLAS_1JET_8TEV_uncertainties(variant='nominal'): # write decorrelated uncertainties file filter_ATLAS_1JET_8TEV_uncertainties(variant='decorrelated') - ## + ## # # code below for testing only. Should be removed at some point # covmat = filter_ATLAS_1JET_8TEV_uncertainties() diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_8TEV_R06/filter_utils.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_8TEV_R06/filter_utils.py index 76bfcb97bb..7655593ec7 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_8TEV_R06/filter_utils.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_1JET_8TEV_R06/filter_utils.py @@ -1,7 +1,6 @@ -import yaml import numpy as np import pandas as pd - +import yaml TABLE_TO_RAPIDITY = { 1: [0.0, 0.5], diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py index 52df10bb73..0655765b51 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py @@ -1,6 +1,8 @@ import yaml + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -31,7 +33,7 @@ def processData(): y_min = 2.5 y_max = 3 y_central = None - hepdata_tables="rawdata/atlas_mjj_jet2015_r04_ystar"+str(i)+".yaml" + hepdata_tables = "rawdata/atlas_mjj_jet2015_r04_ystar" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -44,21 +46,32 @@ def processData(): value_delta = 0 error_value = {} for k in range(len(values[j]['errors'])): - se_delta, se_sigma = se(values[j]['errors'][k]['asymerror']['plus'], values[j]['errors'][k]['asymerror']['minus']) + se_delta, se_sigma = se( + values[j]['errors'][k]['asymerror']['plus'], + values[j]['errors'][k]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_label = str(values[j]['errors'][k]['label']) error_value[error_label] = se_sigma data_central_value = values[j]['value'] + value_delta data_central.append(data_central_value) error.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_jj': {'min': m_jj_min, 'mid': None, 'max': m_jj_max}, 'ystar': {'min': y_min, 'mid': y_central, 'max': y_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_jj': {'min': m_jj_min, 'mid': None, 'max': m_jj_max}, + 'ystar': {'min': y_min, 'mid': y_central, 'max': y_max}, + } kin.append(kin_value) - hepdata_tables="rawdata/atlas_mjj_jet2015_r04_ystar1.yaml" + hepdata_tables = "rawdata/atlas_mjj_jet2015_r04_ystar1.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) error_definition = {} - error_definition['stat'] = {'description': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} + error_definition['stat'] = { + 'description': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } for i in range(1, len(input['dependent_variables'][0]['values'][0]['errors'])): error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] error_definition[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} @@ -68,12 +81,13 @@ def processData(): uncertainties_yaml = {'definitions': error_definition, 'bins': error} with open('data.yaml', 'w') as file: - yaml.dump(data_central_yaml, file, sort_keys=False) + yaml.dump(data_central_yaml, file, sort_keys=False) with open('kinematics.yaml', 'w') as file: - yaml.dump(kinematics_yaml, file, sort_keys=False) + yaml.dump(kinematics_yaml, file, sort_keys=False) with open('uncertainties.yaml', 'w') as file: yaml.dump(uncertainties_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter.py index c3c361cc25..c5a61262d2 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter.py @@ -5,16 +5,15 @@ @author: Mark N. Costantini """ -import yaml +from filter_utils import decompose_covmat, fill_df, range_str_to_floats import numpy as np import pandas as pd from scipy.linalg import block_diag +import yaml from validphys.covmats import dataset_inputs_covmat_from_systematics from validphys.loader import Loader -from filter_utils import range_str_to_floats, decompose_covmat, fill_df - def filter_ATLAS_2JET_7TEV_R06_data_kinetic(): """ @@ -106,7 +105,7 @@ def filter_ATLAS_2JET_7TEV_R06_uncertainties(scenario='nominal'): # Construct Covariance matrix for Systematics Asys = pd.concat([df.drop(['lum'], axis=1) for df in dfs], axis=0).to_numpy() Csys = np.einsum('ij,kj->ik', Asys, Asys) - + # Construct Special Sys (Lum) Cov matrix Alum = pd.concat([df[['lum']] for df in dfs], axis=0).to_numpy() Clum = np.einsum('ij,kj->ik', Alum, Alum) @@ -122,7 +121,7 @@ def filter_ATLAS_2JET_7TEV_R06_uncertainties(scenario='nominal'): BD_stat = block_diag(BD_stat, stat) # covariance matrix without the special systematics, that is, ATLASLUMI11 - covmat_no_lum = BD_stat #Csys + BD_stat + covmat_no_lum = BD_stat # Csys + BD_stat # generate artificial systematics A_art_sys = decompose_covmat(covmat=covmat_no_lum) @@ -137,7 +136,7 @@ def filter_ATLAS_2JET_7TEV_R06_uncertainties(scenario='nominal'): for i in range(1, A_art_sys.shape[0] + 1) } - for i in range(1, Asys.shape[1]+1): + for i in range(1, Asys.shape[1] + 1): error_definition[f"sys_{i}"] = { "description": f"sys {i}", "treatment": "MULT", diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter_bugged.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter_bugged.py index 3a9eec47a5..deab139713 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter_bugged.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter_bugged.py @@ -1,21 +1,24 @@ -from validphys.loader import Loader import yaml +from validphys.loader import Loader SETNAME = "ATLAS_2JET_7TEV_R06" + def filter_ATLAS_2JET_7TEV_R06_uncertainties_bugged(): """ - read systematics from old CommonData format - write to uncertainties_bugged.yaml the old bugged systematics - + This reproduces the same covariance matrix as the old CommonData. """ l = Loader() cd = l.check_commondata(setname=SETNAME).load_commondata_instance() - - additive_sys = cd.commondata_table.drop(['process','kin1','kin2','kin3','data', 'stat'],axis=1)['ADD'].to_numpy() + + additive_sys = cd.commondata_table.drop( + ['process', 'kin1', 'kin2', 'kin3', 'data', 'stat'], axis=1 + )['ADD'].to_numpy() systype = cd.systype_table # error definition @@ -25,7 +28,6 @@ def filter_ATLAS_2JET_7TEV_R06_uncertainties_bugged(): "description": f"sys_{index}", "treatment": row['type'], "type": row['name'], - } # store error in dict diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter_utils.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter_utils.py index fd921e04db..994f430138 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter_utils.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_7TEV_R06/filter_utils.py @@ -1,10 +1,10 @@ -import yaml -import numpy as np -import pandas as pd - # ignore pandas warning import warnings +import numpy as np +import pandas as pd +import yaml + warnings.filterwarnings('ignore') SCENARIO = {'nominal': 0, 'stronger': 1, 'weaker': 2} diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_HADR_DIF/utils.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_HADR_DIF/utils.py index 4fac235055..dac59de537 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_HADR_DIF/utils.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_HADR_DIF/utils.py @@ -14,11 +14,12 @@ @author: Tanishq Sharma """ -import numpy as np - from math import sqrt + +import numpy as np from numpy.linalg import eig + def symmetrize_errors(delta_plus, delta_minus): r"""Compute the symmterized uncertainty and the shift in data point. @@ -28,7 +29,7 @@ def symmetrize_errors(delta_plus, delta_minus): The top/plus uncertainty with sign delta_minus : float The bottom/minus uncertainty with sign - + Returns ------- se_delta : float @@ -37,23 +38,24 @@ def symmetrize_errors(delta_plus, delta_minus): The symmetrized uncertainty to be used in commondata """ - semi_diff = (delta_plus + delta_minus)/2 - average = (delta_plus - delta_minus)/2 + semi_diff = (delta_plus + delta_minus) / 2 + average = (delta_plus - delta_minus) / 2 se_delta = semi_diff - se_sigma = sqrt(average*average + 2*semi_diff*semi_diff) + se_sigma = sqrt(average * average + 2 * semi_diff * semi_diff) return se_delta, se_sigma + def percentage_to_absolute(percentage, value): r"""Compute the absolute value of uncertainty from percentage. Parameters ---------- percentage : string/float - Experimental datasets can provide the percentage - uncertainties with a % sign or without one. + Experimental datasets can provide the percentage + uncertainties with a % sign or without one. The function will autostrip % sign and convert to - a float type in case the percentage uncertainty - comes with a % sign. Else, it will directly perform + a float type in case the percentage uncertainty + comes with a % sign. Else, it will directly perform the computation. value : float The data point @@ -62,16 +64,17 @@ def percentage_to_absolute(percentage, value): ------- absolute : float The absolute value of the uncertainty - + """ if type(percentage) is str: percentage = float(percentage.replace("%", "")) absolute = percentage * value * 0.01 - return absolute + return absolute else: absolute = percentage * value * 0.01 return absolute + def cormat_to_covmat(err_list, cormat_list): r"""Convert correlation matrix elements to covariance matrix elements. @@ -82,9 +85,9 @@ def cormat_to_covmat(err_list, cormat_list): A one dimensional list which contains the uncertainty associated to each data point in order. cormat_list : list - A one dimensional list which contains the elements of + A one dimensional list which contains the elements of the correlation matrix row by row. Since experimental - datasets provide these matrices in a list form, this + datasets provide these matrices in a list form, this simplifies the implementation for the user. Returns @@ -92,7 +95,7 @@ def cormat_to_covmat(err_list, cormat_list): covmat_list : list A one dimensional list which contains the elements of the covariance matrix row by row. - + """ covmat_list = [] for i in range(len(cormat_list)): @@ -101,8 +104,9 @@ def cormat_to_covmat(err_list, cormat_list): covmat_list.append(cormat_list[i] * err_list[a] * err_list[b]) return covmat_list + def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): - r"""Convert the covariance matrix to a matrix of + r"""Convert the covariance matrix to a matrix of artificial uncertainties. Parameters @@ -112,7 +116,7 @@ def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): covmat_list : list A one dimensional list which contains the elements of the covariance matrix row by row. Since experimental - datasets provide these matrices in a list form, this + datasets provide these matrices in a list form, this simplifies the implementation for the user. no_of_norm_mat : int Normalized covariance matrices may have an eigenvalue @@ -122,19 +126,19 @@ def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): in an instance. For example, if a single covariance matrix of a normalized distribution is being processed, the input would be 1. If a covariance matrix contains pertains to - 3 normalized datasets (i.e. cross covmat for 3 + 3 normalized datasets (i.e. cross covmat for 3 distributions), the input would be 3. The default value is - 0 for when the covariance matrix pertains to an absolute + 0 for when the covariance matrix pertains to an absolute distribution. Returns ------- artunc : list A two dimensional matrix (given as a list of lists) - which contains artificial uncertainties to be added - to the commondata. i^th row (or list) contains the + which contains artificial uncertainties to be added + to the commondata. i^th row (or list) contains the artificial uncertainties of the i^th data point. - + """ epsilon = -0.0000000001 neg_eval_count = 0 @@ -163,14 +167,15 @@ def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): if eigval[j] < 0: continue else: - artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) return artunc.tolist() + def cross_cormat_to_covmat(row_err_list, col_err_list, cormat_list): - r"""Convert cross correlation matrix elements - (i.e. those between different different variables or + r"""Convert cross correlation matrix elements + (i.e. those between different different variables or observables) to covariance matrix elements. - + Parameters ---------- row_err_list : list @@ -182,17 +187,17 @@ def cross_cormat_to_covmat(row_err_list, col_err_list, cormat_list): associated to each data point of the variable that is given on the horizontal axis. cormat_list : list - A one dimensional list which contains the elements of + A one dimensional list which contains the elements of the correlation matrix row by row. Since experimental - datasets provide these matrices in a list form, this + datasets provide these matrices in a list form, this simplifies the implementation for the user. - + Returns ------- covmat_list : list A one dimensional list which contains the elements of the covariance matrix row by row. - + """ covmat_list = [] for i in range(len(cormat_list)): @@ -201,17 +206,18 @@ def cross_cormat_to_covmat(row_err_list, col_err_list, cormat_list): covmat_list.append(cormat_list[i] * row_err_list[a] * col_err_list[b]) return covmat_list + def matlist_to_matrix(rows, columns, mat_list): r"""Convert a 1d list to a 2d matrix. Note: This utils function is not strictly needed for data implementation, however, it is provided for the aid of the user due to how matrices are treated - throughout all the other functions. This function + throughout all the other functions. This function allows the user to convert a list that contains the elemnets of matrix row by row to a proper matrix, if need be for any reason. - + Parameters ---------- rows : int @@ -226,7 +232,7 @@ def matlist_to_matrix(rows, columns, mat_list): ------- matrix : numpy.ndarray The matrix as a numpy 2d array. - + """ if rows * columns == len(mat_list): matrix = np.zeros((rows, columns)) @@ -237,7 +243,8 @@ def matlist_to_matrix(rows, columns, mat_list): return matrix else: raise Exception('rows * columns != len(mat_list)') - + + def concat_matrices(rows, columns, list_of_matrices): r"""Join smaller matrices into a large matrix. @@ -250,22 +257,22 @@ def concat_matrices(rows, columns, list_of_matrices): Parameters ---------- rows : int - No. of rows of matrices to be concatenated. E.g., if 6 + No. of rows of matrices to be concatenated. E.g., if 6 matrices: A, B, C, D, E, F need to be joined as [[A, B, C], [D, E, F]], the number of rows would be 2. columns : int - No. of columns of matrices to be concatenated. In the + No. of columns of matrices to be concatenated. In the above example, this would be 3. list_of_matrices : list - A list of the matrices that have to concatenated row by + A list of the matrices that have to concatenated row by row. In the above example, this would be [A, B, C, D, E, F]. - The matrices themselves need to be provided as a list of lists, + The matrices themselves need to be provided as a list of lists, or a numpy 2d array. If the user has the matrix in a 1d row by - row form, use matList_to_matrix() to convert it. It is assumed - the user verifies that all the input matrices have the correct - dimensions. Matrices with incompatible dimensions will lead to + row form, use matList_to_matrix() to convert it. It is assumed + the user verifies that all the input matrices have the correct + dimensions. Matrices with incompatible dimensions will lead to undesired behavior. Returns @@ -273,7 +280,7 @@ def concat_matrices(rows, columns, list_of_matrices): final_mat_list : list A one dimensional list which contains the elements of the final, fully concatenated matrix row by row. - + """ for i in range(len(list_of_matrices)): list_of_matrices[i] = np.array(list_of_matrices[i]) @@ -290,20 +297,21 @@ def concat_matrices(rows, columns, list_of_matrices): final_mat_list.append(final_mat[i][j]) return final_mat_list + def trimat_to_fullmat(mode, tri_mat_list): r"""Convert a list of values of a triangular matrix to a symmetric matrix. - Experimental datasets can provide the entries of + Experimental datasets can provide the entries of correlation or covariance matrices as a triangular - matrix, as these matrices are symmetric by their + matrix, as these matrices are symmetric by their very nature. This function can convert these list to a complete symmetric matrix, that can be used for the dataset implementation. mode : bool Enter 0 or 1 based on the following scenarios: - Use mode 0 if matrix entries are given row by + Use mode 0 if matrix entries are given row by row such as: 0 1 2 3 4 5 6 @@ -316,28 +324,28 @@ def trimat_to_fullmat(mode, tri_mat_list): 5 8 9 Please note that the numbers above (0-9) are not - entries of the matrix but rather the index of the - entries of the list which contains the elements of + entries of the matrix but rather the index of the + entries of the list which contains the elements of the triangular matrix. tri_mat_list : list A list containing the elements of the triangular matrix, - for example, for a 4*4 matrix, the list of - triangular matrix entries could be: - [a, b, c, d, e, f, g, h, i, j] + for example, for a 4*4 matrix, the list of + triangular matrix entries could be: + [a, b, c, d, e, f, g, h, i, j] Returns ------- mat_list : list A one dimensional list which contains the elements of - the fully populated, symmetric matrix row by row. - + the fully populated, symmetric matrix row by row. + """ - dim = int((np.sqrt(1 + 8*len(tri_mat_list)) - 1)/2) + dim = int((np.sqrt(1 + 8 * len(tri_mat_list)) - 1) / 2) matrix = np.zeros((dim, dim)) if mode == 0: for i in range(dim): for j in range(i + 1): - list_el = len(tri_mat_list) - 1 - ((i*(i + 1))//2 + j) + list_el = len(tri_mat_list) - 1 - ((i * (i + 1)) // 2 + j) if i == j: matrix[dim - 1 - i][dim - 1 - j] = tri_mat_list[list_el] else: @@ -346,7 +354,7 @@ def trimat_to_fullmat(mode, tri_mat_list): elif mode == 1: for i in range(dim): for j in range(i + 1): - list_el = (i*(i + 1))//2 + j + list_el = (i * (i + 1)) // 2 + j if i == j: matrix[i][j] = tri_mat_list[list_el] else: @@ -358,4 +366,4 @@ def trimat_to_fullmat(mode, tri_mat_list): for i in range(dim): for j in range(dim): mat_list.append(matrix[i][j]) - return mat_list \ No newline at end of file + return mat_list diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py index 71ded50a8a..37b78e0988 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py @@ -1,7 +1,9 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se + def processData(): with open('metadata.yaml', 'r') as file: @@ -50,15 +52,15 @@ def processData(): covMatArray_dSig_dyttBar = [] covMatArray_dSig_dyttBar_norm = [] -# dSig_dmttBar data - hepdata_tables="rawdata/Table618.yaml" + # dSig_dmttBar data + hepdata_tables = "rawdata/Table618.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - - covariance_matrix="rawdata/Table619.yaml" + + covariance_matrix = "rawdata/Table619.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dmttBar*ndata_dSig_dmttBar): + for i in range(ndata_dSig_dmttBar * ndata_dSig_dmttBar): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dmttBar.append(covMatEl) artUncMat_dSig_dmttBar = cta(ndata_dSig_dmttBar, covMatArray_dSig_dmttBar) @@ -81,12 +83,16 @@ def processData(): # value_delta = value_delta + se_delta # error_label = values[i]['errors'][j]['label'] # error_value[error_label] = se_sigma - data_central_value = values[i]['value'] # + value_delta + data_central_value = values[i]['value'] # + value_delta data_central_dSig_dmttBar.append(data_central_value) for j in range(ndata_dSig_dmttBar): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dmttBar[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dmttBar[i][j]) error_dSig_dmttBar.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + } kin_dSig_dmttBar.append(kin_value) error_definition_dSig_dmttBar = {} @@ -95,30 +101,37 @@ def processData(): # error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] # error_definition_dSig_dmttBar[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dmttBar): - error_definition_dSig_dmttBar['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dmttBar['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dmttBar_yaml = {'data_central': data_central_dSig_dmttBar} kinematics_dSig_dmttBar_yaml = {'bins': kin_dSig_dmttBar} - uncertainties_dSig_dmttBar_yaml = {'definitions': error_definition_dSig_dmttBar, 'bins': error_dSig_dmttBar} + uncertainties_dSig_dmttBar_yaml = { + 'definitions': error_definition_dSig_dmttBar, + 'bins': error_dSig_dmttBar, + } with open('data_dSig_dmttBar.yaml', 'w') as file: - yaml.dump(data_central_dSig_dmttBar_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dmttBar_yaml, file, sort_keys=False) with open('kinematics_dSig_dmttBar.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dmttBar_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dmttBar_yaml, file, sort_keys=False) with open('uncertainties_dSig_dmttBar.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dmttBar_yaml, file, sort_keys=False) -# dSig_dmttBar_norm data - hepdata_tables="rawdata/Table616.yaml" + # dSig_dmttBar_norm data + hepdata_tables = "rawdata/Table616.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table617.yaml" + covariance_matrix = "rawdata/Table617.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dmttBar_norm*ndata_dSig_dmttBar_norm): + for i in range(ndata_dSig_dmttBar_norm * ndata_dSig_dmttBar_norm): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dmttBar_norm.append(covMatEl) artUncMat_dSig_dmttBar_norm = cta(ndata_dSig_dmttBar_norm, covMatArray_dSig_dmttBar_norm) @@ -141,12 +154,16 @@ def processData(): # value_delta = value_delta + se_delta # error_label = values[i]['errors'][j]['label'] # error_value[error_label] = se_sigma - data_central_value = values[i]['value'] # + value_delta + data_central_value = values[i]['value'] # + value_delta data_central_dSig_dmttBar_norm.append(data_central_value) for j in range(ndata_dSig_dmttBar_norm): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dmttBar_norm[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dmttBar_norm[i][j]) error_dSig_dmttBar_norm.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + } kin_dSig_dmttBar_norm.append(kin_value) error_definition_dSig_dmttBar_norm = {} @@ -155,30 +172,37 @@ def processData(): # error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] # error_definition_dSig_dmttBar_norm[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dmttBar_norm): - error_definition_dSig_dmttBar_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dmttBar_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dmttBar_norm_yaml = {'data_central': data_central_dSig_dmttBar_norm} kinematics_dSig_dmttBar_norm_yaml = {'bins': kin_dSig_dmttBar_norm} - uncertainties_dSig_dmttBar_norm_yaml = {'definitions': error_definition_dSig_dmttBar_norm, 'bins': error_dSig_dmttBar_norm} + uncertainties_dSig_dmttBar_norm_yaml = { + 'definitions': error_definition_dSig_dmttBar_norm, + 'bins': error_dSig_dmttBar_norm, + } with open('data_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dmttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dmttBar_norm_yaml, file, sort_keys=False) -# dSig_dpTt data - hepdata_tables="rawdata/Table610.yaml" + # dSig_dpTt data + hepdata_tables = "rawdata/Table610.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table611.yaml" + covariance_matrix = "rawdata/Table611.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dpTt*ndata_dSig_dpTt): + for i in range(ndata_dSig_dpTt * ndata_dSig_dpTt): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dpTt.append(covMatEl) artUncMat_dSig_dpTt = cta(ndata_dSig_dpTt, covMatArray_dSig_dpTt) @@ -201,12 +225,16 @@ def processData(): # value_delta = value_delta + se_delta # error_label = values[i]['errors'][j]['label'] # error_value[error_label] = se_sigma - data_central_value = values[i]['value'] # + value_delta + data_central_value = values[i]['value'] # + value_delta data_central_dSig_dpTt.append(data_central_value) for j in range(ndata_dSig_dpTt): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dpTt[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dpTt[i][j]) error_dSig_dpTt.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}, + } kin_dSig_dpTt.append(kin_value) error_definition_dSig_dpTt = {} @@ -215,30 +243,37 @@ def processData(): # error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] # error_definition_dSig_dpTt[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dpTt): - error_definition_dSig_dpTt['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dpTt['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dpTt_yaml = {'data_central': data_central_dSig_dpTt} kinematics_dSig_dpTt_yaml = {'bins': kin_dSig_dpTt} - uncertainties_dSig_dpTt_yaml = {'definitions': error_definition_dSig_dpTt, 'bins': error_dSig_dpTt} + uncertainties_dSig_dpTt_yaml = { + 'definitions': error_definition_dSig_dpTt, + 'bins': error_dSig_dpTt, + } with open('data_dSig_dpTt.yaml', 'w') as file: - yaml.dump(data_central_dSig_dpTt_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dpTt_yaml, file, sort_keys=False) with open('kinematics_dSig_dpTt.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dpTt_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dpTt_yaml, file, sort_keys=False) with open('uncertainties_dSig_dpTt.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dpTt_yaml, file, sort_keys=False) -# dSig_dpTt_norm data - hepdata_tables="rawdata/Table608.yaml" + # dSig_dpTt_norm data + hepdata_tables = "rawdata/Table608.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table609.yaml" + covariance_matrix = "rawdata/Table609.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dpTt_norm*ndata_dSig_dpTt_norm): + for i in range(ndata_dSig_dpTt_norm * ndata_dSig_dpTt_norm): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dpTt_norm.append(covMatEl) artUncMat_dSig_dpTt_norm = cta(ndata_dSig_dpTt_norm, covMatArray_dSig_dpTt_norm) @@ -261,12 +296,16 @@ def processData(): # value_delta = value_delta + se_delta # error_label = values[i]['errors'][j]['label'] # error_value[error_label] = se_sigma - data_central_value = values[i]['value'] # + value_delta + data_central_value = values[i]['value'] # + value_delta data_central_dSig_dpTt_norm.append(data_central_value) for j in range(ndata_dSig_dpTt_norm): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dpTt_norm[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dpTt_norm[i][j]) error_dSig_dpTt_norm.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}, + } kin_dSig_dpTt_norm.append(kin_value) error_definition_dSig_dpTt_norm = {} @@ -275,30 +314,37 @@ def processData(): # error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] # error_definition_dSig_dpTt_norm[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dpTt_norm): - error_definition_dSig_dpTt_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dpTt_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dpTt_norm_yaml = {'data_central': data_central_dSig_dpTt_norm} kinematics_dSig_dpTt_norm_yaml = {'bins': kin_dSig_dpTt_norm} - uncertainties_dSig_dpTt_norm_yaml = {'definitions': error_definition_dSig_dpTt_norm, 'bins': error_dSig_dpTt_norm} + uncertainties_dSig_dpTt_norm_yaml = { + 'definitions': error_definition_dSig_dpTt_norm, + 'bins': error_dSig_dpTt_norm, + } with open('data_dSig_dpTt_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dpTt_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dpTt_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dpTt_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dpTt_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dpTt_norm_yaml, file, sort_keys=False) -# dSig_dyt data - hepdata_tables="rawdata/Table614.yaml" + # dSig_dyt data + hepdata_tables = "rawdata/Table614.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table615.yaml" + covariance_matrix = "rawdata/Table615.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dyt*ndata_dSig_dyt): + for i in range(ndata_dSig_dyt * ndata_dSig_dyt): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dyt.append(covMatEl) artUncMat_dSig_dyt = cta(ndata_dSig_dyt, covMatArray_dSig_dyt) @@ -321,12 +367,16 @@ def processData(): # value_delta = value_delta + se_delta # error_label = values[i]['errors'][j]['label'] # error_value[error_label] = se_sigma - data_central_value = values[i]['value'] # + value_delta + data_central_value = values[i]['value'] # + value_delta data_central_dSig_dyt.append(data_central_value) for j in range(ndata_dSig_dyt): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dyt[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dyt[i][j]) error_dSig_dyt.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, + } kin_dSig_dyt.append(kin_value) error_definition_dSig_dyt = {} @@ -335,30 +385,34 @@ def processData(): # error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] # error_definition_dSig_dyt[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dyt): - error_definition_dSig_dyt['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyt['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyt_yaml = {'data_central': data_central_dSig_dyt} kinematics_dSig_dyt_yaml = {'bins': kin_dSig_dyt} uncertainties_dSig_dyt_yaml = {'definitions': error_definition_dSig_dyt, 'bins': error_dSig_dyt} with open('data_dSig_dyt.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyt_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyt_yaml, file, sort_keys=False) with open('kinematics_dSig_dyt.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyt_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyt_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyt.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyt_yaml, file, sort_keys=False) -# dSig_dyt_norm data - hepdata_tables="rawdata/Table612.yaml" + # dSig_dyt_norm data + hepdata_tables = "rawdata/Table612.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table613.yaml" + covariance_matrix = "rawdata/Table613.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dyt_norm*ndata_dSig_dyt_norm): + for i in range(ndata_dSig_dyt_norm * ndata_dSig_dyt_norm): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dyt_norm.append(covMatEl) artUncMat_dSig_dyt_norm = cta(ndata_dSig_dyt_norm, covMatArray_dSig_dyt_norm) @@ -381,12 +435,16 @@ def processData(): # value_delta = value_delta + se_delta # error_label = values[i]['errors'][j]['label'] # error_value[error_label] = se_sigma - data_central_value = values[i]['value'] # + value_delta + data_central_value = values[i]['value'] # + value_delta data_central_dSig_dyt_norm.append(data_central_value) for j in range(ndata_dSig_dyt_norm): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dyt_norm[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dyt_norm[i][j]) error_dSig_dyt_norm.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, + } kin_dSig_dyt_norm.append(kin_value) error_definition_dSig_dyt_norm = {} @@ -395,30 +453,37 @@ def processData(): # error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] # error_definition_dSig_dyt_norm[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dyt_norm): - error_definition_dSig_dyt_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyt_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyt_norm_yaml = {'data_central': data_central_dSig_dyt_norm} kinematics_dSig_dyt_norm_yaml = {'bins': kin_dSig_dyt_norm} - uncertainties_dSig_dyt_norm_yaml = {'definitions': error_definition_dSig_dyt_norm, 'bins': error_dSig_dyt_norm} + uncertainties_dSig_dyt_norm_yaml = { + 'definitions': error_definition_dSig_dyt_norm, + 'bins': error_dSig_dyt_norm, + } with open('data_dSig_dyt_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyt_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyt_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dyt_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyt_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyt_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyt_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyt_norm_yaml, file, sort_keys=False) -# dSig_dyttBar data - hepdata_tables="rawdata/Table626.yaml" + # dSig_dyttBar data + hepdata_tables = "rawdata/Table626.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table627.yaml" + covariance_matrix = "rawdata/Table627.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dyttBar*ndata_dSig_dyttBar): + for i in range(ndata_dSig_dyttBar * ndata_dSig_dyttBar): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dyttBar.append(covMatEl) artUncMat_dSig_dyttBar = cta(ndata_dSig_dyttBar, covMatArray_dSig_dyttBar) @@ -441,12 +506,16 @@ def processData(): # value_delta = value_delta + se_delta # error_label = values[i]['errors'][j]['label'] # error_value[error_label] = se_sigma - data_central_value = values[i]['value'] # + value_delta + data_central_value = values[i]['value'] # + value_delta data_central_dSig_dyttBar.append(data_central_value) for j in range(ndata_dSig_dyttBar): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dyttBar[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dyttBar[i][j]) error_dSig_dyttBar.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } kin_dSig_dyttBar.append(kin_value) error_definition_dSig_dyttBar = {} @@ -455,30 +524,37 @@ def processData(): # error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] # error_definition_dSig_dyttBar[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dyttBar): - error_definition_dSig_dyttBar['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyttBar['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyttBar_yaml = {'data_central': data_central_dSig_dyttBar} kinematics_dSig_dyttBar_yaml = {'bins': kin_dSig_dyttBar} - uncertainties_dSig_dyttBar_yaml = {'definitions': error_definition_dSig_dyttBar, 'bins': error_dSig_dyttBar} + uncertainties_dSig_dyttBar_yaml = { + 'definitions': error_definition_dSig_dyttBar, + 'bins': error_dSig_dyttBar, + } with open('data_dSig_dyttBar.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyttBar_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyttBar_yaml, file, sort_keys=False) with open('kinematics_dSig_dyttBar.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyttBar_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyttBar_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyttBar.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_yaml, file, sort_keys=False) -# dSig_dyttBar_norm data - hepdata_tables="rawdata/Table624.yaml" + # dSig_dyttBar_norm data + hepdata_tables = "rawdata/Table624.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table625.yaml" + covariance_matrix = "rawdata/Table625.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dyttBar_norm*ndata_dSig_dyttBar_norm): + for i in range(ndata_dSig_dyttBar_norm * ndata_dSig_dyttBar_norm): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dyttBar_norm.append(covMatEl) artUncMat_dSig_dyttBar_norm = cta(ndata_dSig_dyttBar_norm, covMatArray_dSig_dyttBar_norm) @@ -501,12 +577,16 @@ def processData(): # value_delta = value_delta + se_delta # error_label = values[i]['errors'][j]['label'] # error_value[error_label] = se_sigma - data_central_value = values[i]['value'] # + value_delta + data_central_value = values[i]['value'] # + value_delta data_central_dSig_dyttBar_norm.append(data_central_value) for j in range(ndata_dSig_dyttBar_norm): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dyttBar_norm[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dyttBar_norm[i][j]) error_dSig_dyttBar_norm.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } kin_dSig_dyttBar_norm.append(kin_value) error_definition_dSig_dyttBar_norm = {} @@ -515,19 +595,27 @@ def processData(): # error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] # error_definition_dSig_dyttBar_norm[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dyttBar_norm): - error_definition_dSig_dyttBar_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyttBar_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyttBar_norm_yaml = {'data_central': data_central_dSig_dyttBar_norm} kinematics_dSig_dyttBar_norm_yaml = {'bins': kin_dSig_dyttBar_norm} - uncertainties_dSig_dyttBar_norm_yaml = {'definitions': error_definition_dSig_dyttBar_norm, 'bins': error_dSig_dyttBar_norm} + uncertainties_dSig_dyttBar_norm_yaml = { + 'definitions': error_definition_dSig_dyttBar_norm, + 'bins': error_dSig_dyttBar_norm, + } with open('data_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_norm_yaml, file, sort_keys=False) - + + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py index a10f4cf044..1458e3cd54 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_2L_DIF/filter.py @@ -1,6 +1,8 @@ import yaml + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -23,21 +25,21 @@ def processData(): covMatArray_dSig_dyttBar = [] covMatArray_dSig_dyttBar_norm = [] -# dSig_dmttBar data + # dSig_dmttBar data - hepdata_tables="rawdata/Table_10.yaml" + hepdata_tables = "rawdata/Table_10.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table_22.yaml" + covariance_matrix = "rawdata/Table_22.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) - + sqrts = float(input['dependent_variables'][0]['qualifiers'][1]['value']) m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] - - for i in range(len(values)*len(values)): + + for i in range(len(values) * len(values)): covMatArray_dSig_dmttBar.append(input2['dependent_variables'][0]['values'][i]['value']) artUnc_dSig_dmttBar = cta(len(values), covMatArray_dSig_dmttBar) @@ -46,46 +48,59 @@ def processData(): m_ttBar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(len(values)): - error_value['ArtUnc_'+str(j+1)] = artUnc_dSig_dmttBar[i][j] + error_value['ArtUnc_' + str(j + 1)] = artUnc_dSig_dmttBar[i][j] data_central_value = values[i]['value'] data_central_dSig_dmttBar.append(data_central_value) error_dSig_dmttBar.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + } kin_dSig_dmttBar.append(kin_value) error_definition_dSig_dmttBar = {} for i in range(len(values)): - error_definition_dSig_dmttBar['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dmttBar['ArtUnc_' + str(i + 1)] = { + 'description': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dmttBar_yaml = {'data_central': data_central_dSig_dmttBar} kinematics_dSig_dmttBar_yaml = {'bins': kin_dSig_dmttBar} - uncertainties_dSig_dmttBar_yaml = {'definitions': error_definition_dSig_dmttBar, 'bins': error_dSig_dmttBar} + uncertainties_dSig_dmttBar_yaml = { + 'definitions': error_definition_dSig_dmttBar, + 'bins': error_dSig_dmttBar, + } with open('data_dSig_dmttBar.yaml', 'w') as file: - yaml.dump(data_central_dSig_dmttBar_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dmttBar_yaml, file, sort_keys=False) with open('kinematics_dSig_dmttBar.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dmttBar_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dmttBar_yaml, file, sort_keys=False) with open('uncertainties_dSig_dmttBar.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dmttBar_yaml, file, sort_keys=False) -# dSig_dmttBar_norm data + # dSig_dmttBar_norm data - hepdata_tables="rawdata/Table_4.yaml" + hepdata_tables = "rawdata/Table_4.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table_16.yaml" + covariance_matrix = "rawdata/Table_16.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) - + sqrts = float(input['dependent_variables'][0]['qualifiers'][1]['value']) m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] - - for i in range(len(values)*len(values)): - covMatArray_dSig_dmttBar_norm.append(input2['dependent_variables'][0]['values'][i]['value']*1e-6) + + for i in range(len(values) * len(values)): + covMatArray_dSig_dmttBar_norm.append( + input2['dependent_variables'][0]['values'][i]['value'] * 1e-6 + ) artUnc_dSig_dmttBar_norm = cta(len(values), covMatArray_dSig_dmttBar_norm, 1) for i in range(len(values)): @@ -93,45 +108,56 @@ def processData(): m_ttBar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(len(values)): - error_value['ArtUnc_'+str(j+1)] = artUnc_dSig_dmttBar_norm[i][j] - data_central_value = values[i]['value']*1e-3 + error_value['ArtUnc_' + str(j + 1)] = artUnc_dSig_dmttBar_norm[i][j] + data_central_value = values[i]['value'] * 1e-3 data_central_dSig_dmttBar_norm.append(data_central_value) error_dSig_dmttBar_norm.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + } kin_dSig_dmttBar_norm.append(kin_value) error_definition_dSig_dmttBar_norm = {} for i in range(len(values)): - error_definition_dSig_dmttBar_norm['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dmttBar_norm['ArtUnc_' + str(i + 1)] = { + 'description': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dmttBar_norm_yaml = {'data_central': data_central_dSig_dmttBar_norm} kinematics_dSig_dmttBar_norm_yaml = {'bins': kin_dSig_dmttBar_norm} - uncertainties_dSig_dmttBar_norm_yaml = {'definitions': error_definition_dSig_dmttBar_norm, 'bins': error_dSig_dmttBar_norm} + uncertainties_dSig_dmttBar_norm_yaml = { + 'definitions': error_definition_dSig_dmttBar_norm, + 'bins': error_dSig_dmttBar_norm, + } with open('data_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dmttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dmttBar_norm_yaml, file, sort_keys=False) -# dSig_dyttBar data + # dSig_dyttBar data - hepdata_tables="rawdata/Table_12.yaml" + hepdata_tables = "rawdata/Table_12.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table_24.yaml" + covariance_matrix = "rawdata/Table_24.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) - + sqrts = float(input['dependent_variables'][0]['qualifiers'][1]['value']) m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] - - for i in range(len(values)*len(values)): + + for i in range(len(values) * len(values)): covMatArray_dSig_dyttBar.append(input2['dependent_variables'][0]['values'][i]['value']) artUnc_dSig_dyttBar = cta(len(values), covMatArray_dSig_dyttBar) @@ -140,45 +166,56 @@ def processData(): y_ttBar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(len(values)): - error_value['ArtUnc_'+str(j+1)] = artUnc_dSig_dyttBar[i][j] + error_value['ArtUnc_' + str(j + 1)] = artUnc_dSig_dyttBar[i][j] data_central_value = values[i]['value'] data_central_dSig_dyttBar.append(data_central_value) error_dSig_dyttBar.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } kin_dSig_dyttBar.append(kin_value) error_definition_dSig_dyttBar = {} for i in range(len(values)): - error_definition_dSig_dyttBar['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyttBar['ArtUnc_' + str(i + 1)] = { + 'description': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyttBar_yaml = {'data_central': data_central_dSig_dyttBar} kinematics_dSig_dyttBar_yaml = {'bins': kin_dSig_dyttBar} - uncertainties_dSig_dyttBar_yaml = {'definitions': error_definition_dSig_dyttBar, 'bins': error_dSig_dyttBar} + uncertainties_dSig_dyttBar_yaml = { + 'definitions': error_definition_dSig_dyttBar, + 'bins': error_dSig_dyttBar, + } with open('data_dSig_dyttBar.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyttBar_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyttBar_yaml, file, sort_keys=False) with open('kinematics_dSig_dyttBar.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyttBar_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyttBar_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyttBar.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_yaml, file, sort_keys=False) -# dSig_dyttBar_norm data + # dSig_dyttBar_norm data - hepdata_tables="rawdata/Table_6.yaml" + hepdata_tables = "rawdata/Table_6.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table_18.yaml" + covariance_matrix = "rawdata/Table_18.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) - + sqrts = float(input['dependent_variables'][0]['qualifiers'][1]['value']) m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] - - for i in range(len(values)*len(values)): + + for i in range(len(values) * len(values)): covMatArray_dSig_dyttBar_norm.append(input2['dependent_variables'][0]['values'][i]['value']) artUnc_dSig_dyttBar_norm = cta(len(values), covMatArray_dSig_dyttBar_norm, 1) @@ -187,28 +224,40 @@ def processData(): y_ttBar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(len(values)): - error_value['ArtUnc_'+str(j+1)] = artUnc_dSig_dyttBar_norm[i][j] + error_value['ArtUnc_' + str(j + 1)] = artUnc_dSig_dyttBar_norm[i][j] data_central_value = values[i]['value'] data_central_dSig_dyttBar_norm.append(data_central_value) error_dSig_dyttBar_norm.append(error_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } kin_dSig_dyttBar_norm.append(kin_value) error_definition_dSig_dyttBar_norm = {} for i in range(len(values)): - error_definition_dSig_dyttBar_norm['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyttBar_norm['ArtUnc_' + str(i + 1)] = { + 'description': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyttBar_norm_yaml = {'data_central': data_central_dSig_dyttBar_norm} kinematics_dSig_dyttBar_norm_yaml = {'bins': kin_dSig_dyttBar_norm} - uncertainties_dSig_dyttBar_norm_yaml = {'definitions': error_definition_dSig_dyttBar_norm, 'bins': error_dSig_dyttBar_norm} + uncertainties_dSig_dyttBar_norm_yaml = { + 'definitions': error_definition_dSig_dyttBar_norm, + 'bins': error_dSig_dyttBar_norm, + } with open('data_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_norm_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py index 9beff3dd11..a079949fa8 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py @@ -1,13 +1,13 @@ -import yaml - import numpy as np from numpy.linalg import eig -# use #1693 +import yaml + +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import concat_matrices as cm from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import concat_matrices as cm from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import matlist_to_matrix as mtm +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta + def artunc(): statArr = [] diff --git a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py index b3c39246d5..b114623b64 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_8TEV_LJ_DIF/filter.py @@ -1,12 +1,13 @@ +from pathlib import Path +import re + import artunc import yaml -import re -from pathlib import Path -# use #1693 from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -38,12 +39,12 @@ def processData(): artUnc = artunc.artunc() artUnc_norm = artunc.artunc_norm() -# dSig_dmttBar + # dSig_dmttBar - hepdata_tables='rawdata/Table_23.yaml' + hepdata_tables = 'rawdata/Table_23.yaml' with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + sqrts = 8000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -54,48 +55,89 @@ def processData(): m_ttbar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(25): - error_value['ArtUnc_'+str(j+1)] = artUnc[i][j] + error_value['ArtUnc_' + str(j + 1)] = artUnc[i][j] value_delta = 0 - for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors'])-1): + for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors']) - 1): if 'symerror' in input['dependent_variables'][1]['values'][i]['errors'][j]: - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = pta(input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], data_central_value) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], + data_central_value, + ) else: - se_delta, se_sigma = se(pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['plus'], data_central_value), pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['minus'], data_central_value)) - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = se_sigma + se_delta, se_sigma = se( + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'minus' + ], + data_central_value, + ), + ) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = se_sigma value_delta = value_delta + se_delta error_value['lumi'] = pta(values[i]['errors'][2]['symerror'], data_central_value) data_central_value = data_central_value + value_delta - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar': {'min': m_ttbar_min, 'mid': None, 'max': m_ttbar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttbar_min, 'mid': None, 'max': m_ttbar_max}, + } data_central_dSig_dmttBar.append(data_central_value) kin_dSig_dmttBar.append(kin_value) error_dSig_dmttBar.append(error_value) error_definition_dSig_dmttBar = {} for i in range(25): - error_definition_dSig_dmttBar['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'ATLAS8TEVTTB151104716unc'+str(i+1)} - for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors'])-1): - error_definition_dSig_dmttBar[input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "")] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} - error_definition_dSig_dmttBar['lumi'] = {'definition': 'luminosity uncertainty', 'treatment': 'MULT', 'type': 'ATLASLUMI8'} + error_definition_dSig_dmttBar['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'ATLAS8TEVTTB151104716unc' + str(i + 1), + } + for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors']) - 1): + error_definition_dSig_dmttBar[ + input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "") + ] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dmttBar['lumi'] = { + 'definition': 'luminosity uncertainty', + 'treatment': 'MULT', + 'type': 'ATLASLUMI8', + } data_central_dSig_dmttBar_yaml = {'data_central': data_central_dSig_dmttBar} kinematics_dSig_dmttBar_yaml = {'bins': kin_dSig_dmttBar} - uncertainties_dSig_dmttBar_yaml = {'definitions': error_definition_dSig_dmttBar, 'bins': error_dSig_dmttBar} + uncertainties_dSig_dmttBar_yaml = { + 'definitions': error_definition_dSig_dmttBar, + 'bins': error_dSig_dmttBar, + } with open('data_dSig_dmttBar.yaml', 'w') as file: - yaml.dump(data_central_dSig_dmttBar_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dmttBar_yaml, file, sort_keys=False) with open('kinematics_dSig_dmttBar.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dmttBar_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dmttBar_yaml, file, sort_keys=False) with open('uncertainties_dSig_dmttBar.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dmttBar_yaml, file, sort_keys=False) - -# dSig_dmttBar_norm - hepdata_tables='rawdata/Table_24.yaml' + # dSig_dmttBar_norm + + hepdata_tables = 'rawdata/Table_24.yaml' with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + sqrts = 8000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -106,46 +148,83 @@ def processData(): m_ttbar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(25): - error_value['ArtUnc_'+str(j+1)] = artUnc_norm[i][j] + error_value['ArtUnc_' + str(j + 1)] = artUnc_norm[i][j] value_delta = 0 for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors'])): if 'symerror' in input['dependent_variables'][1]['values'][i]['errors'][j]: - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = pta(input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], data_central_value) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], + data_central_value, + ) else: - se_delta, se_sigma = se(pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['plus'], data_central_value), pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['minus'], data_central_value)) - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = se_sigma + se_delta, se_sigma = se( + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'minus' + ], + data_central_value, + ), + ) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = se_sigma value_delta = value_delta + se_delta data_central_value = data_central_value + value_delta - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar': {'min': m_ttbar_min, 'mid': None, 'max': m_ttbar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttbar_min, 'mid': None, 'max': m_ttbar_max}, + } data_central_dSig_dmttBar_norm.append(data_central_value) kin_dSig_dmttBar_norm.append(kin_value) error_dSig_dmttBar_norm.append(error_value) error_definition_dSig_dmttBar_norm = {} for i in range(25): - error_definition_dSig_dmttBar_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'ATLAS8TEVTTB151104716unc'+str(i+1)} + error_definition_dSig_dmttBar_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'ATLAS8TEVTTB151104716unc' + str(i + 1), + } for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors'])): - error_definition_dSig_dmttBar_norm[input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "")] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dmttBar_norm[ + input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "") + ] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} data_central_dSig_dmttBar_norm_yaml = {'data_central': data_central_dSig_dmttBar_norm} kinematics_dSig_dmttBar_norm_yaml = {'bins': kin_dSig_dmttBar_norm} - uncertainties_dSig_dmttBar_norm_yaml = {'definitions': error_definition_dSig_dmttBar_norm, 'bins': error_dSig_dmttBar_norm} + uncertainties_dSig_dmttBar_norm_yaml = { + 'definitions': error_definition_dSig_dmttBar_norm, + 'bins': error_dSig_dmttBar_norm, + } with open('data_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dmttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dmttBar_norm_yaml, file, sort_keys=False) -# dSig_dpTt + # dSig_dpTt - hepdata_tables='rawdata/Table_29.yaml' + hepdata_tables = 'rawdata/Table_29.yaml' with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + sqrts = 8000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -156,48 +235,89 @@ def processData(): pT_t_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(25): - error_value['ArtUnc_'+str(j+1)] = artUnc[i+7][j] + error_value['ArtUnc_' + str(j + 1)] = artUnc[i + 7][j] value_delta = 0 - for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors'])-1): + for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors']) - 1): if 'symerror' in input['dependent_variables'][1]['values'][i]['errors'][j]: - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = pta(input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], data_central_value) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], + data_central_value, + ) else: - se_delta, se_sigma = se(pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['plus'], data_central_value), pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['minus'], data_central_value)) - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = se_sigma + se_delta, se_sigma = se( + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'minus' + ], + data_central_value, + ), + ) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = se_sigma value_delta = value_delta + se_delta error_value['lumi'] = pta(values[i]['errors'][2]['symerror'], data_central_value) data_central_value = data_central_value + value_delta - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}, + } data_central_dSig_dpTt.append(data_central_value) kin_dSig_dpTt.append(kin_value) error_dSig_dpTt.append(error_value) error_definition_dSig_dpTt = {} for i in range(25): - error_definition_dSig_dpTt['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'ATLAS8TEVTTB151104716unc'+str(i+1)} - for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors'])-1): - error_definition_dSig_dpTt[input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "")] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} - error_definition_dSig_dpTt['lumi'] = {'definition': 'luminosity uncertainty', 'treatment': 'MULT', 'type': 'ATLASLUMI8'} + error_definition_dSig_dpTt['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'ATLAS8TEVTTB151104716unc' + str(i + 1), + } + for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors']) - 1): + error_definition_dSig_dpTt[ + input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "") + ] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dpTt['lumi'] = { + 'definition': 'luminosity uncertainty', + 'treatment': 'MULT', + 'type': 'ATLASLUMI8', + } data_central_dSig_dpTt_yaml = {'data_central': data_central_dSig_dpTt} kinematics_dSig_dpTt_yaml = {'bins': kin_dSig_dpTt} - uncertainties_dSig_dpTt_yaml = {'definitions': error_definition_dSig_dpTt, 'bins': error_dSig_dpTt} + uncertainties_dSig_dpTt_yaml = { + 'definitions': error_definition_dSig_dpTt, + 'bins': error_dSig_dpTt, + } with open('data_dSig_dpTt.yaml', 'w') as file: - yaml.dump(data_central_dSig_dpTt_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dpTt_yaml, file, sort_keys=False) with open('kinematics_dSig_dpTt.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dpTt_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dpTt_yaml, file, sort_keys=False) with open('uncertainties_dSig_dpTt.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dpTt_yaml, file, sort_keys=False) -# dSig_dpTt_norm + # dSig_dpTt_norm - hepdata_tables='rawdata/Table_30.yaml' + hepdata_tables = 'rawdata/Table_30.yaml' with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + sqrts = 8000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -208,46 +328,83 @@ def processData(): pT_t_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(25): - error_value['ArtUnc_'+str(j+1)] = artUnc_norm[i+7][j] + error_value['ArtUnc_' + str(j + 1)] = artUnc_norm[i + 7][j] value_delta = 0 for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors'])): if 'symerror' in input['dependent_variables'][1]['values'][i]['errors'][j]: - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = pta(input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], data_central_value) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], + data_central_value, + ) else: - se_delta, se_sigma = se(pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['plus'], data_central_value), pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['minus'], data_central_value)) - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = se_sigma + se_delta, se_sigma = se( + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'minus' + ], + data_central_value, + ), + ) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = se_sigma value_delta = value_delta + se_delta data_central_value = data_central_value + value_delta - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}, + } data_central_dSig_dpTt_norm.append(data_central_value) kin_dSig_dpTt_norm.append(kin_value) error_dSig_dpTt_norm.append(error_value) error_definition_dSig_dpTt_norm = {} for i in range(25): - error_definition_dSig_dpTt_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'ATLAS8TEVTTB151104716unc'+str(i+1)} + error_definition_dSig_dpTt_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'ATLAS8TEVTTB151104716unc' + str(i + 1), + } for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors'])): - error_definition_dSig_dpTt_norm[input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "")] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dpTt_norm[ + input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "") + ] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} data_central_dSig_dpTt_norm_yaml = {'data_central': data_central_dSig_dpTt_norm} kinematics_dSig_dpTt_norm_yaml = {'bins': kin_dSig_dpTt_norm} - uncertainties_dSig_dpTt_norm_yaml = {'definitions': error_definition_dSig_dpTt_norm, 'bins': error_dSig_dpTt_norm} + uncertainties_dSig_dpTt_norm_yaml = { + 'definitions': error_definition_dSig_dpTt_norm, + 'bins': error_dSig_dpTt_norm, + } with open('data_dSig_dpTt_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dpTt_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dpTt_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dpTt_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dpTt_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dpTt_norm_yaml, file, sort_keys=False) -# dSig_dyt + # dSig_dyt - hepdata_tables='rawdata/Table_31.yaml' + hepdata_tables = 'rawdata/Table_31.yaml' with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + sqrts = 8000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -258,48 +415,86 @@ def processData(): y_t_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(25): - error_value['ArtUnc_'+str(j+1)] = artUnc[i+15][j] + error_value['ArtUnc_' + str(j + 1)] = artUnc[i + 15][j] value_delta = 0 - for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors'])-1): + for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors']) - 1): if 'symerror' in input['dependent_variables'][1]['values'][i]['errors'][j]: - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = pta(input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], data_central_value) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], + data_central_value, + ) else: - se_delta, se_sigma = se(pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['plus'], data_central_value), pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['minus'], data_central_value)) - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = se_sigma + se_delta, se_sigma = se( + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'minus' + ], + data_central_value, + ), + ) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = se_sigma value_delta = value_delta + se_delta error_value['lumi'] = pta(values[i]['errors'][2]['symerror'], data_central_value) data_central_value = data_central_value + value_delta - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, + } data_central_dSig_dyt.append(data_central_value) kin_dSig_dyt.append(kin_value) error_dSig_dyt.append(error_value) error_definition_dSig_dyt = {} for i in range(25): - error_definition_dSig_dyt['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'ATLAS8TEVTTB151104716unc'+str(i+1)} - for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors'])-1): - error_definition_dSig_dyt[input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "")] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} - error_definition_dSig_dyt['lumi'] = {'definition': 'luminosity uncertainty', 'treatment': 'MULT', 'type': 'ATLASLUMI8'} + error_definition_dSig_dyt['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'ATLAS8TEVTTB151104716unc' + str(i + 1), + } + for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors']) - 1): + error_definition_dSig_dyt[ + input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "") + ] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dyt['lumi'] = { + 'definition': 'luminosity uncertainty', + 'treatment': 'MULT', + 'type': 'ATLASLUMI8', + } data_central_dSig_dyt_yaml = {'data_central': data_central_dSig_dyt} kinematics_dSig_dyt_yaml = {'bins': kin_dSig_dyt} uncertainties_dSig_dyt_yaml = {'definitions': error_definition_dSig_dyt, 'bins': error_dSig_dyt} with open('data_dSig_dyt.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyt_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyt_yaml, file, sort_keys=False) with open('kinematics_dSig_dyt.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyt_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyt_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyt.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyt_yaml, file, sort_keys=False) -# dSig_dyt_norm + # dSig_dyt_norm - hepdata_tables='rawdata/Table_32.yaml' + hepdata_tables = 'rawdata/Table_32.yaml' with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + sqrts = 8000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -310,46 +505,83 @@ def processData(): y_t_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(25): - error_value['ArtUnc_'+str(j+1)] = artUnc_norm[i+15][j] + error_value['ArtUnc_' + str(j + 1)] = artUnc_norm[i + 15][j] value_delta = 0 for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors'])): if 'symerror' in input['dependent_variables'][1]['values'][i]['errors'][j]: - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = pta(input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], data_central_value) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], + data_central_value, + ) else: - se_delta, se_sigma = se(pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['plus'], data_central_value), pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['minus'], data_central_value)) - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = se_sigma + se_delta, se_sigma = se( + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'minus' + ], + data_central_value, + ), + ) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = se_sigma value_delta = value_delta + se_delta data_central_value = data_central_value + value_delta - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, + } data_central_dSig_dyt_norm.append(data_central_value) kin_dSig_dyt_norm.append(kin_value) error_dSig_dyt_norm.append(error_value) error_definition_dSig_dyt_norm = {} for i in range(25): - error_definition_dSig_dyt_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'ATLAS8TEVTTB151104716unc'+str(i+1)} + error_definition_dSig_dyt_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'ATLAS8TEVTTB151104716unc' + str(i + 1), + } for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors'])): - error_definition_dSig_dyt_norm[input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "")] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dyt_norm[ + input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "") + ] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} data_central_dSig_dyt_norm_yaml = {'data_central': data_central_dSig_dyt_norm} kinematics_dSig_dyt_norm_yaml = {'bins': kin_dSig_dyt_norm} - uncertainties_dSig_dyt_norm_yaml = {'definitions': error_definition_dSig_dyt_norm, 'bins': error_dSig_dyt_norm} + uncertainties_dSig_dyt_norm_yaml = { + 'definitions': error_definition_dSig_dyt_norm, + 'bins': error_dSig_dyt_norm, + } with open('data_dSig_dyt_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyt_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyt_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dyt_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyt_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyt_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyt_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyt_norm_yaml, file, sort_keys=False) -# dSig_dyttBar + # dSig_dyttBar - hepdata_tables='rawdata/Table_27.yaml' + hepdata_tables = 'rawdata/Table_27.yaml' with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + sqrts = 8000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -360,48 +592,89 @@ def processData(): y_ttBar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(25): - error_value['ArtUnc_'+str(j+1)] = artUnc[i+20][j] + error_value['ArtUnc_' + str(j + 1)] = artUnc[i + 20][j] value_delta = 0 - for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors'])-1): + for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors']) - 1): if 'symerror' in input['dependent_variables'][1]['values'][i]['errors'][j]: - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = pta(input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], data_central_value) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], + data_central_value, + ) else: - se_delta, se_sigma = se(pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['plus'], data_central_value), pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['minus'], data_central_value)) - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = se_sigma + se_delta, se_sigma = se( + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'minus' + ], + data_central_value, + ), + ) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = se_sigma value_delta = value_delta + se_delta error_value['lumi'] = pta(values[i]['errors'][2]['symerror'], data_central_value) data_central_value = data_central_value + value_delta - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } data_central_dSig_dyttBar.append(data_central_value) kin_dSig_dyttBar.append(kin_value) error_dSig_dyttBar.append(error_value) error_definition_dSig_dyttBar = {} for i in range(25): - error_definition_dSig_dyttBar['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'ATLAS8TEVTTB151104716unc'+str(i+1)} - for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors'])-1): - error_definition_dSig_dyttBar[input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "")] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} - error_definition_dSig_dyttBar['lumi'] = {'definition': 'luminosity uncertainty', 'treatment': 'MULT', 'type': 'ATLASLUMI8'} + error_definition_dSig_dyttBar['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'ATLAS8TEVTTB151104716unc' + str(i + 1), + } + for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors']) - 1): + error_definition_dSig_dyttBar[ + input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "") + ] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dyttBar['lumi'] = { + 'definition': 'luminosity uncertainty', + 'treatment': 'MULT', + 'type': 'ATLASLUMI8', + } data_central_dSig_dyttBar_yaml = {'data_central': data_central_dSig_dyttBar} kinematics_dSig_dyttBar_yaml = {'bins': kin_dSig_dyttBar} - uncertainties_dSig_dyttBar_yaml = {'definitions': error_definition_dSig_dyttBar, 'bins': error_dSig_dyttBar} + uncertainties_dSig_dyttBar_yaml = { + 'definitions': error_definition_dSig_dyttBar, + 'bins': error_dSig_dyttBar, + } with open('data_dSig_dyttBar.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyttBar_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyttBar_yaml, file, sort_keys=False) with open('kinematics_dSig_dyttBar.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyttBar_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyttBar_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyttBar.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_yaml, file, sort_keys=False) -# dSig_dyttBar_norm + # dSig_dyttBar_norm - hepdata_tables='rawdata/Table_28.yaml' + hepdata_tables = 'rawdata/Table_28.yaml' with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + sqrts = 8000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -412,40 +685,78 @@ def processData(): y_ttBar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(25): - error_value['ArtUnc_'+str(j+1)] = artUnc_norm[i+20][j] + error_value['ArtUnc_' + str(j + 1)] = artUnc_norm[i + 20][j] value_delta = 0 for j in range(1, len(input['dependent_variables'][1]['values'][i]['errors'])): if 'symerror' in input['dependent_variables'][1]['values'][i]['errors'][j]: - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = pta(input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], data_central_value) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['symerror'], + data_central_value, + ) else: - se_delta, se_sigma = se(pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['plus'], data_central_value), pta(input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror']['minus'], data_central_value)) - error_value[input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace(" ", "")] = se_sigma + se_delta, se_sigma = se( + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + pta( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'minus' + ], + data_central_value, + ), + ) + error_value[ + input['dependent_variables'][1]['values'][i]['errors'][j]['label'].replace( + " ", "" + ) + ] = se_sigma value_delta = value_delta + se_delta data_central_value = data_central_value + value_delta - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } data_central_dSig_dyttBar_norm.append(data_central_value) kin_dSig_dyttBar_norm.append(kin_value) error_dSig_dyttBar_norm.append(error_value) error_definition_dSig_dyttBar_norm = {} for i in range(25): - error_definition_dSig_dyttBar_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'ATLAS8TEVTTB151104716unc'+str(i+1)} + error_definition_dSig_dyttBar_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'ATLAS8TEVTTB151104716unc' + str(i + 1), + } for i in range(1, len(input['dependent_variables'][1]['values'][0]['errors'])): - error_definition_dSig_dyttBar_norm[input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "")] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dyttBar_norm[ + input['dependent_variables'][1]['values'][0]['errors'][i]['label'].replace(" ", "") + ] = {'definition': '', 'treatment': 'MULT', 'type': 'CORR'} data_central_dSig_dyttBar_norm_yaml = {'data_central': data_central_dSig_dyttBar_norm} kinematics_dSig_dyttBar_norm_yaml = {'bins': kin_dSig_dyttBar_norm} - uncertainties_dSig_dyttBar_norm_yaml = {'definitions': error_definition_dSig_dyttBar_norm, 'bins': error_dSig_dyttBar_norm} + uncertainties_dSig_dyttBar_norm_yaml = { + 'definitions': error_definition_dSig_dyttBar_norm, + 'bins': error_dSig_dyttBar_norm, + } with open('data_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_norm_yaml, file, sort_keys=False) + def remove_commas(): pattern = "uncertainties*.yaml" reg = re.compile(fr'({"sys,"})') @@ -453,5 +764,6 @@ def remove_commas(): new_text = reg.sub("syst_", file.read_text()) file.write_text(new_text) + processData() -remove_commas() \ No newline at end of file +remove_commas() diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_13TEV_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_13TEV_DIF/filter.py index 6460122da3..86bf9333aa 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_13TEV_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_13TEV_DIF/filter.py @@ -1,5 +1,6 @@ import yaml + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -14,7 +15,7 @@ def processData(): kin_r07 = [] error_r07 = [] -# r04 data + # r04 data for i in tables_r04: if i == 1: @@ -29,10 +30,10 @@ def processData(): elif i == 4: y_min = 1.5 y_max = 2 - hepdata_tables="rawdata/ak4_xsec_ybin"+str(i)+".yaml" + hepdata_tables = "rawdata/ak4_xsec_ybin" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + values = input['dependent_variables'][0]['values'] sqrts = 13000 @@ -41,7 +42,11 @@ def processData(): data_central_r04.append(data_central_value) pT_min = input['independent_variables'][0]['values'][j]['low'] pT_max = input['independent_variables'][0]['values'][j]['high'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, 'y': {'min': y_min, 'mid': None, 'max': y_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + 'y': {'min': y_min, 'mid': None, 'max': y_max}, + } kin_r04.append(kin_value) error_value = {} error_value['all uncorr. unc.'] = values[j]['errors'][0]['symerror'] @@ -50,29 +55,33 @@ def processData(): error_value[error_label] = values[j]['errors'][k]['symerror'] error_r04.append(error_value) - hepdata_tables="rawdata/ak4_xsec_ybin1.yaml" + hepdata_tables = "rawdata/ak4_xsec_ybin1.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) error_definition_r04 = {} - error_definition_r04['all uncorr. unc.'] = {'description': 'all uncorrelated uncertainties', 'treatment': 'ADD', 'type': 'UNCORR'} + error_definition_r04['all uncorr. unc.'] = { + 'description': 'all uncorrelated uncertainties', + 'treatment': 'ADD', + 'type': 'UNCORR', + } for i in range(1, len(input['dependent_variables'][0]['values'][0]['errors'])): error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] error_definition_r04[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} - + data_central_r04_yaml = {'data_central': data_central_r04} kinematics_r04_yaml = {'bins': kin_r04} uncertainties_r04_yaml = {'definitions': error_definition_r04, 'bins': error_r04} with open('data_r04.yaml', 'w') as file: - yaml.dump(data_central_r04_yaml, file, sort_keys=False) + yaml.dump(data_central_r04_yaml, file, sort_keys=False) with open('kinematics_r04.yaml', 'w') as file: - yaml.dump(kinematics_r04_yaml, file, sort_keys=False) + yaml.dump(kinematics_r04_yaml, file, sort_keys=False) with open('uncertainties_r04.yaml', 'w') as file: yaml.dump(uncertainties_r04_yaml, file, sort_keys=False) -# r07 data + # r07 data for i in tables_r07: if i == 1: @@ -87,10 +96,10 @@ def processData(): elif i == 4: y_min = 1.5 y_max = 2 - hepdata_tables="rawdata/ak7_xsec_ybin"+str(i)+".yaml" + hepdata_tables = "rawdata/ak7_xsec_ybin" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + values = input['dependent_variables'][0]['values'] sqrts = 13000 @@ -99,7 +108,11 @@ def processData(): data_central_r07.append(data_central_value) pT_min = input['independent_variables'][0]['values'][j]['low'] pT_max = input['independent_variables'][0]['values'][j]['high'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, 'y': {'min': y_min, 'mid': None, 'max': y_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + 'y': {'min': y_min, 'mid': None, 'max': y_max}, + } kin_r07.append(kin_value) error_value = {} error_value['all uncorr. unc.'] = values[j]['errors'][0]['symerror'] @@ -108,26 +121,31 @@ def processData(): error_value[error_label] = values[j]['errors'][k]['symerror'] error_r07.append(error_value) - hepdata_tables="rawdata/ak7_xsec_ybin1.yaml" + hepdata_tables = "rawdata/ak7_xsec_ybin1.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) error_definition_r07 = {} - error_definition_r07['all uncorr. unc.'] = {'description': 'all uncorrelated uncertainties', 'treatment': 'ADD', 'type': 'UNCORR'} + error_definition_r07['all uncorr. unc.'] = { + 'description': 'all uncorrelated uncertainties', + 'treatment': 'ADD', + 'type': 'UNCORR', + } for i in range(1, len(input['dependent_variables'][0]['values'][0]['errors'])): error_name = input['dependent_variables'][0]['values'][0]['errors'][i]['label'] error_definition_r07[error_name] = {'description': '', 'treatment': 'MULT', 'type': 'CORR'} - + data_central_r07_yaml = {'data_central': data_central_r07} kinematics_r07_yaml = {'bins': kin_r07} uncertainties_r07_yaml = {'definitions': error_definition_r07, 'bins': error_r07} with open('data_r07.yaml', 'w') as file: - yaml.dump(data_central_r07_yaml, file, sort_keys=False) + yaml.dump(data_central_r07_yaml, file, sort_keys=False) with open('kinematics_r07.yaml', 'w') as file: - yaml.dump(kinematics_r07_yaml, file, sort_keys=False) + yaml.dump(kinematics_r07_yaml, file, sort_keys=False) with open('uncertainties_r07.yaml', 'w') as file: yaml.dump(uncertainties_r07_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter.py index 1f5a03db50..231133fba1 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter.py @@ -6,19 +6,18 @@ @author: Mark N. Costantini """ -import yaml -import numpy as np - from filter_utils import ( + block_diagonal_corr, + correlation_to_covariance, + decompose_covmat, get_data_values, get_kinematics, get_stat_uncertainties, - block_diagonal_corr, - correlation_to_covariance, - uncertainties_df, process_err, - decompose_covmat, + uncertainties_df, ) +import numpy as np +import yaml def filter_CMS_1JET_8TEV_data_kinetic(): @@ -67,14 +66,12 @@ def filter_CMS_1JET_8TEV_uncertainties(): stat_unc = get_stat_uncertainties() # df_unc['ignore'].values # stat_unc = df_unc['stat'].values * df_unc['Sigma'].values / 100 - bd_stat_cov = correlation_to_covariance( - block_diagonal_corr(tables), stat_unc - ) + bd_stat_cov = correlation_to_covariance(block_diagonal_corr(tables), stat_unc) # bd_stat_cov = np.diag(stat_unc**2) # generate artificial systematics by decomposing statistical covariance matrix A_art_stat = decompose_covmat(bd_stat_cov) - A_art_stat = np.nan_to_num(A_art_stat) # set nan to zero + A_art_stat = np.nan_to_num(A_art_stat) # set nan to zero # Luminosity uncertainty lum_unc = df_unc['Luminosity'].values * df_unc['Sigma'].values / 100 @@ -114,9 +111,8 @@ def filter_CMS_1JET_8TEV_uncertainties(): # cov_np = np.einsum('i,j->ij', np_p, np_p) # np_m = df_unc['Sigma'].values * (df_unc['NPCorr'].values * (1. + df_unc['npcorerr-'].values / 100.) - 1) / np.sqrt(2.) # cov_np += np.einsum('i,j->ij', np_m, np_m) - + covmat = cov_JES + cov_unfold + bd_stat_cov + lumi_cov + cov_uncorr - # save systematics to yaml file @@ -156,23 +152,22 @@ def filter_CMS_1JET_8TEV_uncertainties(): "type": "CORR", } - # store error in dict error = [] for n in range(A_art_stat.shape[0]): - error_value={} - + error_value = {} + # artificial stat uncertainties for m in range(A_art_stat.shape[1]): - error_value[f"art_sys_{m+1}"] = float(A_art_stat[n,m]) - + error_value[f"art_sys_{m+1}"] = float(A_art_stat[n, m]) + # unfolding uncertainties for col, m in zip(df_unfold.columns, range(df_unfold.to_numpy().shape[1])): - error_value[f"{col}"] = float(df_unfold.to_numpy()[n,m]) + error_value[f"{col}"] = float(df_unfold.to_numpy()[n, m]) # JES uncertainties for col, m in zip(df_JES.columns, range(df_JES.to_numpy().shape[1])): - error_value[f"{col}"] = float(df_JES.to_numpy()[n,m]) + error_value[f"{col}"] = float(df_JES.to_numpy()[n, m]) # luminosity uncertainties error_value["luminosity_uncertainty"] = float(lum_unc[n]) @@ -184,8 +179,8 @@ def filter_CMS_1JET_8TEV_uncertainties(): uncertainties_yaml = {"definitions": error_definition, "bins": error} - with open(f"uncertainties.yaml",'w') as file: - yaml.dump(uncertainties_yaml,file, sort_keys=False) + with open(f"uncertainties.yaml", 'w') as file: + yaml.dump(uncertainties_yaml, file, sort_keys=False) return covmat @@ -207,31 +202,43 @@ def filter_CMS_1JET_8TEV_uncertainties(): cmat = API.dataset_inputs_covmat_from_systematics(**inp) import matplotlib.pyplot as plt - import seaborn as sns - fig, axs = plt.subplots(1,2, figsize = (12,5), sharey = True) + import seaborn as sns + + fig, axs = plt.subplots(1, 2, figsize=(12, 5), sharey=True) # Create a shared colorbar axis cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7]) - sns.heatmap(covmat / np.outer(np.sqrt(np.diag(covmat)), np.sqrt(np.diag(covmat))), annot=False, cmap="YlGnBu", ax=axs[0], cbar_ax=cbar_ax) - sns.heatmap(cmat / np.outer(np.sqrt(np.diag(cmat)), np.sqrt(np.diag(cmat))), annot=False, cmap="YlGnBu", ax=axs[1], cbar_ax=cbar_ax) + sns.heatmap( + covmat / np.outer(np.sqrt(np.diag(covmat)), np.sqrt(np.diag(covmat))), + annot=False, + cmap="YlGnBu", + ax=axs[0], + cbar_ax=cbar_ax, + ) + sns.heatmap( + cmat / np.outer(np.sqrt(np.diag(cmat)), np.sqrt(np.diag(cmat))), + annot=False, + cmap="YlGnBu", + ax=axs[1], + cbar_ax=cbar_ax, + ) plt.show() - ones = covmat / cmat print(ones) print() print(np.diag(ones)) print() print(np.allclose(np.ones(covmat.shape), ones, rtol=1e-5)) - print(np.max(covmat-cmat), np.min(covmat-cmat)) + print(np.max(covmat - cmat), np.min(covmat - cmat)) # print(np.argmax(covmat-cmat, axis=1), np.argmax(covmat-cmat, axis=0), np.argmin(covmat-cmat)) - max_index = np.argmax(covmat-cmat) + max_index = np.argmax(covmat - cmat) print("Index of the largest entry:", max_index) # Get the row and column indices of the largest entry - max_row_index, max_col_index = np.unravel_index(max_index, (covmat-cmat).shape) + max_row_index, max_col_index = np.unravel_index(max_index, (covmat - cmat).shape) print("Row index of the largest entry:", max_row_index) print("Column index of the largest entry:", max_col_index) print((covmat.flatten()[max_index]), covmat[max_row_index, max_col_index]) diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter_bugged.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter_bugged.py index 9709efbe77..17f543e503 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter_bugged.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter_bugged.py @@ -1,21 +1,24 @@ -from validphys.loader import Loader import yaml +from validphys.loader import Loader SETNAME = "CMS_1JET_8TEV" + def filter_CMS_1JET_8TEV_uncertainties_bugged(): """ - read systematics from old CommonData format - write to uncertainties_bugged.yaml the old bugged systematics - + This reproduces the same covariance matrix as the old CommonData. """ l = Loader() cd = l.check_commondata(setname=SETNAME).load_commondata_instance() - - additive_sys = cd.commondata_table.drop(['process','kin1','kin2','kin3','data', 'stat'],axis=1)['ADD'].to_numpy() + + additive_sys = cd.commondata_table.drop( + ['process', 'kin1', 'kin2', 'kin3', 'data', 'stat'], axis=1 + )['ADD'].to_numpy() systype = cd.systype_table # error definition @@ -25,7 +28,6 @@ def filter_CMS_1JET_8TEV_uncertainties_bugged(): "description": f"sys_{index}", "treatment": row['type'], "type": row['name'], - } # store error in dict diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter_utils.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter_utils.py index bc065a32da..e5eabdcf48 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter_utils.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_1JET_8TEV/filter_utils.py @@ -2,12 +2,12 @@ Utils to be used in CMS_1JET_8TEV/filter.py """ -import yaml -import numpy as np import logging -from scipy.linalg import block_diag -import pandas as pd +import numpy as np +import pandas as pd +from scipy.linalg import block_diag +import yaml log = logging.getLogger(__name__) @@ -120,10 +120,9 @@ def get_data_values(tables, version): values = input['dependent_variables'][0]['values'] pt_values = input['independent_variables'][0]['values'] - + for pt, value in zip(pt_values, values): data_central.append(value['value']) - return data_central @@ -169,21 +168,20 @@ def get_kinematics(tables, version): jet_kt_bins = input['independent_variables'][0]['values'] KT = {} for kt in jet_kt_bins: - + KT['min'], KT['max'] = kt['low'], kt['high'] KT['mid'] = float(f"{0.5 * (kt['low'] + kt['high']):.3f}") kin_value = { 'y': {'min': rap['min'], 'mid': rap['mid'], 'max': rap['max']}, - 'p_T2': {'min': KT['min']**2, 'mid': KT['mid']**2, 'max': KT['max']**2}, + 'p_T2': {'min': KT['min'] ** 2, 'mid': KT['mid'] ** 2, 'max': KT['max'] ** 2}, 'sqrt_s': {'min': None, 'mid': sqrts, 'max': None}, } kin.append(kin_value) - return kin - + def get_stat_correlations(table): """ @@ -204,22 +202,22 @@ def get_stat_correlations(table): """ with open(f'rawdata/CMS_8TeV_jets_Ybin{table}___CMS_8TeV_jets_Ybin{table}.dat', 'r') as file: card = file.readlines() - + # get shape of matrix - shape_mat = TABLE_DATA_SHAPE[table] - + shape_mat = TABLE_DATA_SHAPE[table] + stat_corr = np.zeros((shape_mat, shape_mat)) - + # correlation rows always start at row 18 for j in range(shape_mat): # fill rows of correlation matrix stat_corr[j, :] = np.array( [card[(17 + shape_mat * j) + k].split()[-1] for k in range(shape_mat)] ) - - # add zeros for points in the pt<74 kinematic region + + # add zeros for points in the pt<74 kinematic region # these points should be cut (it is always 9 pt bins in the pt < 74 region) - stat_corr = block_diag(np.zeros((9,9)), stat_corr) + stat_corr = block_diag(np.zeros((9, 9)), stat_corr) return stat_corr @@ -228,7 +226,7 @@ def block_diagonal_corr(tables): forms block diagonal correlation matrix for stat uncertainties. Each block corresponds to a rapidity bin. - + Parameters ---------- tables : list @@ -242,7 +240,7 @@ def block_diagonal_corr(tables): bd_corr = get_stat_correlations(tables[0]) for table in tables[1:]: - + bd_corr = block_diag(bd_corr, get_stat_correlations(table)) return bd_corr @@ -293,18 +291,19 @@ def get_stat_uncertainties(): hepdata_tables = f"rawdata/HEPData-ins1487277-v{version}-Table_{table}.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + # discard pT < 74 GeV entries - for err, pt in zip(input['dependent_variables'][0]['values'], input['independent_variables'][0]['values']): + for err, pt in zip( + input['dependent_variables'][0]['values'], input['independent_variables'][0]['values'] + ): stat_err.append(err['errors'][0]['symerror']) - + return np.array(stat_err) def get_uncertainties_df(table): - """ - """ + """ """ # read dat file into dataframe by skipping the first 41 metadata rows df = pd.read_csv( @@ -314,8 +313,8 @@ def get_uncertainties_df(table): # reindex df = df.reset_index(drop=True) - df = df[1:-1] # discard last one as it is repeated - + df = df[1:-1] # discard last one as it is repeated + return df @@ -328,32 +327,35 @@ def uncertainties_df(tables): df = pd.concat(dfs, axis=0) return df + def process_err(df): """ - Given the uncertainties dataframe, if the two variations in the pair - (of uncertainties) have the same sign, only the largest (in absolute value) + Given the uncertainties dataframe, if the two variations in the pair + (of uncertainties) have the same sign, only the largest (in absolute value) is retained, while the other is set to zero - + """ - for col_idx in np.arange(0,len(df.columns),2): + for col_idx in np.arange(0, len(df.columns), 2): + + for row_idx, (val1, val2) in enumerate(zip(df.iloc[:, col_idx], df.iloc[:, col_idx + 1])): + if np.sign(val1) == np.sign(val2): - for row_idx, (val1, val2) in enumerate(zip(df.iloc[:,col_idx], df.iloc[:, col_idx+1])): - if np.sign(val1) == np.sign(val2): - if np.abs(val1) > np.abs(val2): - df.iloc[row_idx, col_idx+1] = 0 + df.iloc[row_idx, col_idx + 1] = 0 elif np.abs(val1) < np.abs(val2): df.iloc[row_idx, col_idx] = 0 return df + def decompose_covmat(covmat): - """Given a covmat it return an array sys with shape (ndat,ndat) - giving ndat correlated systematics for each of the ndat point. - The original covmat is obtained by doing sys@sys.T""" + """Given a covmat it return an array sys with shape (ndat,ndat) + giving ndat correlated systematics for each of the ndat point. + The original covmat is obtained by doing sys@sys.T""" + + lamb, mat = np.linalg.eig(covmat) + sys = np.multiply(np.sqrt(lamb), mat) + return sys - lamb, mat = np.linalg.eig(covmat) - sys = np.multiply(np.sqrt(lamb), mat) - return sys if __name__ == "__main__": # print(get_kinematics(tables=[1],version=1)) diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_2JET_7TEV/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_2JET_7TEV/filter.py index 793124564c..0a8a7a0dd1 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_2JET_7TEV/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_2JET_7TEV/filter.py @@ -6,24 +6,23 @@ @author: Mark N. Costantini """ -import yaml -import numpy as np -from scipy.linalg import block_diag -import pandas as pd - from filter_utils import ( + JEC_error_matrix, + bin_by_bin_covmat, correlation_to_covariance, - get_corr_dat_file, - get_stat_uncertainties, dat_file_to_df, + decompose_covmat, + get_corr_dat_file, get_data_values, get_kinematics, + get_stat_uncertainties, lumi_covmat, - JEC_error_matrix, unfolding_error_matrix, - bin_by_bin_covmat, - decompose_covmat, ) +import numpy as np +import pandas as pd +from scipy.linalg import block_diag +import yaml def filter_CMS_2JET_7TEV_data_kinetic(): @@ -198,8 +197,8 @@ def filterCMS_2JET_7TEV_uncertainties(): # only to test covmat = filterCMS_2JET_7TEV_uncertainties() - from validphys.loader import Loader from validphys.covmats import dataset_inputs_covmat_from_systematics + from validphys.loader import Loader setname = "CMS_2JET_7TEV" l = Loader() diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_2JET_7TEV/filter_utils.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_2JET_7TEV/filter_utils.py index 12cdb6ab70..1d83335528 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_2JET_7TEV/filter_utils.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_2JET_7TEV/filter_utils.py @@ -1,22 +1,25 @@ """ Utils to be used in CMS_2JET_7TEV.filter.py """ + +import logging + import numpy as np -import yaml import pandas as pd -import logging +import yaml log = logging.getLogger(__name__) -def get_data_values(tables,version): + +def get_data_values(tables, version): """ - returns the central data - + returns the central data + Parameters ---------- tables : list list that enumerates the table number - + version : int integer read from metadata.yaml that indicated the version of the hepdata @@ -27,17 +30,17 @@ def get_data_values(tables,version): list list containing the central values for all hepdata tables - + """ - + data_central = [] for table in tables: - + hepdata_table = f"rawdata/HEPData-ins1208923-v{version}-Table_{table}.yaml" - + with open(hepdata_table, 'r') as file: input = yaml.safe_load(file) - + values = input['dependent_variables'][0]['values'] for value in values: @@ -45,15 +48,16 @@ def get_data_values(tables,version): return data_central -def get_kinematics(tables,version): + +def get_kinematics(tables, version): """ - returns the relevant kinematics values - + returns the relevant kinematics values + Parameters ---------- tables : list list that enumerates the table number - + version : int integer read from metadata.yaml that indicated the version of the hepdata @@ -68,35 +72,37 @@ def get_kinematics(tables,version): kin = [] for table in tables: - + hepdata_table = f"rawdata/HEPData-ins1208923-v{version}-Table_{table}.yaml" - + with open(hepdata_table, 'r') as file: input = yaml.safe_load(file) - + rapidity_interval = input['dependent_variables'][0]['qualifiers'][0]['value'] ydiff = {} if rapidity_interval == '< 0.5': ydiff['min'], ydiff['max'], ydiff['mid'] = 0.0, 0.5, 0.25 else: ydiff = range_str_to_floats(rapidity_interval) - + sqrts = float(input['dependent_variables'][0]['qualifiers'][2]['value']) - + dijet_mass_bins = input['independent_variables'][0]['values'] - + for m12 in dijet_mass_bins: # kinematics m12['low'], m12['high'] = m12['low'], m12['high'] m12['mid'] = float(f"{0.5 * (m12['low']+m12['high']):.3f}") - kin_value = {'ydiff' : {'min': ydiff['min'], 'mid': ydiff['mid'] , 'max': ydiff['max']}, - 'm12' : {'min': m12['low'], 'mid': m12['mid'] , 'max': m12['high']} , - 'sqrt_s' : {'min': None, 'mid': sqrts , 'max': None}} + kin_value = { + 'ydiff': {'min': ydiff['min'], 'mid': ydiff['mid'], 'max': ydiff['max']}, + 'm12': {'min': m12['low'], 'mid': m12['mid'], 'max': m12['high']}, + 'sqrt_s': {'min': None, 'mid': sqrts, 'max': None}, + } kin.append(kin_value) - + return kin @@ -104,14 +110,14 @@ def correlation_to_covariance(correlation, uncertainties): """ Converts a correlation matrix into a covariance matrix using a list of uncertainties. - + Parameters: ----------- correlation : np.ndarray A square matrix of correlations. uncertainties : np.ndarray A 1D array of uncertainties. - + Returns: -------- np.ndarray @@ -120,14 +126,16 @@ def correlation_to_covariance(correlation, uncertainties): covariance = np.outer(uncertainties, uncertainties) * correlation return covariance + def decompose_covmat(covmat): - """Given a covmat it return an array sys with shape (ndat,ndat) - giving ndat correlated systematics for each of the ndat point. - The original covmat is obtained by doing sys@sys.T""" + """Given a covmat it return an array sys with shape (ndat,ndat) + giving ndat correlated systematics for each of the ndat point. + The original covmat is obtained by doing sys@sys.T""" + + lamb, mat = np.linalg.eig(covmat) + sys = np.multiply(np.sqrt(lamb), mat) + return sys - lamb, mat = np.linalg.eig(covmat) - sys = np.multiply(np.sqrt(lamb), mat) - return sys def range_str_to_floats(str_range): """ @@ -163,40 +171,39 @@ def get_corr_dat_file(filename): with open(filename) as file: lines = file.readlines() - + # store the number of the rows where the correlation matrix # is printed begin_rows = [] end_rows = [] - for i,line in enumerate(lines): + for i, line in enumerate(lines): if "Statistical correlation" in line and begin_rows == []: - begin_rows.append(i+2) + begin_rows.append(i + 2) elif "Statistical correlation" in line: - begin_rows.append(i+2) - end_rows.append(i-2) + begin_rows.append(i + 2) + end_rows.append(i - 2) - elif i == len(lines)-1: + elif i == len(lines) - 1: end_rows.append(i) correlation_matrices = [] - for begin_row, end_row in zip(begin_rows,end_rows): - - size_mat = end_row-begin_row+1 - stat_corr = np.zeros((size_mat,size_mat)) + for begin_row, end_row in zip(begin_rows, end_rows): + + size_mat = end_row - begin_row + 1 + stat_corr = np.zeros((size_mat, size_mat)) i = 0 - for idx in range(begin_row,end_row+1): + for idx in range(begin_row, end_row + 1): # ignore first two columns as these give the bin kin - stat_corr[i] = np.fromstring(lines[idx], sep=' ')[2:] - i+=1 - + stat_corr[i] = np.fromstring(lines[idx], sep=' ')[2:] + i += 1 + correlation_matrices.append(stat_corr) - - return correlation_matrices + return correlation_matrices def get_stat_uncertainties(): @@ -209,15 +216,15 @@ def get_stat_uncertainties(): dict dictionary with keys = number of table value = list of statistical uncertainties - + """ - + with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) version = metadata['hepdata']['version'] - tables = metadata['hepdata']['tables'] - + tables = metadata['hepdata']['tables'] + stat_err = {} for table in tables: @@ -237,7 +244,7 @@ def dat_file_to_df(): """ from dijet_sys.dat table return a pandas DataFrame with index given by Ndata, - columns by the uncertainties and + columns by the uncertainties and np.nan entries Returns @@ -246,7 +253,7 @@ def dat_file_to_df(): list of dataframes """ - + with open("rawdata/dijet_sys.dat", 'r') as file: lines = file.readlines() @@ -254,50 +261,82 @@ def dat_file_to_df(): begin_lines = [] end_lines = [] - for i,line in enumerate(lines): - + for i, line in enumerate(lines): + if "mlo" in line: - begin_lines.append(i+2) + begin_lines.append(i + 2) + + if "The NP correction" in line and len(begin_lines) >= 1: + end_lines.append(i - 1) - if "The NP correction" in line and len(begin_lines)>=1: - end_lines.append(i-1) - end_lines.append(len(lines)) # define dataframe - columns = ["JEC0-","JEC0+","JEC1-","JEC1+","JEC2-","JEC2+","JEC3-","JEC3+","JEC4-","JEC4+", - "JEC5-","JEC5+","JEC6-","JEC6+","JEC7-","JEC7+","JEC8-","JEC8+", - "JEC9-","JEC9+","JEC10-","JEC10+","JEC11-","JEC11+","JEC12-","JEC12+", - "JEC13-","JEC13+","Lumi-","Lumi+", "Unfolding+","Unfolding-","Bin-by-bin+","Bin-by-bin-"] + columns = [ + "JEC0-", + "JEC0+", + "JEC1-", + "JEC1+", + "JEC2-", + "JEC2+", + "JEC3-", + "JEC3+", + "JEC4-", + "JEC4+", + "JEC5-", + "JEC5+", + "JEC6-", + "JEC6+", + "JEC7-", + "JEC7+", + "JEC8-", + "JEC8+", + "JEC9-", + "JEC9+", + "JEC10-", + "JEC10+", + "JEC11-", + "JEC11+", + "JEC12-", + "JEC12+", + "JEC13-", + "JEC13+", + "Lumi-", + "Lumi+", + "Unfolding+", + "Unfolding-", + "Bin-by-bin+", + "Bin-by-bin-", + ] dfs = [] - for beg, end in zip(begin_lines,end_lines): + for beg, end in zip(begin_lines, end_lines): - df = pd.DataFrame(columns=columns,index=range(beg,end)) - j=0 - for i in range(beg,end): + df = pd.DataFrame(columns=columns, index=range(beg, end)) + j = 0 + for i in range(beg, end): # do not consider NP uncertainty col_vals = np.fromstring(lines[i], sep=' ')[5:] df.iloc[j] = col_vals - j+=1 + j += 1 dfs.append(df) - + return dfs def JEC_error_matrix(): """ - Jet Energy Scale (JET): 14 Asymmetric uncertainties correlated across all - rapidity and mass bins (CORR). This uncertainty is not always presented as + Jet Energy Scale (JET): 14 Asymmetric uncertainties correlated across all + rapidity and mass bins (CORR). This uncertainty is not always presented as (left<0 and right>0), e.g. [-delta_left,+delta_right] - Hence the D'Agostini prescription for symmetrising errors + Hence the D'Agostini prescription for symmetrising errors is not valid here because it works with the only case displayed above. - Instead, we use here the experimentalists prescription, where we take every - subpart of the uncertainty left and right as independent source of - uncertainty. This is motivated by taking the average - of the left and right uncertainty, hence the origin of the sqrt(2) + Instead, we use here the experimentalists prescription, where we take every + subpart of the uncertainty left and right as independent source of + uncertainty. This is motivated by taking the average + of the left and right uncertainty, hence the origin of the sqrt(2) that we divide by. @@ -310,30 +349,30 @@ def JEC_error_matrix(): dfs = dat_file_to_df() JEC_err = [] for df in dfs: - JEC_err.append(df.filter(like = "JEC")) - + JEC_err.append(df.filter(like="JEC")) + # divide by sqrt(2) since treating each unc of asymm as independent - jec = pd.concat(JEC_err,axis=0) / np.sqrt(2) - + jec = pd.concat(JEC_err, axis=0) / np.sqrt(2) + # get central value to convert mult error with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) version = metadata['hepdata']['version'] - tables = metadata['hepdata']['tables'] - cv = get_data_values(tables,version) + tables = metadata['hepdata']['tables'] + cv = get_data_values(tables, version) cv = np.array(cv) - + # convert mult error to absolute - jec = jec.multiply(cv,axis = 0) - + jec = jec.multiply(cv, axis=0) + return jec def lumi_covmat(): """ - Luminosity uncertainty: this is a symmetric uncertainty of 2.2% correlated - accross all mass and rapidity bins and all CMS datasets at 7 TeV, hence the + Luminosity uncertainty: this is a symmetric uncertainty of 2.2% correlated + accross all mass and rapidity bins and all CMS datasets at 7 TeV, hence the keyword (CMSLUMI11). NOTE: this function is needed to test only whether the full covmat coincides @@ -343,33 +382,34 @@ def lumi_covmat(): ------- np.array covariance matrix for luminosity uncertainty - + """ dfs = dat_file_to_df() lumi_err = [] for df in dfs: - lumi_err.append(df.filter(like = "Lumi+")) - - lumi = pd.concat(lumi_err,axis=0) + lumi_err.append(df.filter(like="Lumi+")) + + lumi = pd.concat(lumi_err, axis=0) # get central value to convert mult error with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) version = metadata['hepdata']['version'] - tables = metadata['hepdata']['tables'] - cv = get_data_values(tables,version) + tables = metadata['hepdata']['tables'] + cv = get_data_values(tables, version) cv = np.array(cv) # convert mult to abs - lumi = lumi.multiply(cv, axis = 0) + lumi = lumi.multiply(cv, axis=0) lumi = lumi.to_numpy().astype(float) - - return np.einsum('ij,kj->ik',lumi,lumi) + + return np.einsum('ij,kj->ik', lumi, lumi) + def unfolding_error_matrix(): """ - Unfolding uncertainty: this asymmetric is correlated across all rapidity + Unfolding uncertainty: this asymmetric is correlated across all rapidity and mass bins (CORR). @@ -381,27 +421,28 @@ def unfolding_error_matrix(): dfs = dat_file_to_df() unfold_err = [] for df in dfs: - unfold_err.append(df.filter(like = "Unfolding")) - - unfold = pd.concat(unfold_err,axis=0) / np.sqrt(2) + unfold_err.append(df.filter(like="Unfolding")) + + unfold = pd.concat(unfold_err, axis=0) / np.sqrt(2) # get central value to convert mult error with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) version = metadata['hepdata']['version'] - tables = metadata['hepdata']['tables'] - cv = get_data_values(tables,version) + tables = metadata['hepdata']['tables'] + cv = get_data_values(tables, version) cv = np.array(cv) # convert mult to abs - unfold = unfold.multiply(cv, axis = 0) - + unfold = unfold.multiply(cv, axis=0) + return unfold + def bin_by_bin_covmat(): """ - Bin-by-Bin uncertainty: this is a symmetric uncertainty fully uncorrelated + Bin-by-Bin uncertainty: this is a symmetric uncertainty fully uncorrelated accross bins of mass and rapidity (UNCORR) NOTE: this function is needed to test only whether the full covmat coincides @@ -415,24 +456,24 @@ def bin_by_bin_covmat(): dfs = dat_file_to_df() bin_err = [] for df in dfs: - bin_err.append(df.filter(like = "Bin-by-bin-")) # symm so choose only one - - bin = pd.concat(bin_err,axis=0) - + bin_err.append(df.filter(like="Bin-by-bin-")) # symm so choose only one + + bin = pd.concat(bin_err, axis=0) + # get central value to convert mult error with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) version = metadata['hepdata']['version'] - tables = metadata['hepdata']['tables'] - cv = get_data_values(tables,version) + tables = metadata['hepdata']['tables'] + cv = get_data_values(tables, version) cv = np.array(cv) # convert mult to abs - bin = bin.multiply(cv, axis = 0) - + bin = bin.multiply(cv, axis=0) + bin = bin.to_numpy().astype(float) # fully uncorrelated - bin_cov = np.diag(bin.reshape(bin.shape[0])**2) + bin_cov = np.diag(bin.reshape(bin.shape[0]) ** 2) return bin_cov diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py index a46f21ad9a..0d358adae5 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py @@ -2,6 +2,7 @@ from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -40,19 +41,18 @@ def processData(): covmat_dSig_dyttBar = [] covmat_dSig_dyttBar_norm = [] -# dSig_dpTt + # dSig_dpTt - hepdata_tables="rawdata/d01-x01-y01.yaml" + hepdata_tables = "rawdata/d01-x01-y01.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/d01-x01-y01_cov.yaml" + covariance_matrix = "rawdata/d01-x01-y01_cov.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) for i in range(36): covmat_dSig_dpTt.append(input2['dependent_variables'][0]['values'][i]['value']) artunc_dSig_dpTt = cta(6, covmat_dSig_dpTt, 0) - sqrts = 13000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -63,42 +63,52 @@ def processData(): pT_t_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(6): - error_value['ArtUnc_'+str(j+1)] = artunc_dSig_dpTt[i][j] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}} + error_value['ArtUnc_' + str(j + 1)] = artunc_dSig_dpTt[i][j] + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}, + } data_central_dSig_dpTt.append(data_central_value) kin_dSig_dpTt.append(kin_value) error_dSig_dpTt.append(error_value) error_definition_dSig_dpTt = {} for i in range(6): - error_definition_dSig_dpTt['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dpTt['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dpTt_yaml = {'data_central': data_central_dSig_dpTt} kinematics_dSig_dpTt_yaml = {'bins': kin_dSig_dpTt} - uncertainties_dSig_dpTt_yaml = {'definitions': error_definition_dSig_dpTt, 'bins': error_dSig_dpTt} + uncertainties_dSig_dpTt_yaml = { + 'definitions': error_definition_dSig_dpTt, + 'bins': error_dSig_dpTt, + } with open('data_dSig_dpTt.yaml', 'w') as file: - yaml.dump(data_central_dSig_dpTt_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dpTt_yaml, file, sort_keys=False) with open('kinematics_dSig_dpTt.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dpTt_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dpTt_yaml, file, sort_keys=False) with open('uncertainties_dSig_dpTt.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dpTt_yaml, file, sort_keys=False) -# dSig_dpTt_norm + # dSig_dpTt_norm - hepdata_tables="rawdata/d02-x01-y01.yaml" + hepdata_tables = "rawdata/d02-x01-y01.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/d02-x01-y01_cov.yaml" + covariance_matrix = "rawdata/d02-x01-y01_cov.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) for i in range(25): covmat_dSig_dpTt_norm.append(input2['dependent_variables'][0]['values'][i]['value']) artunc_dSig_dpTt_norm = cta(5, covmat_dSig_dpTt_norm, 1) - sqrts = 13000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -109,42 +119,52 @@ def processData(): pT_t_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(5): - error_value['ArtUnc_'+str(j+1)] = artunc_dSig_dpTt_norm[i][j] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}} + error_value['ArtUnc_' + str(j + 1)] = artunc_dSig_dpTt_norm[i][j] + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}, + } data_central_dSig_dpTt_norm.append(data_central_value) kin_dSig_dpTt_norm.append(kin_value) error_dSig_dpTt_norm.append(error_value) error_definition_dSig_dpTt_norm = {} for i in range(5): - error_definition_dSig_dpTt_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dpTt_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dpTt_norm_yaml = {'data_central': data_central_dSig_dpTt_norm} kinematics_dSig_dpTt_norm_yaml = {'bins': kin_dSig_dpTt_norm} - uncertainties_dSig_dpTt_norm_yaml = {'definitions': error_definition_dSig_dpTt_norm, 'bins': error_dSig_dpTt_norm} + uncertainties_dSig_dpTt_norm_yaml = { + 'definitions': error_definition_dSig_dpTt_norm, + 'bins': error_dSig_dpTt_norm, + } with open('data_dSig_dpTt_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dpTt_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dpTt_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dpTt_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dpTt_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dpTt_norm_yaml, file, sort_keys=False) -# dSig_dmttBar + # dSig_dmttBar - hepdata_tables="rawdata/d045-x01-y01.yaml" + hepdata_tables = "rawdata/d045-x01-y01.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/d045-x01-y01_cov.yaml" + covariance_matrix = "rawdata/d045-x01-y01_cov.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) for i in range(49): covmat_dSig_dmttBar.append(input2['dependent_variables'][0]['values'][i]['value']) artunc_dSig_dmttBar = cta(7, covmat_dSig_dmttBar, 0) - sqrts = 13000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -155,42 +175,52 @@ def processData(): m_ttBar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(7): - error_value['ArtUnc_'+str(j+1)] = artunc_dSig_dmttBar[i][j] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}} + error_value['ArtUnc_' + str(j + 1)] = artunc_dSig_dmttBar[i][j] + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + } data_central_dSig_dmttBar.append(data_central_value) kin_dSig_dmttBar.append(kin_value) error_dSig_dmttBar.append(error_value) error_definition_dSig_dmttBar = {} for i in range(7): - error_definition_dSig_dmttBar['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dmttBar['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dmttBar_yaml = {'data_central': data_central_dSig_dmttBar} kinematics_dSig_dmttBar_yaml = {'bins': kin_dSig_dmttBar} - uncertainties_dSig_dmttBar_yaml = {'definitions': error_definition_dSig_dmttBar, 'bins': error_dSig_dmttBar} + uncertainties_dSig_dmttBar_yaml = { + 'definitions': error_definition_dSig_dmttBar, + 'bins': error_dSig_dmttBar, + } with open('data_dSig_dmttBar.yaml', 'w') as file: - yaml.dump(data_central_dSig_dmttBar_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dmttBar_yaml, file, sort_keys=False) with open('kinematics_dSig_dmttBar.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dmttBar_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dmttBar_yaml, file, sort_keys=False) with open('uncertainties_dSig_dmttBar.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dmttBar_yaml, file, sort_keys=False) -# dSig_dmttBar_norm + # dSig_dmttBar_norm - hepdata_tables="rawdata/d046-x01-y01.yaml" + hepdata_tables = "rawdata/d046-x01-y01.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/d046-x01-y01_cov.yaml" + covariance_matrix = "rawdata/d046-x01-y01_cov.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) for i in range(36): covmat_dSig_dmttBar_norm.append(input2['dependent_variables'][0]['values'][i]['value']) artunc_dSig_dmttBar_norm = cta(6, covmat_dSig_dmttBar_norm, 1) - sqrts = 13000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -201,42 +231,52 @@ def processData(): m_ttBar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(6): - error_value['ArtUnc_'+str(j+1)] = artunc_dSig_dmttBar_norm[i][j] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}} + error_value['ArtUnc_' + str(j + 1)] = artunc_dSig_dmttBar_norm[i][j] + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + } data_central_dSig_dmttBar_norm.append(data_central_value) kin_dSig_dmttBar_norm.append(kin_value) error_dSig_dmttBar_norm.append(error_value) error_definition_dSig_dmttBar_norm = {} for i in range(6): - error_definition_dSig_dmttBar_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dmttBar_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dmttBar_norm_yaml = {'data_central': data_central_dSig_dmttBar_norm} kinematics_dSig_dmttBar_norm_yaml = {'bins': kin_dSig_dmttBar_norm} - uncertainties_dSig_dmttBar_norm_yaml = {'definitions': error_definition_dSig_dmttBar_norm, 'bins': error_dSig_dmttBar_norm} + uncertainties_dSig_dmttBar_norm_yaml = { + 'definitions': error_definition_dSig_dmttBar_norm, + 'bins': error_dSig_dmttBar_norm, + } with open('data_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dmttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dmttBar_norm_yaml, file, sort_keys=False) -# dSig_dyt + # dSig_dyt - hepdata_tables="rawdata/d021-x01-y01.yaml" + hepdata_tables = "rawdata/d021-x01-y01.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/d021-x01-y01_cov.yaml" + covariance_matrix = "rawdata/d021-x01-y01_cov.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) for i in range(100): covmat_dSig_dyt.append(input2['dependent_variables'][0]['values'][i]['value']) artunc_dSig_dyt = cta(10, covmat_dSig_dyt, 0) - sqrts = 13000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -247,42 +287,49 @@ def processData(): y_t_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(10): - error_value['ArtUnc_'+str(j+1)] = artunc_dSig_dyt[i][j] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}} + error_value['ArtUnc_' + str(j + 1)] = artunc_dSig_dyt[i][j] + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, + } data_central_dSig_dyt.append(data_central_value) kin_dSig_dyt.append(kin_value) error_dSig_dyt.append(error_value) error_definition_dSig_dyt = {} for i in range(10): - error_definition_dSig_dyt['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyt['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyt_yaml = {'data_central': data_central_dSig_dyt} kinematics_dSig_dyt_yaml = {'bins': kin_dSig_dyt} uncertainties_dSig_dyt_yaml = {'definitions': error_definition_dSig_dyt, 'bins': error_dSig_dyt} with open('data_dSig_dyt.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyt_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyt_yaml, file, sort_keys=False) with open('kinematics_dSig_dyt.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyt_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyt_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyt.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyt_yaml, file, sort_keys=False) -# dSig_dyt_norm + # dSig_dyt_norm - hepdata_tables="rawdata/d022-x01-y01.yaml" + hepdata_tables = "rawdata/d022-x01-y01.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/d022-x01-y01_cov.yaml" + covariance_matrix = "rawdata/d022-x01-y01_cov.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) for i in range(81): covmat_dSig_dyt_norm.append(input2['dependent_variables'][0]['values'][i]['value']) artunc_dSig_dyt_norm = cta(9, covmat_dSig_dyt_norm, 1) - sqrts = 13000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -293,42 +340,52 @@ def processData(): y_t_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(9): - error_value['ArtUnc_'+str(j+1)] = artunc_dSig_dyt_norm[i][j] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}} + error_value['ArtUnc_' + str(j + 1)] = artunc_dSig_dyt_norm[i][j] + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, + } data_central_dSig_dyt_norm.append(data_central_value) kin_dSig_dyt_norm.append(kin_value) error_dSig_dyt_norm.append(error_value) error_definition_dSig_dyt_norm = {} for i in range(9): - error_definition_dSig_dyt_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyt_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyt_norm_yaml = {'data_central': data_central_dSig_dyt_norm} kinematics_dSig_dyt_norm_yaml = {'bins': kin_dSig_dyt_norm} - uncertainties_dSig_dyt_norm_yaml = {'definitions': error_definition_dSig_dyt_norm, 'bins': error_dSig_dyt_norm} + uncertainties_dSig_dyt_norm_yaml = { + 'definitions': error_definition_dSig_dyt_norm, + 'bins': error_dSig_dyt_norm, + } with open('data_dSig_dyt_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyt_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyt_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dyt_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyt_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyt_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyt_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyt_norm_yaml, file, sort_keys=False) -# dSig_dyttBar + # dSig_dyttBar - hepdata_tables="rawdata/d041-x01-y01.yaml" + hepdata_tables = "rawdata/d041-x01-y01.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/d041-x01-y01_cov.yaml" + covariance_matrix = "rawdata/d041-x01-y01_cov.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) for i in range(100): covmat_dSig_dyttBar.append(input2['dependent_variables'][0]['values'][i]['value']) artunc_dSig_dyttBar = cta(10, covmat_dSig_dyttBar, 0) - sqrts = 13000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -339,42 +396,52 @@ def processData(): y_ttBar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(10): - error_value['ArtUnc_'+str(j+1)] = artunc_dSig_dyttBar[i][j] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}} + error_value['ArtUnc_' + str(j + 1)] = artunc_dSig_dyttBar[i][j] + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } data_central_dSig_dyttBar.append(data_central_value) kin_dSig_dyttBar.append(kin_value) error_dSig_dyttBar.append(error_value) error_definition_dSig_dyttBar = {} for i in range(10): - error_definition_dSig_dyttBar['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyttBar['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyttBar_yaml = {'data_central': data_central_dSig_dyttBar} kinematics_dSig_dyttBar_yaml = {'bins': kin_dSig_dyttBar} - uncertainties_dSig_dyttBar_yaml = {'definitions': error_definition_dSig_dyttBar, 'bins': error_dSig_dyttBar} + uncertainties_dSig_dyttBar_yaml = { + 'definitions': error_definition_dSig_dyttBar, + 'bins': error_dSig_dyttBar, + } with open('data_dSig_dyttBar.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyttBar_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyttBar_yaml, file, sort_keys=False) with open('kinematics_dSig_dyttBar.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyttBar_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyttBar_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyttBar.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_yaml, file, sort_keys=False) -# dSig_dyttBar_norm + # dSig_dyttBar_norm - hepdata_tables="rawdata/d042-x01-y01.yaml" + hepdata_tables = "rawdata/d042-x01-y01.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/d042-x01-y01_cov.yaml" + covariance_matrix = "rawdata/d042-x01-y01_cov.yaml" with open(covariance_matrix, 'r') as file: input2 = yaml.safe_load(file) for i in range(81): covmat_dSig_dyttBar_norm.append(input2['dependent_variables'][0]['values'][i]['value']) artunc_dSig_dyttBar_norm = cta(9, covmat_dSig_dyttBar_norm, 1) - sqrts = 13000.0 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] @@ -385,27 +452,39 @@ def processData(): y_ttBar_max = input['independent_variables'][0]['values'][i]['high'] error_value = {} for j in range(9): - error_value['ArtUnc_'+str(j+1)] = artunc_dSig_dyttBar_norm[i][j] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}} + error_value['ArtUnc_' + str(j + 1)] = artunc_dSig_dyttBar_norm[i][j] + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } data_central_dSig_dyttBar_norm.append(data_central_value) kin_dSig_dyttBar_norm.append(kin_value) error_dSig_dyttBar_norm.append(error_value) error_definition_dSig_dyttBar_norm = {} for i in range(9): - error_definition_dSig_dyttBar_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyttBar_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyttBar_norm_yaml = {'data_central': data_central_dSig_dyttBar_norm} kinematics_dSig_dyttBar_norm_yaml = {'bins': kin_dSig_dyttBar_norm} - uncertainties_dSig_dyttBar_norm_yaml = {'definitions': error_definition_dSig_dyttBar_norm, 'bins': error_dSig_dyttBar_norm} + uncertainties_dSig_dyttBar_norm_yaml = { + 'definitions': error_definition_dSig_dyttBar_norm, + 'bins': error_dSig_dyttBar_norm, + } with open('data_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_norm_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py index 75eebeeddb..fcda4a06a5 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_LJ_DIF/filter.py @@ -1,6 +1,8 @@ import yaml + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -69,66 +71,84 @@ def processData(): covMatArray_dSig_dyt = [] covMatArray_dSig_dyt_norm = [] -# dSig_dmttBar data + # dSig_dmttBar data - hepdata_tables="rawdata/"+tables_dSig_dmttBar[0]+".yaml" + hepdata_tables = "rawdata/" + tables_dSig_dmttBar[0] + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - - covariance_matrix="rawdata/parton_abs_ttm_covariance.yaml" + + covariance_matrix = "rawdata/parton_abs_ttm_covariance.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dmttBar*ndata_dSig_dmttBar): + for i in range(ndata_dSig_dmttBar * ndata_dSig_dmttBar): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dmttBar.append(covMatEl) artUncMat_dSig_dmttBar = cta(ndata_dSig_dmttBar, covMatArray_dSig_dmttBar, 0) sqrts = 13000 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] - + for i in range(len(values)): data_central_value = values[i]['value'] data_central_dSig_dmttBar.append(data_central_value) m_ttBar_min = input['independent_variables'][0]['values'][i]['low'] m_ttBar_max = input['independent_variables'][0]['values'][i]['high'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + } kin_dSig_dmttBar.append(kin_value) error_value = {} error_value['stat'] = values[i]['errors'][0]['symerror'] - error_value['sys'] = 0 #values[i]['errors'][1]['symerror'] + error_value['sys'] = 0 # values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dmttBar): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dmttBar[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dmttBar[i][j]) error_dSig_dmttBar.append(error_value) - + error_definition_dSig_dmttBar = {} - error_definition_dSig_dmttBar['stat'] = {'definition': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} - error_definition_dSig_dmttBar['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dmttBar['stat'] = { + 'definition': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } + error_definition_dSig_dmttBar['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(ndata_dSig_dmttBar): - error_definition_dSig_dmttBar['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dmttBar['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dmttBar_yaml = {'data_central': data_central_dSig_dmttBar} kinematics_dSig_dmttBar_yaml = {'bins': kin_dSig_dmttBar} - uncertainties_dSig_dmttBar_yaml = {'definitions': error_definition_dSig_dmttBar, 'bins': error_dSig_dmttBar} + uncertainties_dSig_dmttBar_yaml = { + 'definitions': error_definition_dSig_dmttBar, + 'bins': error_dSig_dmttBar, + } with open('data_dSig_dmttBar.yaml', 'w') as file: - yaml.dump(data_central_dSig_dmttBar_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dmttBar_yaml, file, sort_keys=False) with open('kinematics_dSig_dmttBar.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dmttBar_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dmttBar_yaml, file, sort_keys=False) with open('uncertainties_dSig_dmttBar.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dmttBar_yaml, file, sort_keys=False) - -# dSig_dmttBar_norm data + # dSig_dmttBar_norm data - hepdata_tables="rawdata/"+tables_dSig_dmttBar_norm[0]+".yaml" + hepdata_tables = "rawdata/" + tables_dSig_dmttBar_norm[0] + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/parton_norm_ttm_covariance.yaml" + covariance_matrix = "rawdata/parton_norm_ttm_covariance.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dmttBar_norm*ndata_dSig_dmttBar_norm): + for i in range(ndata_dSig_dmttBar_norm * ndata_dSig_dmttBar_norm): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dmttBar_norm.append(covMatEl) artUncMat_dSig_dmttBar_norm = cta(ndata_dSig_dmttBar_norm, covMatArray_dSig_dmttBar_norm, 1) @@ -142,44 +162,63 @@ def processData(): data_central_dSig_dmttBar_norm.append(data_central_value) m_ttBar_min = input['independent_variables'][0]['values'][i]['low'] m_ttBar_max = input['independent_variables'][0]['values'][i]['high'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + } kin_dSig_dmttBar_norm.append(kin_value) error_value = {} error_value['stat'] = values[i]['errors'][0]['symerror'] - error_value['sys'] = 0 #values[i]['errors'][1]['symerror'] + error_value['sys'] = 0 # values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dmttBar_norm): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dmttBar_norm[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dmttBar_norm[i][j]) error_dSig_dmttBar_norm.append(error_value) - + error_definition_dSig_dmttBar_norm = {} - error_definition_dSig_dmttBar_norm['stat'] = {'definition': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} - error_definition_dSig_dmttBar_norm['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dmttBar_norm['stat'] = { + 'definition': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } + error_definition_dSig_dmttBar_norm['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(ndata_dSig_dmttBar_norm): - error_definition_dSig_dmttBar_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dmttBar_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dmttBar_norm_yaml = {'data_central': data_central_dSig_dmttBar_norm} kinematics_dSig_dmttBar_norm_yaml = {'bins': kin_dSig_dmttBar_norm} - uncertainties_dSig_dmttBar_norm_yaml = {'definitions': error_definition_dSig_dmttBar_norm, 'bins': error_dSig_dmttBar_norm} + uncertainties_dSig_dmttBar_norm_yaml = { + 'definitions': error_definition_dSig_dmttBar_norm, + 'bins': error_dSig_dmttBar_norm, + } with open('data_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dmttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dmttBar_norm_yaml, file, sort_keys=False) -# dSig_dyttBar data + # dSig_dyttBar data - hepdata_tables="rawdata/"+tables_dSig_dyttBar[0]+".yaml" + hepdata_tables = "rawdata/" + tables_dSig_dyttBar[0] + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - - covariance_matrix="rawdata/parton_abs_tty_covariance.yaml" + + covariance_matrix = "rawdata/parton_abs_tty_covariance.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dyttBar*ndata_dSig_dyttBar): + for i in range(ndata_dSig_dyttBar * ndata_dSig_dyttBar): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dyttBar.append(covMatEl) artUncMat_dSig_dyttBar = cta(ndata_dSig_dyttBar, covMatArray_dSig_dyttBar, 0) @@ -193,44 +232,63 @@ def processData(): data_central_dSig_dyttBar.append(data_central_value) y_ttBar_min = input['independent_variables'][0]['values'][i]['low'] y_ttBar_max = input['independent_variables'][0]['values'][i]['high'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } kin_dSig_dyttBar.append(kin_value) error_value = {} error_value['stat'] = values[i]['errors'][0]['symerror'] - error_value['sys'] = 0 #values[i]['errors'][1]['symerror'] + error_value['sys'] = 0 # values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dyttBar): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dyttBar[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dyttBar[i][j]) error_dSig_dyttBar.append(error_value) - + error_definition_dSig_dyttBar = {} - error_definition_dSig_dyttBar['stat'] = {'definition': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} - error_definition_dSig_dyttBar['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dyttBar['stat'] = { + 'definition': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } + error_definition_dSig_dyttBar['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(ndata_dSig_dyttBar): - error_definition_dSig_dyttBar['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyttBar['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyttBar_yaml = {'data_central': data_central_dSig_dyttBar} kinematics_dSig_dyttBar_yaml = {'bins': kin_dSig_dyttBar} - uncertainties_dSig_dyttBar_yaml = {'definitions': error_definition_dSig_dyttBar, 'bins': error_dSig_dyttBar} + uncertainties_dSig_dyttBar_yaml = { + 'definitions': error_definition_dSig_dyttBar, + 'bins': error_dSig_dyttBar, + } with open('data_dSig_dyttBar.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyttBar_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyttBar_yaml, file, sort_keys=False) with open('kinematics_dSig_dyttBar.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyttBar_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyttBar_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyttBar.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_yaml, file, sort_keys=False) -# dSig_dyttBar_norm data + # dSig_dyttBar_norm data - hepdata_tables="rawdata/"+tables_dSig_dyttBar_norm[0]+".yaml" + hepdata_tables = "rawdata/" + tables_dSig_dyttBar_norm[0] + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - - covariance_matrix="rawdata/parton_norm_tty_covariance.yaml" + + covariance_matrix = "rawdata/parton_norm_tty_covariance.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dyttBar_norm*ndata_dSig_dyttBar_norm): + for i in range(ndata_dSig_dyttBar_norm * ndata_dSig_dyttBar_norm): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dyttBar_norm.append(covMatEl) artUncMat_dSig_dyttBar_norm = cta(ndata_dSig_dyttBar_norm, covMatArray_dSig_dyttBar_norm, 1) @@ -244,46 +302,67 @@ def processData(): data_central_dSig_dyttBar_norm.append(data_central_value) y_ttBar_min = input['independent_variables'][0]['values'][i]['low'] y_ttBar_max = input['independent_variables'][0]['values'][i]['high'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } kin_dSig_dyttBar_norm.append(kin_value) error_value = {} error_value['stat'] = values[i]['errors'][0]['symerror'] - error_value['sys'] = 0 #values[i]['errors'][1]['symerror'] + error_value['sys'] = 0 # values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dyttBar_norm): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dyttBar_norm[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dyttBar_norm[i][j]) error_dSig_dyttBar_norm.append(error_value) - + error_definition_dSig_dyttBar_norm = {} - error_definition_dSig_dyttBar_norm['stat'] = {'definition': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} - error_definition_dSig_dyttBar_norm['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dyttBar_norm['stat'] = { + 'definition': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } + error_definition_dSig_dyttBar_norm['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(ndata_dSig_dyttBar_norm): - error_definition_dSig_dyttBar_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyttBar_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyttBar_norm_yaml = {'data_central': data_central_dSig_dyttBar_norm} kinematics_dSig_dyttBar_norm_yaml = {'bins': kin_dSig_dyttBar_norm} - uncertainties_dSig_dyttBar_norm_yaml = {'definitions': error_definition_dSig_dyttBar_norm, 'bins': error_dSig_dyttBar_norm} + uncertainties_dSig_dyttBar_norm_yaml = { + 'definitions': error_definition_dSig_dyttBar_norm, + 'bins': error_dSig_dyttBar_norm, + } with open('data_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_norm_yaml, file, sort_keys=False) -# d2Sig_dyttBar_dmttBar data + # d2Sig_dyttBar_dmttBar data - covariance_matrix="rawdata/parton_abs_ttm+tty_covariance.yaml" + covariance_matrix = "rawdata/parton_abs_ttm+tty_covariance.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_d2Sig_dyttbar_dmttbar*ndata_d2Sig_dyttbar_dmttbar): + for i in range(ndata_d2Sig_dyttbar_dmttbar * ndata_d2Sig_dyttbar_dmttbar): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_d2Sig_dyttbar_dmttbar.append(covMatEl) - artUncMat_d2Sig_dyttbar_dmttbar = cta(ndata_d2Sig_dyttbar_dmttbar, covMatArray_d2Sig_dyttbar_dmttbar, 0) + artUncMat_d2Sig_dyttbar_dmttbar = cta( + ndata_d2Sig_dyttbar_dmttbar, covMatArray_d2Sig_dyttbar_dmttbar, 0 + ) for i in tables_d2Sig_dyttbar_dmttbar: - hepdata_tables="rawdata/parton_abs_ttm+tty_"+str(i)+".yaml" + hepdata_tables = "rawdata/parton_abs_ttm+tty_" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -291,53 +370,75 @@ def processData(): m_t2 = 29756.25 m_ttBar_min = input['dependent_variables'][0]['qualifiers'][0]['value'] m_ttBar_max = input['dependent_variables'][0]['qualifiers'][1]['value'] - values = input ['dependent_variables'][0]['values'] + values = input['dependent_variables'][0]['values'] for j in range(len(values)): data_central_value = values[j]['value'] data_central_d2Sig_dyttbar_dmttbar.append(data_central_value) y_ttBar_min = input['independent_variables'][0]['values'][j]['low'] y_ttBar_max = input['independent_variables'][0]['values'][j]['high'] - kin_value = {'sqrts':{'min': None,'mid': sqrts,'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar':{'min': m_ttBar_min,'mid': None,'max': m_ttBar_max}, 'y_ttBar':{'min': y_ttBar_min,'mid': None,'max': y_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } kin_d2Sig_dyttbar_dmttbar.append(kin_value) error_value = {} error_value['stat'] = values[j]['errors'][0]['symerror'] - error_value['sys'] = 0 #values[j]['errors'][1]['symerror'] + error_value['sys'] = 0 # values[j]['errors'][1]['symerror'] for k in range(ndata_d2Sig_dyttbar_dmttbar): - error_value['ArtUnc_'+str(k+1)] = float(artUncMat_d2Sig_dyttbar_dmttbar[j][k]) + error_value['ArtUnc_' + str(k + 1)] = float(artUncMat_d2Sig_dyttbar_dmttbar[j][k]) error_d2Sig_dyttbar_dmttbar.append(error_value) error_definition_d2Sig_dyttbar_dmttbar = {} - error_definition_d2Sig_dyttbar_dmttbar['stat'] = {'definition': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} - error_definition_d2Sig_dyttbar_dmttbar['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_d2Sig_dyttbar_dmttbar['stat'] = { + 'definition': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } + error_definition_d2Sig_dyttbar_dmttbar['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(ndata_d2Sig_dyttbar_dmttbar): - error_definition_d2Sig_dyttbar_dmttbar['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_d2Sig_dyttbar_dmttbar['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_d2Sig_dyttbar_dmttbar_yaml = {'data_central': data_central_d2Sig_dyttbar_dmttbar} kinematics_d2Sig_dyttbar_dmttbar_yaml = {'bins': kin_d2Sig_dyttbar_dmttbar} - uncertainties_d2Sig_dyttbar_dmttbar_yaml = {'definitions': error_definition_d2Sig_dyttbar_dmttbar, 'bins': error_d2Sig_dyttbar_dmttbar} + uncertainties_d2Sig_dyttbar_dmttbar_yaml = { + 'definitions': error_definition_d2Sig_dyttbar_dmttbar, + 'bins': error_d2Sig_dyttbar_dmttbar, + } with open('data_d2Sig_dyttBar_dmttBar.yaml', 'w') as file: - yaml.dump(data_central_d2Sig_dyttbar_dmttbar_yaml, file, sort_keys=False) + yaml.dump(data_central_d2Sig_dyttbar_dmttbar_yaml, file, sort_keys=False) with open('kinematics_d2Sig_dyttBar_dmttBar.yaml', 'w') as file: - yaml.dump(kinematics_d2Sig_dyttbar_dmttbar_yaml, file, sort_keys=False) + yaml.dump(kinematics_d2Sig_dyttbar_dmttbar_yaml, file, sort_keys=False) with open('uncertainties_d2Sig_dyttBar_dmttBar.yaml', 'w') as file: yaml.dump(uncertainties_d2Sig_dyttbar_dmttbar_yaml, file, sort_keys=False) -# d2Sig_dyttBar_dmttBar_norm data + # d2Sig_dyttBar_dmttBar_norm data - covariance_matrix="rawdata/parton_norm_ttm+tty_covariance.yaml" + covariance_matrix = "rawdata/parton_norm_ttm+tty_covariance.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_d2Sig_dyttbar_dmttbar_norm*ndata_d2Sig_dyttbar_dmttbar_norm): + for i in range(ndata_d2Sig_dyttbar_dmttbar_norm * ndata_d2Sig_dyttbar_dmttbar_norm): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_d2Sig_dyttbar_dmttbar_norm.append(covMatEl) - artUncMat_d2Sig_dyttbar_dmttbar_norm = cta(ndata_d2Sig_dyttbar_dmttbar_norm, covMatArray_d2Sig_dyttbar_dmttbar_norm, 1) + artUncMat_d2Sig_dyttbar_dmttbar_norm = cta( + ndata_d2Sig_dyttbar_dmttbar_norm, covMatArray_d2Sig_dyttbar_dmttbar_norm, 1 + ) for i in tables_d2Sig_dyttBar_dmttBar_norm: - hepdata_tables="rawdata/parton_norm_ttm+tty_"+str(i)+".yaml" + hepdata_tables = "rawdata/parton_norm_ttm+tty_" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -345,237 +446,333 @@ def processData(): m_t2 = 29756.25 m_ttBar_min = input['dependent_variables'][0]['qualifiers'][0]['value'] m_ttBar_max = input['dependent_variables'][0]['qualifiers'][1]['value'] - values = input ['dependent_variables'][0]['values'] + values = input['dependent_variables'][0]['values'] for j in range(len(values)): data_central_value = values[j]['value'] data_central_d2Sig_dyttbar_dmttbar_norm.append(data_central_value) y_ttBar_min = input['independent_variables'][0]['values'][j]['low'] y_ttBar_max = input['independent_variables'][0]['values'][j]['high'] - kin_value = {'sqrts':{'min': None,'mid': sqrts,'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar':{'min': m_ttBar_min,'mid': None,'max': m_ttBar_max}, 'y_ttBar':{'min': y_ttBar_min,'mid': None,'max': y_ttBar_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + } kin_d2Sig_dyttbar_dmttbar_norm.append(kin_value) error_value = {} error_value['stat'] = values[j]['errors'][0]['symerror'] - error_value['sys'] = 0 #values[j]['errors'][1]['symerror'] + error_value['sys'] = 0 # values[j]['errors'][1]['symerror'] for k in range(ndata_d2Sig_dyttbar_dmttbar_norm): - error_value['ArtUnc_'+str(k+1)] = float(artUncMat_d2Sig_dyttbar_dmttbar_norm[j][k]) + error_value['ArtUnc_' + str(k + 1)] = float( + artUncMat_d2Sig_dyttbar_dmttbar_norm[j][k] + ) error_d2Sig_dyttbar_dmttbar_norm.append(error_value) error_definition_d2Sig_dyttbar_dmttbar_norm = {} - error_definition_d2Sig_dyttbar_dmttbar_norm['stat'] = {'definition': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} - error_definition_d2Sig_dyttbar_dmttbar_norm['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_d2Sig_dyttbar_dmttbar_norm['stat'] = { + 'definition': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } + error_definition_d2Sig_dyttbar_dmttbar_norm['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(ndata_d2Sig_dyttbar_dmttbar_norm): - error_definition_d2Sig_dyttbar_dmttbar_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} - - data_central_d2Sig_dyttbar_dmttbar_norm_yaml = {'data_central': data_central_d2Sig_dyttbar_dmttbar_norm} + error_definition_d2Sig_dyttbar_dmttbar_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } + + data_central_d2Sig_dyttbar_dmttbar_norm_yaml = { + 'data_central': data_central_d2Sig_dyttbar_dmttbar_norm + } kinematics_d2Sig_dyttbar_dmttbar_norm_yaml = {'bins': kin_d2Sig_dyttbar_dmttbar_norm} - uncertainties_d2Sig_dyttbar_dmttbar_norm_yaml = {'definitions': error_definition_d2Sig_dyttbar_dmttbar_norm, 'bins': error_d2Sig_dyttbar_dmttbar_norm} + uncertainties_d2Sig_dyttbar_dmttbar_norm_yaml = { + 'definitions': error_definition_d2Sig_dyttbar_dmttbar_norm, + 'bins': error_d2Sig_dyttbar_dmttbar_norm, + } with open('data_d2Sig_dyttBar_dmttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_d2Sig_dyttbar_dmttbar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_d2Sig_dyttbar_dmttbar_norm_yaml, file, sort_keys=False) with open('kinematics_d2Sig_dyttBar_dmttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_d2Sig_dyttbar_dmttbar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_d2Sig_dyttbar_dmttbar_norm_yaml, file, sort_keys=False) with open('uncertainties_d2Sig_dyttBar_dmttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_d2Sig_dyttbar_dmttbar_norm_yaml, file, sort_keys=False) -# dSig_dpTt data + # dSig_dpTt data - hepdata_tables="rawdata/"+tables_dSig_dpTt[0]+".yaml" + hepdata_tables = "rawdata/" + tables_dSig_dpTt[0] + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - - covariance_matrix="rawdata/parton_abs_tleppt_covariance.yaml" + + covariance_matrix = "rawdata/parton_abs_tleppt_covariance.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dpTt*ndata_dSig_dpTt): + for i in range(ndata_dSig_dpTt * ndata_dSig_dpTt): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dpTt.append(covMatEl) artUncMat_dSig_dpTt = cta(ndata_dSig_dpTt, covMatArray_dSig_dpTt, 0) sqrts = 13000 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] - + for i in range(len(values)): data_central_value = values[i]['value'] data_central_dSig_dpTt.append(data_central_value) pT_t_min = input['independent_variables'][0]['values'][i]['low'] pT_t_max = input['independent_variables'][0]['values'][i]['high'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}, + } kin_dSig_dpTt.append(kin_value) error_value = {} error_value['stat'] = values[i]['errors'][0]['symerror'] - error_value['sys'] = 0 #values[i]['errors'][1]['symerror'] + error_value['sys'] = 0 # values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dpTt): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dpTt[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dpTt[i][j]) error_dSig_dpTt.append(error_value) - + error_definition_dSig_dpTt = {} - error_definition_dSig_dpTt['stat'] = {'definition': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} - error_definition_dSig_dpTt['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dpTt['stat'] = { + 'definition': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } + error_definition_dSig_dpTt['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(ndata_dSig_dpTt): - error_definition_dSig_dpTt['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dpTt['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dpTt_yaml = {'data_central': data_central_dSig_dpTt} kinematics_dSig_dpTt_yaml = {'bins': kin_dSig_dpTt} - uncertainties_dSig_dpTt_yaml = {'definitions': error_definition_dSig_dpTt, 'bins': error_dSig_dpTt} + uncertainties_dSig_dpTt_yaml = { + 'definitions': error_definition_dSig_dpTt, + 'bins': error_dSig_dpTt, + } with open('data_dSig_dpTt.yaml', 'w') as file: - yaml.dump(data_central_dSig_dpTt_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dpTt_yaml, file, sort_keys=False) with open('kinematics_dSig_dpTt.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dpTt_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dpTt_yaml, file, sort_keys=False) with open('uncertainties_dSig_dpTt.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dpTt_yaml, file, sort_keys=False) + # dSig_dpTt_norm data -# dSig_dpTt_norm data - - hepdata_tables="rawdata/"+tables_dSig_dpTt_norm[0]+".yaml" + hepdata_tables = "rawdata/" + tables_dSig_dpTt_norm[0] + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - - covariance_matrix="rawdata/parton_norm_tleppt_covariance.yaml" + + covariance_matrix = "rawdata/parton_norm_tleppt_covariance.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dpTt_norm*ndata_dSig_dpTt_norm): + for i in range(ndata_dSig_dpTt_norm * ndata_dSig_dpTt_norm): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dpTt_norm.append(covMatEl) artUncMat_dSig_dpTt_norm = cta(ndata_dSig_dpTt_norm, covMatArray_dSig_dpTt_norm, 1) sqrts = 13000 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] - + for i in range(len(values)): data_central_value = values[i]['value'] data_central_dSig_dpTt_norm.append(data_central_value) pT_t_min = input['independent_variables'][0]['values'][i]['low'] pT_t_max = input['independent_variables'][0]['values'][i]['high'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}, + } kin_dSig_dpTt_norm.append(kin_value) error_value = {} error_value['stat'] = values[i]['errors'][0]['symerror'] - error_value['sys'] = 0 #values[i]['errors'][1]['symerror'] + error_value['sys'] = 0 # values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dpTt_norm): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dpTt_norm[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dpTt_norm[i][j]) error_dSig_dpTt_norm.append(error_value) - + error_definition_dSig_dpTt_norm = {} - error_definition_dSig_dpTt_norm['stat'] = {'definition': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} - error_definition_dSig_dpTt_norm['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dpTt_norm['stat'] = { + 'definition': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } + error_definition_dSig_dpTt_norm['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(ndata_dSig_dpTt_norm): - error_definition_dSig_dpTt_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dpTt_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dpTt_norm_yaml = {'data_central': data_central_dSig_dpTt_norm} kinematics_dSig_dpTt_norm_yaml = {'bins': kin_dSig_dpTt_norm} - uncertainties_dSig_dpTt_norm_yaml = {'definitions': error_definition_dSig_dpTt_norm, 'bins': error_dSig_dpTt_norm} + uncertainties_dSig_dpTt_norm_yaml = { + 'definitions': error_definition_dSig_dpTt_norm, + 'bins': error_dSig_dpTt_norm, + } with open('data_dSig_dpTt_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dpTt_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dpTt_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dpTt_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dpTt_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dpTt_norm_yaml, file, sort_keys=False) + # dSig_dyt data -# dSig_dyt data - - hepdata_tables="rawdata/"+tables_dSig_dyt[0]+".yaml" + hepdata_tables = "rawdata/" + tables_dSig_dyt[0] + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - - covariance_matrix="rawdata/parton_abs_tlepy_covariance.yaml" + + covariance_matrix = "rawdata/parton_abs_tlepy_covariance.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dyt*ndata_dSig_dyt): + for i in range(ndata_dSig_dyt * ndata_dSig_dyt): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dyt.append(covMatEl) artUncMat_dSig_dyt = cta(ndata_dSig_dyt, covMatArray_dSig_dyt, 0) sqrts = 13000 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] - + for i in range(len(values)): data_central_value = values[i]['value'] data_central_dSig_dyt.append(data_central_value) y_t_min = input['independent_variables'][0]['values'][i]['low'] y_t_max = input['independent_variables'][0]['values'][i]['high'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, + } kin_dSig_dyt.append(kin_value) error_value = {} error_value['stat'] = values[i]['errors'][0]['symerror'] - error_value['sys'] = 0 #values[i]['errors'][1]['symerror'] + error_value['sys'] = 0 # values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dyt): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dyt[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dyt[i][j]) error_dSig_dyt.append(error_value) - + error_definition_dSig_dyt = {} - error_definition_dSig_dyt['stat'] = {'definition': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} - error_definition_dSig_dyt['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dyt['stat'] = { + 'definition': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } + error_definition_dSig_dyt['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(ndata_dSig_dyt): - error_definition_dSig_dyt['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyt['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyt_yaml = {'data_central': data_central_dSig_dyt} kinematics_dSig_dyt_yaml = {'bins': kin_dSig_dyt} uncertainties_dSig_dyt_yaml = {'definitions': error_definition_dSig_dyt, 'bins': error_dSig_dyt} with open('data_dSig_dyt.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyt_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyt_yaml, file, sort_keys=False) with open('kinematics_dSig_dyt.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyt_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyt_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyt.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyt_yaml, file, sort_keys=False) -# dSig_dyt_norm data + # dSig_dyt_norm data - hepdata_tables="rawdata/"+tables_dSig_dyt_norm[0]+".yaml" + hepdata_tables = "rawdata/" + tables_dSig_dyt_norm[0] + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - - covariance_matrix="rawdata/parton_norm_tlepy_covariance.yaml" + + covariance_matrix = "rawdata/parton_norm_tlepy_covariance.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - for i in range(ndata_dSig_dyt_norm*ndata_dSig_dyt_norm): + for i in range(ndata_dSig_dyt_norm * ndata_dSig_dyt_norm): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dyt_norm.append(covMatEl) artUncMat_dSig_dyt_norm = cta(ndata_dSig_dyt_norm, covMatArray_dSig_dyt_norm, 1) sqrts = 13000 m_t2 = 29756.25 values = input['dependent_variables'][0]['values'] - + for i in range(len(values)): data_central_value = values[i]['value'] data_central_dSig_dyt_norm.append(data_central_value) y_t_min = input['independent_variables'][0]['values'][i]['low'] y_t_max = input['independent_variables'][0]['values'][i]['high'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, + } kin_dSig_dyt_norm.append(kin_value) error_value = {} error_value['stat'] = values[i]['errors'][0]['symerror'] - error_value['sys'] = 0 #values[i]['errors'][1]['symerror'] + error_value['sys'] = 0 # values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dyt_norm): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dyt_norm[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dyt_norm[i][j]) error_dSig_dyt_norm.append(error_value) - + error_definition_dSig_dyt_norm = {} - error_definition_dSig_dyt_norm['stat'] = {'definition': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} - error_definition_dSig_dyt_norm['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dyt_norm['stat'] = { + 'definition': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } + error_definition_dSig_dyt_norm['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(ndata_dSig_dyt_norm): - error_definition_dSig_dyt_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyt_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } data_central_dSig_dyt_norm_yaml = {'data_central': data_central_dSig_dyt_norm} kinematics_dSig_dyt_norm_yaml = {'bins': kin_dSig_dyt_norm} - uncertainties_dSig_dyt_norm_yaml = {'definitions': error_definition_dSig_dyt_norm, 'bins': error_dSig_dyt_norm} + uncertainties_dSig_dyt_norm_yaml = { + 'definitions': error_definition_dSig_dyt_norm, + 'bins': error_dSig_dyt_norm, + } with open('data_dSig_dyt_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyt_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyt_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dyt_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyt_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyt_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyt_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyt_norm_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py index 4eb8975a11..0cd577753c 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_2L_DIF/filter.py @@ -1,10 +1,12 @@ import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import trimat_to_fullmat as ttf + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -19,17 +21,17 @@ def processData(): kin_d2Sig_dmttBar_dyttBar_norm = [] error_d2Sig_dmttBar_dyttBar_norm = [] -# d2Sig_dyt_dpTt_norm + # d2Sig_dyt_dpTt_norm - hepdata_tables="rawdata/CMS_8TeV_ttbar_DoubleDiff_yt_ptt.yaml" + hepdata_tables = "rawdata/CMS_8TeV_ttbar_DoubleDiff_yt_ptt.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - correlation_matrix="rawdata/CMS_8TeV_ttbar_DoubleDiff_yt_ptt_statcorr.yaml" + correlation_matrix = "rawdata/CMS_8TeV_ttbar_DoubleDiff_yt_ptt_statcorr.yaml" with open(correlation_matrix, 'r') as file: input2 = yaml.safe_load(file) -# systematics_breakdown="rawdata/CMS_8TeV_ttbar_DoubleDiff_yt_ptt_syst.yaml" -# with open(systematics_breakdown, 'r') as file: -# input3 = yaml.safe_load(file) + # systematics_breakdown="rawdata/CMS_8TeV_ttbar_DoubleDiff_yt_ptt_syst.yaml" + # with open(systematics_breakdown, 'r') as file: + # input3 = yaml.safe_load(file) sqrts = 8000 m_t2 = 29756.25 @@ -43,7 +45,6 @@ def processData(): cormatlist1 = ttf(0, trimatlist1) covmatlist1 = ctc(statlist1, cormatlist1) artunc1 = cta(len(values), covmatlist1, 1) - for i in range(len(values)): data_central_value = values[i]['value'] @@ -58,41 +59,57 @@ def processData(): data_central_value = data_central_value + se_delta error_value['sys'] = se_sigma for j in range(len(values)): - error_value['ArtUnc_'+str(j+1)] = artunc1[i][j] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}} + error_value['ArtUnc_' + str(j + 1)] = artunc1[i][j] + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, + 'pT_t': {'min': pT_t_min, 'mid': None, 'max': pT_t_max}, + } data_central_d2Sig_dyt_dpTt_norm.append(data_central_value) kin_d2Sig_dyt_dpTt_norm.append(kin_value) error_d2Sig_dyt_dpTt_norm.append(error_value) error_definition_d2Sig_dyt_dpTt_norm = {} - error_definition_d2Sig_dyt_dpTt_norm['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_d2Sig_dyt_dpTt_norm['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(16): - error_definition_d2Sig_dyt_dpTt_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} - + error_definition_d2Sig_dyt_dpTt_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } + data_central_d2Sig_dyt_dpTt_norm_yaml = {'data_central': data_central_d2Sig_dyt_dpTt_norm} kinematics_d2Sig_dyt_dpTt_norm_yaml = {'bins': kin_d2Sig_dyt_dpTt_norm} - uncertainties_d2Sig_dyt_dpTt_norm_yaml = {'definitions': error_definition_d2Sig_dyt_dpTt_norm, 'bins': error_d2Sig_dyt_dpTt_norm} + uncertainties_d2Sig_dyt_dpTt_norm_yaml = { + 'definitions': error_definition_d2Sig_dyt_dpTt_norm, + 'bins': error_d2Sig_dyt_dpTt_norm, + } with open('data_d2Sig_dyt_dpTt_norm.yaml', 'w') as file: - yaml.dump(data_central_d2Sig_dyt_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_d2Sig_dyt_dpTt_norm_yaml, file, sort_keys=False) with open('kinematics_d2Sig_dyt_dpTt_norm.yaml', 'w') as file: - yaml.dump(kinematics_d2Sig_dyt_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_d2Sig_dyt_dpTt_norm_yaml, file, sort_keys=False) with open('uncertainties_d2Sig_dyt_dpTt_norm.yaml', 'w') as file: yaml.dump(uncertainties_d2Sig_dyt_dpTt_norm_yaml, file, sort_keys=False) -# d2Sig_dyt_dmttBar_norm + # d2Sig_dyt_dmttBar_norm - hepdata_tables="rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_yt.yaml" + hepdata_tables = "rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_yt.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - correlation_matrix="rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_yt_statcorr.yaml" + correlation_matrix = "rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_yt_statcorr.yaml" with open(correlation_matrix, 'r') as file: input2 = yaml.safe_load(file) -# systematics_breakdown="rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_yt_syst.yaml" -# with open(systematics_breakdown, 'r') as file: -# input3 = yaml.safe_load(file) + # systematics_breakdown="rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_yt_syst.yaml" + # with open(systematics_breakdown, 'r') as file: + # input3 = yaml.safe_load(file) sqrts = 8000 m_t2 = 29756.25 @@ -106,7 +123,6 @@ def processData(): cormatlist2 = ttf(0, trimatlist2) covmatlist2 = ctc(statlist2, cormatlist2) artunc2 = cta(len(values), covmatlist2, 1) - for i in range(len(values)): data_central_value = values[i]['value'] @@ -121,41 +137,57 @@ def processData(): data_central_value = data_central_value + se_delta error_value['sys'] = se_sigma for j in range(len(values)): - error_value['ArtUnc_'+str(j+1)] = artunc2[i][j] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}} + error_value['ArtUnc_' + str(j + 1)] = artunc2[i][j] + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_t': {'min': y_t_min, 'mid': None, 'max': y_t_max}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + } data_central_d2Sig_dyt_dmttBar_norm.append(data_central_value) kin_d2Sig_dyt_dmttBar_norm.append(kin_value) error_d2Sig_dyt_dmttBar_norm.append(error_value) error_definition_d2Sig_dyt_dmttBar_norm = {} - error_definition_d2Sig_dyt_dmttBar_norm['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_d2Sig_dyt_dmttBar_norm['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(16): - error_definition_d2Sig_dyt_dmttBar_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} - + error_definition_d2Sig_dyt_dmttBar_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } + data_central_d2Sig_dyt_dmttBar_norm_yaml = {'data_central': data_central_d2Sig_dyt_dmttBar_norm} kinematics_d2Sig_dyt_dmttBar_norm_yaml = {'bins': kin_d2Sig_dyt_dmttBar_norm} - uncertainties_d2Sig_dyt_dmttBar_norm_yaml = {'definitions': error_definition_d2Sig_dyt_dmttBar_norm, 'bins': error_d2Sig_dyt_dmttBar_norm} + uncertainties_d2Sig_dyt_dmttBar_norm_yaml = { + 'definitions': error_definition_d2Sig_dyt_dmttBar_norm, + 'bins': error_d2Sig_dyt_dmttBar_norm, + } with open('data_d2Sig_dyt_dmttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_d2Sig_dyt_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_d2Sig_dyt_dmttBar_norm_yaml, file, sort_keys=False) with open('kinematics_d2Sig_dyt_dmttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_d2Sig_dyt_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_d2Sig_dyt_dmttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_d2Sig_dyt_dmttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_d2Sig_dyt_dmttBar_norm_yaml, file, sort_keys=False) -# d2Sig_dmttBar_dyttBar_norm + # d2Sig_dmttBar_dyttBar_norm - hepdata_tables="rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_ytt.yaml" + hepdata_tables = "rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_ytt.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - correlation_matrix="rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_ytt_statcorr.yaml" + correlation_matrix = "rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_ytt_statcorr.yaml" with open(correlation_matrix, 'r') as file: input2 = yaml.safe_load(file) -# systematics_breakdown="rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_ytt_syst.yaml" -# with open(systematics_breakdown, 'r') as file: -# input3 = yaml.safe_load(file) + # systematics_breakdown="rawdata/CMS_8TeV_ttbar_DoubleDiff_mtt_ytt_syst.yaml" + # with open(systematics_breakdown, 'r') as file: + # input3 = yaml.safe_load(file) sqrts = 8000 m_t2 = 29756.25 @@ -169,7 +201,6 @@ def processData(): cormatlist3 = ttf(0, trimatlist3) covmatlist3 = ctc(statlist3, cormatlist3) artunc3 = cta(len(values), covmatlist3, 1) - for i in range(len(values)): data_central_value = values[i]['value'] @@ -184,28 +215,47 @@ def processData(): data_central_value = data_central_value + se_delta error_value['sys'] = se_sigma for j in range(len(values)): - error_value['ArtUnc_'+str(j+1)] = artunc3[i][j] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}} + error_value['ArtUnc_' + str(j + 1)] = artunc3[i][j] + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': None, 'max': y_ttBar_max}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': None, 'max': m_ttBar_max}, + } data_central_d2Sig_dmttBar_dyttBar_norm.append(data_central_value) kin_d2Sig_dmttBar_dyttBar_norm.append(kin_value) error_d2Sig_dmttBar_dyttBar_norm.append(error_value) error_definition_d2Sig_dmttBar_dyttBar_norm = {} - error_definition_d2Sig_dmttBar_dyttBar_norm['sys'] = {'definition': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + error_definition_d2Sig_dmttBar_dyttBar_norm['sys'] = { + 'definition': 'total systematic uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + } for i in range(16): - error_definition_d2Sig_dmttBar_dyttBar_norm['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} - - data_central_d2Sig_dmttBar_dyttBar_norm_yaml = {'data_central': data_central_d2Sig_dmttBar_dyttBar_norm} + error_definition_d2Sig_dmttBar_dyttBar_norm['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } + + data_central_d2Sig_dmttBar_dyttBar_norm_yaml = { + 'data_central': data_central_d2Sig_dmttBar_dyttBar_norm + } kinematics_d2Sig_dmttBar_dyttBar_norm_yaml = {'bins': kin_d2Sig_dmttBar_dyttBar_norm} - uncertainties_d2Sig_dmttBar_dyttBar_norm_yaml = {'definitions': error_definition_d2Sig_dmttBar_dyttBar_norm, 'bins': error_d2Sig_dmttBar_dyttBar_norm} + uncertainties_d2Sig_dmttBar_dyttBar_norm_yaml = { + 'definitions': error_definition_d2Sig_dmttBar_dyttBar_norm, + 'bins': error_d2Sig_dmttBar_dyttBar_norm, + } with open('data_d2Sig_dmttBar_dyttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_d2Sig_dmttBar_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_d2Sig_dmttBar_dyttBar_norm_yaml, file, sort_keys=False) with open('kinematics_d2Sig_dmttBar_dyttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_d2Sig_dmttBar_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_d2Sig_dmttBar_dyttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_d2Sig_dmttBar_dyttBar_norm.yaml', 'w') as file: - yaml.dump(uncertainties_d2Sig_dmttBar_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(uncertainties_d2Sig_dmttBar_dyttBar_norm_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py index 850103d456..5f5c5af484 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_8TEV_LJ_DIF/filter.py @@ -1,7 +1,9 @@ import yaml + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -29,21 +31,21 @@ def processData(): covMatArray_dSig_dyttBar = [] covMatArray_dSig_dmttBar = [] -# dSig_dpTt data + # dSig_dpTt data - hepdata_tables="rawdata/Table15.yaml" + hepdata_tables = "rawdata/Table15.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table16.yaml" + covariance_matrix = "rawdata/Table16.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - systematics_breakdown="rawdata/Table17.yaml" + systematics_breakdown = "rawdata/Table17.yaml" with open(systematics_breakdown, 'r') as file3: input3 = yaml.safe_load(file3) - for i in range(ndata_dSig_dpTt*ndata_dSig_dpTt): + for i in range(ndata_dSig_dpTt * ndata_dSig_dpTt): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dpTt.append(covMatEl) artUncMat_dSig_dpTt = cta(ndata_dSig_dpTt, covMatArray_dSig_dpTt, 1) @@ -60,51 +62,73 @@ def processData(): error_value['stat'] = 0 # error_value['sys'] = values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dpTt): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dpTt[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dpTt[i][j]) data_central_value = values[i]['value'] for j in range(11): - error_value[input3['independent_variables'][0]['values'][j]['value']] = pta(str(input3['dependent_variables'][i]['values'][j]['value']), data_central_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'pT_t': {'min': pT_t_min, 'mid': pT_t_mid, 'max': pT_t_max}} + error_value[input3['independent_variables'][0]['values'][j]['value']] = pta( + str(input3['dependent_variables'][i]['values'][j]['value']), data_central_value + ) + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'pT_t': {'min': pT_t_min, 'mid': pT_t_mid, 'max': pT_t_max}, + } data_central_dSig_dpTt.append(data_central_value) kin_dSig_dpTt.append(kin_value) error_dSig_dpTt.append(error_value) error_definition_dSig_dpTt = {} - error_definition_dSig_dpTt['stat'] = {'description': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} + error_definition_dSig_dpTt['stat'] = { + 'description': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } # error_definition_dSig_dpTt['sys'] = {'description': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dpTt): - error_definition_dSig_dpTt['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dpTt['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } for i in range(11): - error_definition_dSig_dpTt[input3['independent_variables'][0]['values'][i]['value']] = {'definition': 'systematic uncertainty- '+str(input3['independent_variables'][0]['values'][i]['value']), 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dpTt[input3['independent_variables'][0]['values'][i]['value']] = { + 'definition': 'systematic uncertainty- ' + + str(input3['independent_variables'][0]['values'][i]['value']), + 'treatment': 'MULT', + 'type': 'CORR', + } data_central_dSig_dpTt_norm_yaml = {'data_central': data_central_dSig_dpTt} kinematics_dSig_dpTt_norm_yaml = {'bins': kin_dSig_dpTt} - uncertainties_dSig_dpTt_norm_yaml = {'definitions': error_definition_dSig_dpTt, 'bins': error_dSig_dpTt} + uncertainties_dSig_dpTt_norm_yaml = { + 'definitions': error_definition_dSig_dpTt, + 'bins': error_dSig_dpTt, + } with open('data_dSig_dpTt_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dpTt_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dpTt_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dpTt_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dpTt_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dpTt_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dpTt_norm_yaml, file, sort_keys=False) -# dSig_dyt data + # dSig_dyt data - hepdata_tables="rawdata/Table21.yaml" + hepdata_tables = "rawdata/Table21.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table22.yaml" + covariance_matrix = "rawdata/Table22.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - systematics_breakdown="rawdata/Table23.yaml" + systematics_breakdown = "rawdata/Table23.yaml" with open(systematics_breakdown, 'r') as file3: input3 = yaml.safe_load(file3) - for i in range(ndata_dSig_dyt*ndata_dSig_dyt): + for i in range(ndata_dSig_dyt * ndata_dSig_dyt): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dyt.append(covMatEl) artUncMat_dSig_dyt = cta(ndata_dSig_dyt, covMatArray_dSig_dyt, 1) @@ -121,51 +145,73 @@ def processData(): error_value['stat'] = 0 # error_value['sys'] = values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dyt): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dyt[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dyt[i][j]) data_central_value = values[i]['value'] for j in range(11): - error_value[input3['independent_variables'][0]['values'][j]['value']] = pta(str(input3['dependent_variables'][i]['values'][j]['value']), data_central_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_t': {'min': y_t_min, 'mid': y_t_mid, 'max': y_t_max}} + error_value[input3['independent_variables'][0]['values'][j]['value']] = pta( + str(input3['dependent_variables'][i]['values'][j]['value']), data_central_value + ) + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_t': {'min': y_t_min, 'mid': y_t_mid, 'max': y_t_max}, + } data_central_dSig_dyt.append(data_central_value) kin_dSig_dyt.append(kin_value) error_dSig_dyt.append(error_value) error_definition_dSig_dyt = {} - error_definition_dSig_dyt['stat'] = {'description': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} + error_definition_dSig_dyt['stat'] = { + 'description': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } # error_definition_dSig_dyt['sys'] = {'description': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dyt): - error_definition_dSig_dyt['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyt['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } for i in range(11): - error_definition_dSig_dyt[input3['independent_variables'][0]['values'][i]['value']] = {'definition': 'systematic uncertainty- '+str(input3['independent_variables'][0]['values'][i]['value']), 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dyt[input3['independent_variables'][0]['values'][i]['value']] = { + 'definition': 'systematic uncertainty- ' + + str(input3['independent_variables'][0]['values'][i]['value']), + 'treatment': 'MULT', + 'type': 'CORR', + } data_central_dSig_dyt_norm_yaml = {'data_central': data_central_dSig_dyt} kinematics_dSig_dyt_norm_yaml = {'bins': kin_dSig_dyt} - uncertainties_dSig_dyt_norm_yaml = {'definitions': error_definition_dSig_dyt, 'bins': error_dSig_dyt} + uncertainties_dSig_dyt_norm_yaml = { + 'definitions': error_definition_dSig_dyt, + 'bins': error_dSig_dyt, + } with open('data_dSig_dyt_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyt_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyt_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dyt_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyt_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyt_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyt_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyt_norm_yaml, file, sort_keys=False) -# dSig_dyttBar data + # dSig_dyttBar data - hepdata_tables="rawdata/Table36.yaml" + hepdata_tables = "rawdata/Table36.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table37.yaml" + covariance_matrix = "rawdata/Table37.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - systematics_breakdown="rawdata/Table38.yaml" + systematics_breakdown = "rawdata/Table38.yaml" with open(systematics_breakdown, 'r') as file3: input3 = yaml.safe_load(file3) - for i in range(ndata_dSig_dyttBar*ndata_dSig_dyttBar): + for i in range(ndata_dSig_dyttBar * ndata_dSig_dyttBar): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dyttBar.append(covMatEl) artUncMat_dSig_dyttBar = cta(ndata_dSig_dyttBar, covMatArray_dSig_dyttBar, 1) @@ -182,51 +228,73 @@ def processData(): error_value['stat'] = 0 # error_value['sys'] = values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dyttBar): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dyttBar[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dyttBar[i][j]) data_central_value = values[i]['value'] for j in range(11): - error_value[input3['independent_variables'][0]['values'][j]['value']] = pta(str(input3['dependent_variables'][i]['values'][j]['value']), data_central_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'y_ttBar': {'min': y_ttBar_min, 'mid': y_ttBar_mid, 'max': y_ttBar_max}} + error_value[input3['independent_variables'][0]['values'][j]['value']] = pta( + str(input3['dependent_variables'][i]['values'][j]['value']), data_central_value + ) + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'y_ttBar': {'min': y_ttBar_min, 'mid': y_ttBar_mid, 'max': y_ttBar_max}, + } data_central_dSig_dyttBar.append(data_central_value) kin_dSig_dyttBar.append(kin_value) error_dSig_dyttBar.append(error_value) error_definition_dSig_dyttBar = {} - error_definition_dSig_dyttBar['stat'] = {'description': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} + error_definition_dSig_dyttBar['stat'] = { + 'description': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } # error_definition_dSig_dyttBar['sys'] = {'description': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dyttBar): - error_definition_dSig_dyttBar['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dyttBar['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } for i in range(11): - error_definition_dSig_dyttBar[input3['independent_variables'][0]['values'][i]['value']] = {'definition': 'systematic uncertainty- '+str(input3['independent_variables'][0]['values'][i]['value']), 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dyttBar[input3['independent_variables'][0]['values'][i]['value']] = { + 'definition': 'systematic uncertainty- ' + + str(input3['independent_variables'][0]['values'][i]['value']), + 'treatment': 'MULT', + 'type': 'CORR', + } data_central_dSig_dyttBar_norm_yaml = {'data_central': data_central_dSig_dyttBar} kinematics_dSig_dyttBar_norm_yaml = {'bins': kin_dSig_dyttBar} - uncertainties_dSig_dyttBar_norm_yaml = {'definitions': error_definition_dSig_dyttBar, 'bins': error_dSig_dyttBar} + uncertainties_dSig_dyttBar_norm_yaml = { + 'definitions': error_definition_dSig_dyttBar, + 'bins': error_dSig_dyttBar, + } with open('data_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dyttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dyttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dyttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dyttBar_norm_yaml, file, sort_keys=False) -# dSig_dmttBar data + # dSig_dmttBar data - hepdata_tables="rawdata/Table39.yaml" + hepdata_tables = "rawdata/Table39.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - covariance_matrix="rawdata/Table40.yaml" + covariance_matrix = "rawdata/Table40.yaml" with open(covariance_matrix, 'r') as file2: input2 = yaml.safe_load(file2) - systematics_breakdown="rawdata/Table41.yaml" + systematics_breakdown = "rawdata/Table41.yaml" with open(systematics_breakdown, 'r') as file3: input3 = yaml.safe_load(file3) - for i in range(ndata_dSig_dmttBar*ndata_dSig_dmttBar): + for i in range(ndata_dSig_dmttBar * ndata_dSig_dmttBar): covMatEl = input2['dependent_variables'][0]['values'][i]['value'] covMatArray_dSig_dmttBar.append(covMatEl) artUncMat_dSig_dmttBar = cta(ndata_dSig_dmttBar, covMatArray_dSig_dmttBar, 1) @@ -243,34 +311,57 @@ def processData(): error_value['stat'] = 0 # error_value['sys'] = values[i]['errors'][1]['symerror'] for j in range(ndata_dSig_dmttBar): - error_value['ArtUnc_'+str(j+1)] = float(artUncMat_dSig_dmttBar[i][j]) + error_value['ArtUnc_' + str(j + 1)] = float(artUncMat_dSig_dmttBar[i][j]) data_central_value = values[i]['value'] for j in range(11): - error_value[input3['independent_variables'][0]['values'][j]['value']] = pta(str(input3['dependent_variables'][i]['values'][j]['value']), data_central_value) - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, 'm_ttBar': {'min': m_ttBar_min, 'mid': m_ttBar_mid, 'max': m_ttBar_max}} + error_value[input3['independent_variables'][0]['values'][j]['value']] = pta( + str(input3['dependent_variables'][i]['values'][j]['value']), data_central_value + ) + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'm_t2': {'min': None, 'mid': m_t2, 'max': None}, + 'm_ttBar': {'min': m_ttBar_min, 'mid': m_ttBar_mid, 'max': m_ttBar_max}, + } data_central_dSig_dmttBar.append(data_central_value) kin_dSig_dmttBar.append(kin_value) error_dSig_dmttBar.append(error_value) error_definition_dSig_dmttBar = {} - error_definition_dSig_dmttBar['stat'] = {'description': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} + error_definition_dSig_dmttBar['stat'] = { + 'description': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + } # error_definition_dSig_dmttBar['sys'] = {'description': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'} for i in range(ndata_dSig_dmttBar): - error_definition_dSig_dmttBar['ArtUnc_'+str(i+1)] = {'definition': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'CORR'} + error_definition_dSig_dmttBar['ArtUnc_' + str(i + 1)] = { + 'definition': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'CORR', + } for i in range(11): - error_definition_dSig_dmttBar[input3['independent_variables'][0]['values'][i]['value']] = {'definition': 'systematic uncertainty- '+str(input3['independent_variables'][0]['values'][i]['value']), 'treatment': 'MULT', 'type': 'CORR'} + error_definition_dSig_dmttBar[input3['independent_variables'][0]['values'][i]['value']] = { + 'definition': 'systematic uncertainty- ' + + str(input3['independent_variables'][0]['values'][i]['value']), + 'treatment': 'MULT', + 'type': 'CORR', + } data_central_dSig_dmttBar_norm_yaml = {'data_central': data_central_dSig_dmttBar} kinematics_dSig_dmttBar_norm_yaml = {'bins': kin_dSig_dmttBar} - uncertainties_dSig_dmttBar_norm_yaml = {'definitions': error_definition_dSig_dmttBar, 'bins': error_dSig_dmttBar} + uncertainties_dSig_dmttBar_norm_yaml = { + 'definitions': error_definition_dSig_dmttBar, + 'bins': error_dSig_dmttBar, + } with open('data_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('kinematics_dSig_dmttBar_norm.yaml', 'w') as file: - yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_dSig_dmttBar_norm_yaml, file, sort_keys=False) with open('uncertainties_dSig_dmttBar_norm.yaml', 'w') as file: yaml.dump(uncertainties_dSig_dmttBar_norm_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_13TEV_ETA/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_13TEV_ETA/filter.py index 5952736382..9c3338e1db 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_13TEV_ETA/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_13TEV_ETA/filter.py @@ -1,7 +1,6 @@ +from filter_utils import get_data_values, get_kinematics, get_systematics import yaml -from filter_utils import get_kinematics, get_data_values, get_systematics - def filter_CMS_W_13TEV_data_kinetic(figure): """ @@ -57,7 +56,7 @@ def filter_CMS_W_13TEV_uncertainties(observable, figure): errors = [] for sys in systematics: - + error_definitions[sys[0]['name']] = { "description": f"{sys[0]['name']}", "treatment": "ADD", @@ -92,4 +91,3 @@ def filter_CMS_W_13TEV_uncertainties(observable, figure): # WM data filter_CMS_W_13TEV_data_kinetic(figure="17b") filter_CMS_W_13TEV_uncertainties(observable="W-", figure="17b") - diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_13TEV_ETA/filter_utils.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_13TEV_ETA/filter_utils.py index 53cb75104b..494784a178 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_13TEV_ETA/filter_utils.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_13TEV_ETA/filter_utils.py @@ -1,6 +1,6 @@ -import yaml -import uproot import numpy as np +import uproot +import yaml def get_kinematics(version, figure): @@ -131,15 +131,13 @@ def get_systematics(observable, version, figure): submatrix_array = np.array(submatrix) # Get Luminosity covariance matrix - if observable=="W+": + if observable == "W+": with open("rawdata/HEPData-ins1810913-v1-Impacts_Figure_A23a.yaml", "r") as file: impacts = yaml.safe_load(file) - elif observable=="W-": + elif observable == "W-": with open("rawdata/HEPData-ins1810913-v1-Impacts_Figure_A23b.yaml", "r") as file: impacts = yaml.safe_load(file) - - lumi_unc = np.array([val['value'] for val in impacts['dependent_variables'][2]['values']]) hepdata_table = f"rawdata/HEPData-ins1810913-v{version}-Figure_{figure}.yaml" @@ -153,12 +151,12 @@ def get_systematics(observable, version, figure): lumi_unc *= values / 100 lumi_covmat = lumi_unc[:, np.newaxis] @ lumi_unc[:, np.newaxis].T - artificial_uncertainties = np.real(decompose_covmat(lumi_covmat+submatrix_array)) - + artificial_uncertainties = np.real(decompose_covmat(lumi_covmat + submatrix_array)) + uncertainties = [] for i, unc in enumerate(artificial_uncertainties.T): - + name = f"artificial_uncertainty_{i}" values = [unc[i] for i in range(len(unc))] uncertainties.append([{"name": name, "values": values}]) diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py index 2df2cee9f2..8397558851 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_ELECTRON/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV SQRT_S = 7_000.0 # GeV @@ -231,9 +231,7 @@ def format_uncertainties(uncs: dict, artunc: np.ndarray) -> list: return combined_errors -def dump_commondata( - kinematics: list, data: list, errors: list, nb_syscorr: int -) -> None: +def dump_commondata(kinematics: list, data: list, errors: list, nb_syscorr: int) -> None: """Function that generates and writes the commondata files. Parameters @@ -270,11 +268,7 @@ def dump_commondata( yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter() -> None: @@ -301,27 +295,21 @@ def main_filter() -> None: yaml_content = load_yaml(table_id=tabid, version=version) # Extract the kinematic, data, and uncertainties - kinematics = get_kinematics( - yaml_content, bin_index, MAP_TABLE[tabid] - ) + kinematics = get_kinematics(yaml_content, bin_index, MAP_TABLE[tabid]) data_central = get_data_values(yaml_content, bin_index, indx=idx) uncertainties = get_errors(yaml_content, bin_index, indx=idx) # Collect all the results from different tables comb_kins += kinematics comb_data += data_central - comb_covmat += read_corrmatrix( - NB_POINTS[nbp_idx], MAP_TAB_UNC[tabid] - ) + comb_covmat += read_corrmatrix(NB_POINTS[nbp_idx], MAP_TAB_UNC[tabid]) combined_errors.append(uncertainties) nbp_idx += 1 errors_combined = concatenate_dicts(combined_errors) # Compute the Artifical Systematics from CovMat - artunc = generate_artificial_unc( - ndata=nbpoints, covmat_list=comb_covmat, no_of_norm_mat=0 - ) + artunc = generate_artificial_unc(ndata=nbpoints, covmat_list=comb_covmat, no_of_norm_mat=0) errors = format_uncertainties(errors_combined, artunc) # Generate all the necessary files diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py index bcedf2f0e2..d1bbb8538d 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_7TEV_MUON/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV SQRT_S = 7_000.0 # GeV @@ -158,9 +158,7 @@ def read_corrmatrix(nb_datapoints: int) -> np.ndarray: """ corrmat = pd.read_csv( - "./rawdata/covmat.corr", - names=[f'{i}' for i in range(nb_datapoints)], - delim_whitespace=True, + "./rawdata/covmat.corr", names=[f'{i}' for i in range(nb_datapoints)], delim_whitespace=True ) return corrmat.iloc[:, :].values @@ -254,9 +252,7 @@ def format_uncertainties(uncs: dict, artunc: np.ndarray) -> list: return combined_errors -def dump_commondata( - kinematics: list, data: list, errors: list, nb_syscorr: int -) -> None: +def dump_commondata(kinematics: list, data: list, errors: list, nb_syscorr: int) -> None: """Function that generates and writes the commondata files. Parameters @@ -293,11 +289,7 @@ def dump_commondata( yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter() -> None: @@ -324,9 +316,7 @@ def main_filter() -> None: yaml_content = load_yaml(table_id=tabid, version=version) # Extract the kinematic, data, and uncertainties - kinematics = get_kinematics( - yaml_content, bin_index, MAP_TABLE[tabid] - ) + kinematics = get_kinematics(yaml_content, bin_index, MAP_TABLE[tabid]) data_central = get_data_values(yaml_content, bin_index, indx=idx) uncertainties = get_errors(yaml_content, bin_index, indx=idx) @@ -341,9 +331,7 @@ def main_filter() -> None: # Compute the Artifical Systematics from CovMat corrmat = read_corrmatrix(nb_datapoints=nbpoints) covmat = multiply_syst(corrmat, errors_combined["sys_corr"]) - artunc = generate_artificial_unc( - ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0 - ) + artunc = generate_artificial_unc(ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0) errors = format_uncertainties(errors_combined, artunc) # Generate all the necessary files diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py index 802b2e6905..ba34402178 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_WPWM_8TEV_MUON/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - MW_VALUE = 80.398 # GeV SQRT_S = 8_000.0 # GeV NORM_FACTOR = 1e3 # Convert pb -> fb @@ -117,9 +117,7 @@ def get_data_values(hepdata: dict, bin_index: list, indx: int = 0) -> list: return [NORM_FACTOR * central[i]["value"] for i in bin_index] -def get_errors( - hepdata: dict, bin_index: list, central: list, indx: int = 0 -) -> dict: +def get_errors(hepdata: dict, bin_index: list, central: list, indx: int = 0) -> dict: """Extract the error values from the HepData yaml file. Parameters @@ -235,9 +233,7 @@ def concatenate_dicts(multidict: list[dict]) -> dict: return new_dict -def format_uncertainties( - uncs: dict, artunc_syst: np.ndarray, artunc_stat: np.ndarray -) -> list: +def format_uncertainties(uncs: dict, artunc_syst: np.ndarray, artunc_stat: np.ndarray) -> list: """Format the uncertainties to be dumped into the yaml file. Parameters @@ -278,11 +274,7 @@ def format_uncertainties( def dump_commondata( - kinematics: list, - data: list, - errors: list, - nb_syscorr: int, - nb_statcorr: int, + kinematics: list, data: list, errors: list, nb_syscorr: int, nb_statcorr: int ) -> None: """Function that generates and writes the commondata files. @@ -335,11 +327,7 @@ def dump_commondata( yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter() -> None: @@ -372,13 +360,9 @@ def main_filter() -> None: yaml_content = load_yaml(table_id=tabid, version=version) # Extract the kinematic, data, and uncertainties - kinematics = get_kinematics( - yaml_content, bin_index, MAP_TABLE[tabid] - ) + kinematics = get_kinematics(yaml_content, bin_index, MAP_TABLE[tabid]) data_central = get_data_values(yaml_content, bin_index, indx=idx) - uncertainties = get_errors( - yaml_content, bin_index, data_central, indx=idx - ) + uncertainties = get_errors(yaml_content, bin_index, data_central, indx=idx) # Collect all the results from different tables comb_kins += kinematics @@ -396,9 +380,7 @@ def main_filter() -> None: stat_corrmat = read_corrmatrix(nbpoints, unc_hepdata=SYST_CORRMAT_ID) process_systcorr = process_corrmat(syst_corrmat, errors_combined["stat"]) - process_statcorr = process_corrmat( - stat_corrmat, errors_combined["sys_corr"] - ) + process_statcorr = process_corrmat(stat_corrmat, errors_combined["sys_corr"]) # Compute the Artifical Statistical and Systematics from CovMats artunc_syst = generate_artificial_unc( @@ -410,13 +392,7 @@ def main_filter() -> None: errors = format_uncertainties(errors_combined, artunc_syst, artunc_stat) # Generate all the necessary files - dump_commondata( - comb_kins, - comb_data, - errors, - artunc_syst.shape[-1], - artunc_stat.shape[-1], - ) + dump_commondata(comb_kins, comb_data, errors, artunc_syst.shape[-1], artunc_stat.shape[-1]) return diff --git a/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py index 2a7a15d6e4..040b44911f 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/CMS_Z0_7TEV_DIMUON/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - # MZ_VALUE = 91.1876 # GeV # MW_VALUE = 80.398 # GeV # NORM_FACTOR = 1e-2 # Correct for Units @@ -30,9 +30,7 @@ def load_rawdata() -> pd.DataFrame: """ return pd.read_csv( - "./rawdata/CMS-DY2D11-ABS.data", - delim_whitespace=True, - names=['y', 'M', 'sigma'], + "./rawdata/CMS-DY2D11-ABS.data", delim_whitespace=True, names=['y', 'M', 'sigma'] ) @@ -55,9 +53,7 @@ def read_metadata() -> tuple[int, int, list]: return version, nb_datapoints, tables -def get_kinematics( - hepdata: pd.DataFrame, bin_index: list, boson: str = "Z" -) -> list: +def get_kinematics(hepdata: pd.DataFrame, bin_index: list, boson: str = "Z") -> list: """Read the version and list of tables from metadata. Parameters @@ -89,9 +85,7 @@ def get_kinematics( return kinematics -def get_data_values( - hepdata: pd.DataFrame, bin_index: list, indx: int = 0 -) -> list: +def get_data_values(hepdata: pd.DataFrame, bin_index: list, indx: int = 0) -> list: """Extract the central values from the HepData yaml file. Parameters @@ -129,9 +123,7 @@ def read_corrmatrix(nb_datapoints: int) -> np.ndarray: entries of the corr/cov-mat as an array """ - df_corrmat = pd.read_csv( - "./rawdata/covmat.corr", delim_whitespace=True, header=None - ) + df_corrmat = pd.read_csv("./rawdata/covmat.corr", delim_whitespace=True, header=None) corrmat = df_corrmat.iloc[:, 2].values return corrmat.reshape(nb_datapoints, nb_datapoints) @@ -226,9 +218,7 @@ def format_uncertainties(artunc: np.ndarray, central: list) -> list: return combined_errors -def dump_commondata( - kinematics: list, data: list, errors: list, nb_syscorr: int -) -> None: +def dump_commondata(kinematics: list, data: list, errors: list, nb_syscorr: int) -> None: """Function that generates and writes the commondata files. Parameters @@ -271,11 +261,7 @@ def dump_commondata( yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter() -> None: diff --git a/nnpdf_data/nnpdf_data/new_commondata/COMPASS15_NC_NOTFIXED_MUD/filter.py b/nnpdf_data/nnpdf_data/new_commondata/COMPASS15_NC_NOTFIXED_MUD/filter.py index ac80f85f61..953512b38e 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/COMPASS15_NC_NOTFIXED_MUD/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/COMPASS15_NC_NOTFIXED_MUD/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -77,23 +78,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/COMPASS15_NC_NOTFIXED_MUP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/COMPASS15_NC_NOTFIXED_MUP/filter.py index 75206f3264..a0457b10a9 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/COMPASS15_NC_NOTFIXED_MUP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/COMPASS15_NC_NOTFIXED_MUP/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -77,23 +78,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/E142_NC_NOTFIXED_EN/filter.py b/nnpdf_data/nnpdf_data/new_commondata/E142_NC_NOTFIXED_EN/filter.py index 3d516be61e..db424387ee 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/E142_NC_NOTFIXED_EN/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/E142_NC_NOTFIXED_EN/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -73,23 +74,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/E143_NC_NOTFIXED_ED/filter.py b/nnpdf_data/nnpdf_data/new_commondata/E143_NC_NOTFIXED_ED/filter.py index ce141bfb4b..57b6bfff85 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/E143_NC_NOTFIXED_ED/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/E143_NC_NOTFIXED_ED/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -87,16 +88,8 @@ def write_data(df): error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, "sys_beam": { "description": "systematic uncertainty due to beam Normalization", "treatment": "MULT", diff --git a/nnpdf_data/nnpdf_data/new_commondata/E143_NC_NOTFIXED_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/E143_NC_NOTFIXED_EP/filter.py index efcf322331..4384b6ee12 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/E143_NC_NOTFIXED_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/E143_NC_NOTFIXED_EP/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -85,16 +86,8 @@ def write_data(df): error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, "sys_beam": { "description": "systematic uncertainty due to beam Normalization", "treatment": "MULT", diff --git a/nnpdf_data/nnpdf_data/new_commondata/E154_NC_9GEV_EN/filter.py b/nnpdf_data/nnpdf_data/new_commondata/E154_NC_9GEV_EN/filter.py index cc4f6ea94a..ffecc110e4 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/E154_NC_9GEV_EN/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/E154_NC_9GEV_EN/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -77,23 +78,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/E155_NC_9GEV_EN/filter.py b/nnpdf_data/nnpdf_data/new_commondata/E155_NC_9GEV_EN/filter.py index 711f6cb6f6..69d2f4483b 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/E155_NC_9GEV_EN/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/E155_NC_9GEV_EN/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -67,16 +68,8 @@ def write_data(df): error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, "sys_norm": { "description": "systematic uncertainty due to Normalization", "treatment": "MULT", diff --git a/nnpdf_data/nnpdf_data/new_commondata/E155_NC_9GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/E155_NC_9GEV_EP/filter.py index f81444be45..b774e3f1a6 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/E155_NC_9GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/E155_NC_9GEV_EP/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -67,16 +68,8 @@ def write_data(df): error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, "sys_norm": { "description": "systematic uncertainty due to Normalization", "treatment": "MULT", diff --git a/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_211GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_211GEV_EP/filter.py index 0f8c4d6ab1..59d5f08b27 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_211GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_211GEV_EP/filter.py @@ -1,10 +1,10 @@ -import yaml -import numpy as np -import pandas as pd - from pathlib import Path from typing import Optional, Union +import numpy as np +import pandas as pd +import yaml + np.random.seed(1234567890) @@ -24,10 +24,7 @@ def read_txt_data(path_txt: Path) -> pd.DataFrame: """ colnames = ["x", "Q2", "abs"] return pd.read_csv( - path_txt, - delim_whitespace=True, - names=colnames, - usecols=[i for i in range(len(colnames))], + path_txt, delim_whitespace=True, names=colnames, usecols=[i for i in range(len(colnames))] ) @@ -57,9 +54,7 @@ def fluctuate_data(central: np.ndarray, abserr: np.ndarray) -> np.ndarray: def write_data( - df: pd.DataFrame, - abserr: Optional[Union[np.ndarray, None]] = None, - add_fluctuate: bool = False, + df: pd.DataFrame, abserr: Optional[Union[np.ndarray, None]] = None, add_fluctuate: bool = False ) -> None: """Write the input kinematics, central values, and uncertainties into the new commondata format. @@ -104,16 +99,8 @@ def write_data( errors = [{"stat": float(d["abs"]), "sys": 0.0} for _, d in df.iterrows()] error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": errors} @@ -126,4 +113,4 @@ def write_data( df = read_txt_data(input_txt) cv_preds = read_cvs() fluctuated_cv = fluctuate_data(cv_preds, df["abs"].values) - write_data(df, abserr=fluctuated_cv, add_fluctuate=True) \ No newline at end of file + write_data(df, abserr=fluctuated_cv, add_fluctuate=True) diff --git a/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_43GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_43GEV_EP/filter.py index dd8bbaa256..07cb7acf2b 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_43GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_43GEV_EP/filter.py @@ -1,10 +1,10 @@ -import yaml -import numpy as np -import pandas as pd - from pathlib import Path from typing import Optional, Union +import numpy as np +import pandas as pd +import yaml + np.random.seed(1234567890) @@ -24,10 +24,7 @@ def read_txt_data(path_txt: Path) -> pd.DataFrame: """ colnames = ["x", "Q2", "abs"] return pd.read_csv( - path_txt, - delim_whitespace=True, - names=colnames, - usecols=[i for i in range(len(colnames))], + path_txt, delim_whitespace=True, names=colnames, usecols=[i for i in range(len(colnames))] ) @@ -57,9 +54,7 @@ def fluctuate_data(central: np.ndarray, abserr: np.ndarray) -> np.ndarray: def write_data( - df: pd.DataFrame, - abserr: Optional[Union[np.ndarray, None]] = None, - add_fluctuate: bool = False, + df: pd.DataFrame, abserr: Optional[Union[np.ndarray, None]] = None, add_fluctuate: bool = False ) -> None: """Write the input kinematics, central values, and uncertainties into the new commondata format. @@ -104,16 +99,8 @@ def write_data( errors = [{"stat": float(d["abs"]), "sys": 0.0} for _, d in df.iterrows()] error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": errors} @@ -126,4 +113,4 @@ def write_data( df = read_txt_data(input_txt) cv_preds = read_cvs() fluctuated_cv = fluctuate_data(cv_preds, df["abs"].values) - write_data(df, abserr=fluctuated_cv, add_fluctuate=True) \ No newline at end of file + write_data(df, abserr=fluctuated_cv, add_fluctuate=True) diff --git a/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_67GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_67GEV_EP/filter.py index 2f2ffc2faa..0af0badc4e 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_67GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/EIC_NC_67GEV_EP/filter.py @@ -1,10 +1,10 @@ -import yaml -import numpy as np -import pandas as pd - from pathlib import Path from typing import Optional, Union +import numpy as np +import pandas as pd +import yaml + np.random.seed(1234567890) @@ -24,10 +24,7 @@ def read_txt_data(path_txt: Path) -> pd.DataFrame: """ colnames = ["x", "Q2", "abs"] return pd.read_csv( - path_txt, - delim_whitespace=True, - names=colnames, - usecols=[i for i in range(len(colnames))], + path_txt, delim_whitespace=True, names=colnames, usecols=[i for i in range(len(colnames))] ) @@ -57,9 +54,7 @@ def fluctuate_data(central: np.ndarray, abserr: np.ndarray) -> np.ndarray: def write_data( - df: pd.DataFrame, - abserr: Optional[Union[np.ndarray, None]] = None, - add_fluctuate: bool = False, + df: pd.DataFrame, abserr: Optional[Union[np.ndarray, None]] = None, add_fluctuate: bool = False ) -> None: """Write the input kinematics, central values, and uncertainties into the new commondata format. @@ -104,16 +99,8 @@ def write_data( errors = [{"stat": float(d["abs"]), "sys": 0.0} for _, d in df.iterrows()] error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": errors} @@ -126,4 +113,4 @@ def write_data( df = read_txt_data(input_txt) cv_preds = read_cvs() fluctuated_cv = fluctuate_data(cv_preds, df["abs"].values) - write_data(df, abserr=fluctuated_cv, add_fluctuate=True) \ No newline at end of file + write_data(df, abserr=fluctuated_cv, add_fluctuate=True) diff --git a/nnpdf_data/nnpdf_data/new_commondata/EIcC_NC_15GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/EIcC_NC_15GEV_EP/filter.py index 8ac70405fd..8e38b16b0a 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/EIcC_NC_15GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/EIcC_NC_15GEV_EP/filter.py @@ -1,10 +1,10 @@ -import yaml -import numpy as np -import pandas as pd - from pathlib import Path from typing import Optional, Union +import numpy as np +import pandas as pd +import yaml + np.random.seed(1234567890) @@ -24,10 +24,7 @@ def read_txt_data(path_txt: Path) -> pd.DataFrame: """ colnames = ["x", "Q2", "abs"] return pd.read_csv( - path_txt, - delim_whitespace=True, - names=colnames, - usecols=[i for i in range(len(colnames))], + path_txt, delim_whitespace=True, names=colnames, usecols=[i for i in range(len(colnames))] ) @@ -57,9 +54,7 @@ def fluctuate_data(central: np.ndarray, abserr: np.ndarray) -> np.ndarray: def write_data( - df: pd.DataFrame, - abserr: Optional[Union[np.ndarray, None]] = None, - add_fluctuate: bool = False, + df: pd.DataFrame, abserr: Optional[Union[np.ndarray, None]] = None, add_fluctuate: bool = False ) -> None: """Write the input kinematics, central values, and uncertainties into the new commondata format. @@ -104,16 +99,8 @@ def write_data( errors = [{"stat": float(d["abs"]), "sys": 0.0} for _, d in df.iterrows()] error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": errors} @@ -126,4 +113,4 @@ def write_data( df = read_txt_data(input_txt) cv_preds = read_cvs() fluctuated_cv = fluctuate_data(cv_preds, df["abs"].values) - write_data(df, abserr=fluctuated_cv, add_fluctuate=True) \ No newline at end of file + write_data(df, abserr=fluctuated_cv, add_fluctuate=True) diff --git a/nnpdf_data/nnpdf_data/new_commondata/EIcC_NC_22GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/EIcC_NC_22GEV_EP/filter.py index 2d3c6134d3..9c50341898 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/EIcC_NC_22GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/EIcC_NC_22GEV_EP/filter.py @@ -1,10 +1,10 @@ -import yaml -import numpy as np -import pandas as pd - from pathlib import Path from typing import Optional, Union +import numpy as np +import pandas as pd +import yaml + np.random.seed(1234567890) @@ -24,10 +24,7 @@ def read_txt_data(path_txt: Path) -> pd.DataFrame: """ colnames = ["x", "Q2", "abs"] return pd.read_csv( - path_txt, - delim_whitespace=True, - names=colnames, - usecols=[i for i in range(len(colnames))], + path_txt, delim_whitespace=True, names=colnames, usecols=[i for i in range(len(colnames))] ) @@ -57,9 +54,7 @@ def fluctuate_data(central: np.ndarray, abserr: np.ndarray) -> np.ndarray: def write_data( - df: pd.DataFrame, - abserr: Optional[Union[np.ndarray, None]] = None, - add_fluctuate: bool = False, + df: pd.DataFrame, abserr: Optional[Union[np.ndarray, None]] = None, add_fluctuate: bool = False ) -> None: """Write the input kinematics, central values, and uncertainties into the new commondata format. @@ -104,16 +99,8 @@ def write_data( errors = [{"stat": float(d["abs"]), "sys": 0.0} for _, d in df.iterrows()] error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": errors} @@ -126,4 +113,4 @@ def write_data( df = read_txt_data(input_txt) cv_preds = read_cvs() fluctuated_cv = fluctuate_data(cv_preds, df["abs"].values) - write_data(df, abserr=fluctuated_cv, add_fluctuate=True) \ No newline at end of file + write_data(df, abserr=fluctuated_cv, add_fluctuate=True) diff --git a/nnpdf_data/nnpdf_data/new_commondata/EMC_NC_NOTFIXED_MUP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/EMC_NC_NOTFIXED_MUP/filter.py index bdfa4f0f18..a813e035eb 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/EMC_NC_NOTFIXED_MUP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/EMC_NC_NOTFIXED_MUP/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -68,16 +69,8 @@ def write_data(df): error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, "sys_norm": { "description": "systematic uncertainty due to errors in polarisation and F2 value", "treatment": "MULT", diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py index 79e6f4eaba..736ea6b856 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py @@ -1,9 +1,10 @@ -import yaml import numpy -# use #1693 +import yaml + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta + def artunc(): with open('rawdata/data49.yaml', 'r') as file: @@ -16,7 +17,7 @@ def artunc(): errPercArr = [] dataArr = [] for i in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]: - hepdata_tables="rawdata/data"+str(i)+".yaml" + hepdata_tables = "rawdata/data" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) values = input['dependent_variables'][0]['values'] @@ -25,7 +26,6 @@ def artunc(): errPercArr.append(errPerc) dataArr.append(float(values[j]['value'])) - errArr = [] for i in range(96): errArr.append(pta(errPercArr[i], dataArr[i])) @@ -34,8 +34,8 @@ def artunc(): artUnc = numpy.zeros((96, 96)) for i in range(96): - for j in range(i+1): - cmhap = (i * (i+1)) // 2 + j + for j in range(i + 1): + cmhap = (i * (i + 1)) // 2 + j if i == j: covMat[i][j] = corMatHalfArr[cmhap] * errArr[i] * errArr[j] else: @@ -50,8 +50,9 @@ def artunc(): return artUnc + def artunc_norm(): - + with open('rawdata/data50.yaml', 'r') as file: corMatFile = yaml.safe_load(file) @@ -62,7 +63,7 @@ def artunc_norm(): errPercArr = [] dataArr = [] for i in [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]: - hepdata_tables="rawdata/data"+str(i)+".yaml" + hepdata_tables = "rawdata/data" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) values = input['dependent_variables'][0]['values'] @@ -73,14 +74,14 @@ def artunc_norm(): errArr = [] for i in range(96): - errArr.append(pta(errPercArr[i], dataArr[i])) + errArr.append(pta(errPercArr[i], dataArr[i])) covMat = numpy.zeros((96, 96)) artUnc = numpy.zeros((96, 96)) for i in range(96): - for j in range(i+1): - cmhap = (i * (i+1)) // 2 + j + for j in range(i + 1): + cmhap = (i * (i + 1)) // 2 + j if i == j: covMat[i][j] = corMatHalfArr[cmhap] * errArr[i] * errArr[j] else: diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py index f08b857e32..7f19469816 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/filter.py @@ -1,9 +1,11 @@ +from math import sqrt + import artUnc import yaml -# use #1693 + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se -from math import sqrt + def processData(): with open('metadata.yaml', 'r') as file: @@ -30,7 +32,7 @@ def processData(): artUncMatr = artUnc.artunc() artUncMatr_norm = artUnc.artunc_norm() -# jet data + # jet data for i in tables: if i == 1: @@ -58,7 +60,7 @@ def processData(): Q2_min = 60 Q2_max = 80 - hepdata_tables="rawdata/data"+str(i)+".yaml" + hepdata_tables = "rawdata/data" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -69,53 +71,99 @@ def processData(): data_central_value = float(values[j]['value']) pT_max = input['independent_variables'][0]['values'][j]['high'] pT_min = input['independent_variables'][0]['values'][j]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max},'pT': {'min': pT_min, 'mid': None, 'max': pT_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + } kin.append(kin_value) value_delta = 0 error_value = {} for k in 0, 1, 5, 6, 7, 8, 9, 10, 11: if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']] = pta(values[j]['errors'][k]['symerror'], data_central_value) + error_value[values[j]['errors'][k]['label']] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)) + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value), + ) value_delta = value_delta + se_delta error_value[values[j]['errors'][k]['label']] = se_sigma for k in 2, 3, 4: if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']+'_1'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) - error_value[values[j]['errors'][k]['label']+'_2'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_1'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value)/sqrt(2), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)/sqrt(2)) + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value) + / sqrt(2), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value) + / sqrt(2), + ) value_delta = value_delta + se_delta + se_delta - error_value[values[j]['errors'][k]['label']+'_1'] = se_sigma - error_value[values[j]['errors'][k]['label']+'_2'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_1'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_2'] = se_sigma data_central_value = data_central_value + value_delta data_central.append(data_central_value) for k in range(96): - error_value['ArtUnc_'+str(k+1)] = float(artUncMatr[j][k]) + error_value['ArtUnc_' + str(k + 1)] = float(artUncMatr[j][k]) error_value['stat'] = 0 error.append(error_value) error_definition = { - 'stat':{'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Uncorr':{'description': 'systematic uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Model_1':{'description': 'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Model_2':{'description': 'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ModelRW_1':{'description': 'reweighting uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'ModelRW_2':{'description': 'reweighting uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'JES_1':{'description': 'jet energy scale uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'JES_2':{'description': 'jet energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'RCES':{'description': 'remaining cluster energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ElEn':{'description': 'electron energy uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ElTh':{'description': 'electron theta uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'Lumi':{'description': 'luminosity', 'treatment': 'MULT', 'type': 'CORR'}, - 'LArN':{'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, - 'StatMC':{'description': 'MC statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'RadErr':{'description': 'radiative uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} + 'stat': {'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Uncorr': {'description': 'systematic uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_1': {'description': 'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_2': {'description': 'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'ModelRW_1': { + 'description': 'reweighting uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'ModelRW_2': { + 'description': 'reweighting uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'JES_1': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'JES_2': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'RCES': { + 'description': 'remaining cluster energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'ElEn': {'description': 'electron energy uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'ElTh': {'description': 'electron theta uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'Lumi': {'description': 'luminosity', 'treatment': 'MULT', 'type': 'CORR'}, + 'LArN': {'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, + 'StatMC': { + 'description': 'MC statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'RadErr': {'description': 'radiative uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, } for i in range(96): - error_definition['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'H1JETS161103421unc'+str(i+1)} + error_definition['ArtUnc_' + str(i + 1)] = { + 'description': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'H1JETS161103421unc' + str(i + 1), + } data_central_yaml = {'data_central': data_central} kinematics_yaml = {'bins': kin} @@ -130,7 +178,7 @@ def processData(): with open('uncertainties.yaml', 'w') as file: yaml.dump(uncertainties_yaml, file, sort_keys=False) -# jet_norm data + # jet_norm data for i in tables_norm: if i == 25: @@ -158,7 +206,7 @@ def processData(): Q2_min = 60 Q2_max = 80 - hepdata_tables="rawdata/data"+str(i)+".yaml" + hepdata_tables = "rawdata/data" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -169,53 +217,99 @@ def processData(): data_central_value = float(values[j]['value']) pT_max = input['independent_variables'][0]['values'][j]['high'] pT_min = input['independent_variables'][0]['values'][j]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max},'pT': {'min': pT_min, 'mid': None, 'max': pT_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + } kin_norm.append(kin_value) value_delta = 0 error_value = {} for k in 0, 1, 5, 6, 7, 8, 9, 10, 11: if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']] = pta(values[j]['errors'][k]['symerror'], data_central_value) + error_value[values[j]['errors'][k]['label']] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)) + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value), + ) value_delta = value_delta + se_delta error_value[values[j]['errors'][k]['label']] = se_sigma for k in 2, 3, 4: if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']+'_1'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) - error_value[values[j]['errors'][k]['label']+'_2'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_1'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value)/sqrt(2), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)/sqrt(2)) + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value) + / sqrt(2), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value) + / sqrt(2), + ) value_delta = value_delta + se_delta + se_delta - error_value[values[j]['errors'][k]['label']+'_1'] = se_sigma - error_value[values[j]['errors'][k]['label']+'_2'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_1'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_2'] = se_sigma data_central_value = data_central_value + value_delta data_central_norm.append(data_central_value) for k in range(96): - error_value['ArtUnc_'+str(k+1)] = float(artUncMatr_norm[j][k]) + error_value['ArtUnc_' + str(k + 1)] = float(artUncMatr_norm[j][k]) error_value['stat'] = 0 error_norm.append(error_value) error_definition_norm = { - 'stat':{'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Uncorr':{'description': 'systematic uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Model_1':{'description': 'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Model_2':{'description': 'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ModelRW_1':{'description': 'reweighting uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'ModelRW_2':{'description': 'reweighting uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'JES_1':{'description': 'jet energy scale uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'JES_2':{'description': 'jet energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'RCES':{'description': 'remaining cluster energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ElEn':{'description': 'electron energy uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ElTh':{'description': 'electron theta uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'Lumi':{'description': 'luminosity', 'treatment': 'MULT', 'type': 'CORR'}, - 'LArN':{'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, - 'StatMC':{'description': 'MC statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'RadErr':{'description': 'radiative uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} + 'stat': {'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Uncorr': {'description': 'systematic uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_1': {'description': 'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_2': {'description': 'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'ModelRW_1': { + 'description': 'reweighting uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'ModelRW_2': { + 'description': 'reweighting uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'JES_1': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'JES_2': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'RCES': { + 'description': 'remaining cluster energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'ElEn': {'description': 'electron energy uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'ElTh': {'description': 'electron theta uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'Lumi': {'description': 'luminosity', 'treatment': 'MULT', 'type': 'CORR'}, + 'LArN': {'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, + 'StatMC': { + 'description': 'MC statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'RadErr': {'description': 'radiative uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, } for i in range(96): - error_definition_norm['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'H1JETS161103421NORMunc'+str(i+1)} + error_definition_norm['ArtUnc_' + str(i + 1)] = { + 'description': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'H1JETS161103421NORMunc' + str(i + 1), + } data_central_norm_yaml = {'data_central': data_central_norm} kinematics_norm_yaml = {'bins': kin_norm} @@ -230,12 +324,12 @@ def processData(): with open('uncertainties_norm.yaml', 'w') as file: yaml.dump(uncertainties_norm_yaml, file, sort_keys=False) -# jet_highQ2 data + # jet_highQ2 data - hepdata_tables="rawdata/data51.yaml" + hepdata_tables = "rawdata/data51.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + sqrts = float(input['dependent_variables'][0]['qualifiers'][2]['value']) pT_min = 5 pT_max = 7 @@ -245,49 +339,80 @@ def processData(): data_central_value = float(values[i]['value']) Q2_max = input['independent_variables'][0]['values'][i]['high'] Q2_min = input['independent_variables'][0]['values'][i]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max},'pT': {'min': pT_min, 'mid': None, 'max': pT_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + } kin_highQ2.append(kin_value) value_delta = 0 error_value = {} for k in 0, 3, 4, 5, 6, 7, 8: - if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']] = pta(values[j]['errors'][k]['symerror'], data_central_value) - else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)) - value_delta = value_delta + se_delta - error_value[values[j]['errors'][k]['label']] = se_sigma + if 'symerror' in values[j]['errors'][k]: + error_value[values[j]['errors'][k]['label']] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) + else: + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value), + ) + value_delta = value_delta + se_delta + error_value[values[j]['errors'][k]['label']] = se_sigma for k in 1, 2: if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']+'_1'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) - error_value[values[j]['errors'][k]['label']+'_2'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_1'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value)/sqrt(2), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)/sqrt(2)) + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value) / sqrt(2), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value) / sqrt(2), + ) value_delta = value_delta + se_delta + se_delta - error_value[values[j]['errors'][k]['label']+'_1'] = se_sigma - error_value[values[j]['errors'][k]['label']+'_2'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_1'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_2'] = se_sigma data_central_value = data_central_value + value_delta data_central_highQ2.append(data_central_value) error_highQ2.append(error_value) - + error_definition_highQ2 = { - 'stat':{'description':'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR' }, - 'Model_1':{'description':'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR' }, - 'Model_2':{'description':'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR' }, - 'JES_1':{'description':'jet energy scale uncertainty', 'treatment': 'ADD', 'type': 'UNCORR' }, - 'JES_2':{'description':'jet energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR' }, - 'RCES':{'description':'remaining cluster energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR' }, - '$E_{e^\prime}$':{'description':'electron energy', 'treatment': 'MULT', 'type': 'CORR' }, - '$\theta_{e^\prime}$':{'description':'electron theta', 'treatment': 'MULT', 'type': 'CORR' }, - 'ID(e)':{'description': 'electron identification', 'treatment': 'MULT', 'type': 'CORR'}, - 'LArNoise':{'description':'lar noice', 'treatment': 'MULT', 'type': 'CORR' }, - 'Norm':{'description': 'normalization uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + 'stat': {'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_1': {'description': 'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_2': {'description': 'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'JES_1': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'JES_2': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'RCES': { + 'description': 'remaining cluster energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + '$E_{e^\prime}$': {'description': 'electron energy', 'treatment': 'MULT', 'type': 'CORR'}, + '$\theta_{e^\prime}$': { + 'description': 'electron theta', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'ID(e)': {'description': 'electron identification', 'treatment': 'MULT', 'type': 'CORR'}, + 'LArNoise': {'description': 'lar noice', 'treatment': 'MULT', 'type': 'CORR'}, + 'Norm': {'description': 'normalization uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, } data_central_highQ2_yaml = {'data_central': data_central_highQ2} kinematics_highQ2_yaml = {'bins': kin_highQ2} uncertainties_highQ2_yaml = {'definitions': error_definition_highQ2, 'bins': error_highQ2} - with open('data_highQ2.yaml', 'w') as file: yaml.dump(data_central_highQ2_yaml, file, sort_keys=False) @@ -297,12 +422,12 @@ def processData(): with open('uncertainties_highQ2.yaml', 'w') as file: yaml.dump(uncertainties_highQ2_yaml, file, sort_keys=False) -# jet_highQ2_norm data + # jet_highQ2_norm data - hepdata_tables="rawdata/data52.yaml" + hepdata_tables = "rawdata/data52.yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) - + sqrts = float(input['dependent_variables'][0]['qualifiers'][2]['value']) pT_min = 5 pT_max = 7 @@ -312,46 +437,80 @@ def processData(): data_central_value = float(values[i]['value']) Q2_max = input['independent_variables'][0]['values'][i]['high'] Q2_min = input['independent_variables'][0]['values'][i]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max},'pT': {'min': pT_min, 'mid': None, 'max': pT_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + } kin_highQ2_norm.append(kin_value) value_delta = 0 error_value = {} for k in 0, 3, 4, 5, 6: - if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']] = pta(values[j]['errors'][k]['symerror'], data_central_value) - else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)) - value_delta = value_delta + se_delta - error_value[values[j]['errors'][k]['label']] = se_sigma + if 'symerror' in values[j]['errors'][k]: + error_value[values[j]['errors'][k]['label']] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) + else: + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value), + ) + value_delta = value_delta + se_delta + error_value[values[j]['errors'][k]['label']] = se_sigma for k in 1, 2: if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']+'_1'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) - error_value[values[j]['errors'][k]['label']+'_2'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_1'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value)/sqrt(2), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)/sqrt(2)) + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value) / sqrt(2), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value) / sqrt(2), + ) value_delta = value_delta + se_delta + se_delta - error_value[values[j]['errors'][k]['label']+'_1'] = se_sigma - error_value[values[j]['errors'][k]['label']+'_2'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_1'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_2'] = se_sigma data_central_value = data_central_value + value_delta data_central_highQ2_norm.append(data_central_value) error_highQ2_norm.append(error_value) - + error_definition_highQ2_norm = { - 'stat':{'description':'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR' }, - 'Model_1':{'description':'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR' }, - 'Model_2':{'description':'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR' }, - 'JES_1':{'description':'jet energy scale uncertainty', 'treatment': 'ADD', 'type': 'UNCORR' }, - 'JES_2':{'description':'jet energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR' }, - 'RCES':{'description':'remaining cluster energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR' }, - '$E_{e^\prime}$':{'description':'electron energy', 'treatment': 'MULT', 'type': 'CORR' }, - '$\theta_{e^\prime}$':{'description':'electron theta', 'treatment': 'MULT', 'type': 'CORR' }, - 'LArNoise':{'description':'lar noice', 'treatment': 'MULT', 'type': 'CORR' } + 'stat': {'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_1': {'description': 'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_2': {'description': 'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'JES_1': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'JES_2': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'RCES': { + 'description': 'remaining cluster energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + '$E_{e^\prime}$': {'description': 'electron energy', 'treatment': 'MULT', 'type': 'CORR'}, + '$\theta_{e^\prime}$': { + 'description': 'electron theta', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'LArNoise': {'description': 'lar noice', 'treatment': 'MULT', 'type': 'CORR'}, } data_central_highQ2_norm_yaml = {'data_central': data_central_highQ2_norm} kinematics_highQ2_norm_yaml = {'bins': kin_highQ2_norm} - uncertainties_highQ2_norm_yaml = {'definitions': error_definition_highQ2_norm, 'bins': error_highQ2_norm} - + uncertainties_highQ2_norm_yaml = { + 'definitions': error_definition_highQ2_norm, + 'bins': error_highQ2_norm, + } with open('data_highQ2_norm.yaml', 'w') as file: yaml.dump(data_central_highQ2_norm_yaml, file, sort_keys=False) @@ -362,4 +521,5 @@ def processData(): with open('uncertainties_highQ2_norm.yaml', 'w') as file: yaml.dump(uncertainties_highQ2_norm_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py index ae18d9d818..00505782ec 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/filter.py @@ -1,7 +1,7 @@ +from manual_impl import artunc, jet_data, jet_sys import yaml -# use #1693 + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta -from manual_impl import jet_data, jet_sys, artunc def processData(): @@ -21,9 +21,9 @@ def processData(): kin_norm = [] error_norm = [] -# jet data + # jet data - hepdata_tables="rawdata/Table"+str(tables[0])+".yaml" + hepdata_tables = "rawdata/Table" + str(tables[0]) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -38,49 +38,57 @@ def processData(): Q2_min = input['independent_variables'][0]['values'][i]['low'] pT_max = input['independent_variables'][1]['values'][i]['high'] pT_min = input['independent_variables'][1]['values'][i]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max},'pT': {'min': pT_min, 'mid': None, 'max': pT_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + } kin.append(kin_value) error_value = {} # error_value['stat'] = pta(values[i]['errors'][0]['symerror'], data_central_value) # error_value['sys'] = pta(values[i]['errors'][1]['symerror'], data_central_value) for j in range(len(jet_sys[i])): - error_value['Syst_'+str(j+1)] = jet_sys[i][j] + error_value['Syst_' + str(j + 1)] = jet_sys[i][j] for j in range(len(artunc[i])): - error_value['ArtUnc_'+str(j+1)] = artunc[i][j] + error_value['ArtUnc_' + str(j + 1)] = artunc[i][j] error.append(error_value) # error_definition = {'stat':{'description': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, 'sys':{'description': 'total systematic uncertainty', 'treatment':'MULT' , 'type': 'CORR'}} - error_definition = {'Syst_1':{'description': 'model', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_2':{'description': 'jes', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_3':{'description': 'jes', 'treatment': 'MULT', 'type': 'CORR'}, - 'Syst_4':{'description': 'rces', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_5':{'description': 'rces', 'treatment': 'MULT', 'type': 'CORR'}, - 'Syst_6':{'description': 'e_e', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_7':{'description': 'theta_e', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_8':{'description': 'ID_e', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_9':{'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, - 'Syst_10':{'description': 'norm', 'treatment': 'MULT', 'type': 'CORR'}} + error_definition = { + 'Syst_1': {'description': 'model', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_2': {'description': 'jes', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_3': {'description': 'jes', 'treatment': 'MULT', 'type': 'CORR'}, + 'Syst_4': {'description': 'rces', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_5': {'description': 'rces', 'treatment': 'MULT', 'type': 'CORR'}, + 'Syst_6': {'description': 'e_e', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_7': {'description': 'theta_e', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_8': {'description': 'ID_e', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_9': {'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, + 'Syst_10': {'description': 'norm', 'treatment': 'MULT', 'type': 'CORR'}, + } for i in range(48): - error_definition['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty ' + str(i+1), 'treatment': 'ADD', 'type': 'H1JETS14064709unc'+str(i+1)} - - + error_definition['ArtUnc_' + str(i + 1)] = { + 'description': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'H1JETS14064709unc' + str(i + 1), + } data_central_yaml = {'data_central': data_central} kinematics_yaml = {'bins': kin} uncertainties_yaml = {'definitions': error_definition, 'bins': error} with open('data.yaml', 'w') as file: - yaml.dump(data_central_yaml, file, sort_keys=False) + yaml.dump(data_central_yaml, file, sort_keys=False) with open('kinematics.yaml', 'w') as file: - yaml.dump(kinematics_yaml, file, sort_keys=False) + yaml.dump(kinematics_yaml, file, sort_keys=False) with open('uncertainties.yaml', 'w') as file: yaml.dump(uncertainties_yaml, file, sort_keys=False) - # jet_norm data + # jet_norm data - hepdata_tables="rawdata/Table"+str(tables_norm[0])+".yaml" + hepdata_tables = "rawdata/Table" + str(tables_norm[0]) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -94,26 +102,38 @@ def processData(): Q2_min = input['independent_variables'][0]['values'][i]['low'] pT_max = input['independent_variables'][1]['values'][i]['high'] pT_min = input['independent_variables'][1]['values'][i]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max},'pT': {'min': pT_min, 'mid': None, 'max': pT_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + } kin_norm.append(kin_value) error_value = {} error_value['stat'] = pta(values[i]['errors'][0]['symerror'], data_central_value) error_value['sys'] = pta(values[i]['errors'][1]['symerror'], data_central_value) error_norm.append(error_value) - error_definition_norm = {'stat':{'description': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, 'sys':{'description': 'total systematic uncertainty', 'treatment':'MULT' , 'type': 'CORR'}} + error_definition_norm = { + 'stat': { + 'description': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'sys': {'description': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + } data_central_norm_yaml = {'data_central': data_central_norm} kinematics_norm_yaml = {'bins': kin_norm} uncertainties_norm_yaml = {'definitions': error_definition_norm, 'bins': error_norm} with open('data_norm.yaml', 'w') as file: - yaml.dump(data_central_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_norm_yaml, file, sort_keys=False) with open('kinematics_norm.yaml', 'w') as file: - yaml.dump(kinematics_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_norm_yaml, file, sort_keys=False) with open('uncertainties_norm.yaml', 'w') as file: yaml.dump(uncertainties_norm_yaml, file, sort_keys=False) -processData() \ No newline at end of file + +processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py index d1554b3822..50e3f03421 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_351PB-1_DIF/manual_impl.py @@ -1,5 +1,5 @@ from math import sqrt -# use #1693 + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py index 79e6f4eaba..736ea6b856 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/artUnc.py @@ -1,9 +1,10 @@ -import yaml import numpy -# use #1693 +import yaml + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta + def artunc(): with open('rawdata/data49.yaml', 'r') as file: @@ -16,7 +17,7 @@ def artunc(): errPercArr = [] dataArr = [] for i in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]: - hepdata_tables="rawdata/data"+str(i)+".yaml" + hepdata_tables = "rawdata/data" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) values = input['dependent_variables'][0]['values'] @@ -25,7 +26,6 @@ def artunc(): errPercArr.append(errPerc) dataArr.append(float(values[j]['value'])) - errArr = [] for i in range(96): errArr.append(pta(errPercArr[i], dataArr[i])) @@ -34,8 +34,8 @@ def artunc(): artUnc = numpy.zeros((96, 96)) for i in range(96): - for j in range(i+1): - cmhap = (i * (i+1)) // 2 + j + for j in range(i + 1): + cmhap = (i * (i + 1)) // 2 + j if i == j: covMat[i][j] = corMatHalfArr[cmhap] * errArr[i] * errArr[j] else: @@ -50,8 +50,9 @@ def artunc(): return artUnc + def artunc_norm(): - + with open('rawdata/data50.yaml', 'r') as file: corMatFile = yaml.safe_load(file) @@ -62,7 +63,7 @@ def artunc_norm(): errPercArr = [] dataArr = [] for i in [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]: - hepdata_tables="rawdata/data"+str(i)+".yaml" + hepdata_tables = "rawdata/data" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) values = input['dependent_variables'][0]['values'] @@ -73,14 +74,14 @@ def artunc_norm(): errArr = [] for i in range(96): - errArr.append(pta(errPercArr[i], dataArr[i])) + errArr.append(pta(errPercArr[i], dataArr[i])) covMat = numpy.zeros((96, 96)) artUnc = numpy.zeros((96, 96)) for i in range(96): - for j in range(i+1): - cmhap = (i * (i+1)) // 2 + j + for j in range(i + 1): + cmhap = (i * (i + 1)) // 2 + j if i == j: covMat[i][j] = corMatHalfArr[cmhap] * errArr[i] * errArr[j] else: diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py index c5dbbd4e7f..ecf4dff877 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_290PB-1_DIF/filter.py @@ -1,9 +1,11 @@ +from math import sqrt + import artUnc import yaml -# use #1693 + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se -from math import sqrt + def processData(): with open('metadata.yaml', 'r') as file: @@ -22,7 +24,7 @@ def processData(): artUncMatr = artUnc.artunc() artUncMatr_norm = artUnc.artunc_norm() -# dijet data + # dijet data for i in tables: if i == 9: @@ -50,7 +52,7 @@ def processData(): Q2_min = 60 Q2_max = 80 - hepdata_tables="rawdata/data"+str(i)+".yaml" + hepdata_tables = "rawdata/data" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -61,53 +63,99 @@ def processData(): data_central_value = float(values[j]['value']) pT_max = input['independent_variables'][0]['values'][j]['high'] pT_min = input['independent_variables'][0]['values'][j]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max},'pT': {'min': pT_min, 'mid': None, 'max': pT_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + } kin.append(kin_value) value_delta = 0 error_value = {} for k in 0, 1, 5, 6, 7, 8, 9, 10, 11: if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']] = pta(values[j]['errors'][k]['symerror'], data_central_value) + error_value[values[j]['errors'][k]['label']] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)) + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value), + ) value_delta = value_delta + se_delta error_value[values[j]['errors'][k]['label']] = se_sigma for k in 2, 3, 4: if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']+'_1'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) - error_value[values[j]['errors'][k]['label']+'_2'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_1'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value)/sqrt(2), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)/sqrt(2)) + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value) + / sqrt(2), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value) + / sqrt(2), + ) value_delta = value_delta + se_delta + se_delta - error_value[values[j]['errors'][k]['label']+'_1'] = se_sigma - error_value[values[j]['errors'][k]['label']+'_2'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_1'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_2'] = se_sigma data_central_value = data_central_value + value_delta data_central.append(data_central_value) for k in range(96): - error_value['ArtUnc_'+str(k+1)] = float(artUncMatr[j + 48][k]) + error_value['ArtUnc_' + str(k + 1)] = float(artUncMatr[j + 48][k]) error_value['stat'] = 0 error.append(error_value) error_definition = { - 'stat':{'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Uncorr':{'description': 'systematic uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Model_1':{'description': 'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Model_2':{'description': 'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ModelRW_1':{'description': 'reweighting uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'ModelRW_2':{'description': 'reweighting uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'JES_1':{'description': 'jet energy scale uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'JES_2':{'description': 'jet energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'RCES':{'description': 'remaining cluster energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ElEn':{'description': 'electron energy uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ElTh':{'description': 'electron theta uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'Lumi':{'description': 'luminosity', 'treatment': 'MULT', 'type': 'CORR'}, - 'LArN':{'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, - 'StatMC':{'description': 'MC statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'RadErr':{'description': 'radiative uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} + 'stat': {'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Uncorr': {'description': 'systematic uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_1': {'description': 'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_2': {'description': 'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'ModelRW_1': { + 'description': 'reweighting uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'ModelRW_2': { + 'description': 'reweighting uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'JES_1': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'JES_2': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'RCES': { + 'description': 'remaining cluster energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'ElEn': {'description': 'electron energy uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'ElTh': {'description': 'electron theta uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'Lumi': {'description': 'luminosity', 'treatment': 'MULT', 'type': 'CORR'}, + 'LArN': {'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, + 'StatMC': { + 'description': 'MC statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'RadErr': {'description': 'radiative uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, } for i in range(96): - error_definition['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'H1JETS161103421unc'+str(i+1)} + error_definition['ArtUnc_' + str(i + 1)] = { + 'description': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'H1JETS161103421unc' + str(i + 1), + } data_central_yaml = {'data_central': data_central} kinematics_yaml = {'bins': kin} @@ -122,7 +170,7 @@ def processData(): with open('uncertainties.yaml', 'w') as file: yaml.dump(uncertainties_yaml, file, sort_keys=False) -# dijet_norm data + # dijet_norm data for i in tables_norm: if i == 33: @@ -150,7 +198,7 @@ def processData(): Q2_min = 60 Q2_max = 80 - hepdata_tables="rawdata/data"+str(i)+".yaml" + hepdata_tables = "rawdata/data" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -161,52 +209,98 @@ def processData(): data_central_value = float(values[j]['value']) pT_max = input['independent_variables'][0]['values'][j]['high'] pT_min = input['independent_variables'][0]['values'][j]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max},'pT': {'min': pT_min, 'mid': None, 'max': pT_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + } kin_norm.append(kin_value) value_delta = 0 error_value = {} for k in 0, 1, 5, 6, 7, 8, 9, 10, 11: if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']] = pta(values[j]['errors'][k]['symerror'], data_central_value) + error_value[values[j]['errors'][k]['label']] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)) + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value), + ) value_delta = value_delta + se_delta error_value[values[j]['errors'][k]['label']] = se_sigma for k in 2, 3, 4: if 'symerror' in values[j]['errors'][k]: - error_value[values[j]['errors'][k]['label']+'_1'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) - error_value[values[j]['errors'][k]['label']+'_2'] = pta(values[j]['errors'][k]['symerror'], data_central_value)/sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_1'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = pta( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) else: - se_delta, se_sigma = se(pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value)/sqrt(2), pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value)/sqrt(2)) + se_delta, se_sigma = se( + pta(values[j]['errors'][k]['asymerror']['plus'], data_central_value) + / sqrt(2), + pta(values[j]['errors'][k]['asymerror']['minus'], data_central_value) + / sqrt(2), + ) value_delta = value_delta + se_delta + se_delta - error_value[values[j]['errors'][k]['label']+'_1'] = se_sigma - error_value[values[j]['errors'][k]['label']+'_2'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_1'] = se_sigma + error_value[values[j]['errors'][k]['label'] + '_2'] = se_sigma data_central_value = data_central_value + value_delta data_central_norm.append(data_central_value) for k in range(96): - error_value['ArtUnc_'+str(k+1)] = float(artUncMatr_norm[j + 48][k]) + error_value['ArtUnc_' + str(k + 1)] = float(artUncMatr_norm[j + 48][k]) error_value['stat'] = 0 error_norm.append(error_value) error_definition_norm = { - 'stat':{'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Uncorr':{'description': 'systematic uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Model_1':{'description': 'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Model_2':{'description': 'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ModelRW_1':{'description': 'reweighting uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'ModelRW_2':{'description': 'reweighting uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'JES_1':{'description': 'jet energy scale uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'JES_2':{'description': 'jet energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'RCES':{'description': 'remaining cluster energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ElEn':{'description': 'electron energy uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'ElTh':{'description': 'electron theta uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, - 'Lumi':{'description': 'luminosity', 'treatment': 'MULT', 'type': 'CORR'}, - 'LArN':{'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, - 'StatMC':{'description': 'MC statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'RadErr':{'description': 'radiative uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'} + 'stat': {'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Uncorr': {'description': 'systematic uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_1': {'description': 'MC model uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Model_2': {'description': 'MC model uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'ModelRW_1': { + 'description': 'reweighting uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'ModelRW_2': { + 'description': 'reweighting uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'JES_1': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'JES_2': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'RCES': { + 'description': 'remaining cluster energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, + 'ElEn': {'description': 'electron energy uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'ElTh': {'description': 'electron theta uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + 'Lumi': {'description': 'luminosity', 'treatment': 'MULT', 'type': 'CORR'}, + 'LArN': {'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, + 'StatMC': { + 'description': 'MC statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'RadErr': {'description': 'radiative uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, } for i in range(96): - error_definition_norm['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty '+str(i+1), 'treatment': 'ADD', 'type': 'H1JETS161103421NORMunc'+str(i+1)} + error_definition_norm['ArtUnc_' + str(i + 1)] = { + 'description': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'H1JETS161103421NORMunc' + str(i + 1), + } data_central_norm_yaml = {'data_central': data_central_norm} kinematics_norm_yaml = {'bins': kin_norm} @@ -221,4 +315,5 @@ def processData(): with open('uncertainties_norm.yaml', 'w') as file: yaml.dump(uncertainties_norm_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py index c164efcc05..39f732434d 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/filter.py @@ -1,7 +1,8 @@ +from manual_impl import artunc, dijet_data, dijet_sys import yaml -# use #1693 + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import percentage_to_absolute as pta -from manual_impl import dijet_data, dijet_sys, artunc + def processData(): with open('metadata.yaml', 'r') as file: @@ -20,9 +21,9 @@ def processData(): kin_norm = [] error_norm = [] -# dijet data + # dijet data - hepdata_tables="rawdata/Table"+str(tables[0])+".yaml" + hepdata_tables = "rawdata/Table" + str(tables[0]) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -37,48 +38,57 @@ def processData(): Q2_min = input['independent_variables'][0]['values'][i]['low'] pT_max = input['independent_variables'][1]['values'][i]['high'] pT_min = input['independent_variables'][1]['values'][i]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + } kin.append(kin_value) error_value = {} # error_value['stat'] = pta(values[i]['errors'][0]['symerror'], data_central_value) # error_value['sys'] = pta(values[i]['errors'][1]['symerror'], data_central_value) for j in range(len(dijet_sys[i])): - error_value['Syst_'+str(j+1)] = dijet_sys[i][j] - for j in range(len(artunc[i+24])): - error_value['ArtUnc_'+str(j+1)] = artunc[i+24][j] + error_value['Syst_' + str(j + 1)] = dijet_sys[i][j] + for j in range(len(artunc[i + 24])): + error_value['ArtUnc_' + str(j + 1)] = artunc[i + 24][j] error.append(error_value) # error_definition = {'stat':{'description': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, 'sys':{'description': 'total systematic uncertainty', 'treatment':'MULT' , 'type': 'CORR'}} - error_definition = {'Syst_1':{'description': 'model', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_2':{'description': 'jes', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_3':{'description': 'jes', 'treatment': 'MULT', 'type': 'CORR'}, - 'Syst_4':{'description': 'rces', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_5':{'description': 'rces', 'treatment': 'MULT', 'type': 'CORR'}, - 'Syst_6':{'description': 'e_e', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_7':{'description': 'theta_e', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_8':{'description': 'ID_e', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'Syst_9':{'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, - 'Syst_10':{'description': 'norm', 'treatment': 'MULT', 'type': 'CORR'}} + error_definition = { + 'Syst_1': {'description': 'model', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_2': {'description': 'jes', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_3': {'description': 'jes', 'treatment': 'MULT', 'type': 'CORR'}, + 'Syst_4': {'description': 'rces', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_5': {'description': 'rces', 'treatment': 'MULT', 'type': 'CORR'}, + 'Syst_6': {'description': 'e_e', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_7': {'description': 'theta_e', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_8': {'description': 'ID_e', 'treatment': 'ADD', 'type': 'UNCORR'}, + 'Syst_9': {'description': 'lar noise', 'treatment': 'MULT', 'type': 'CORR'}, + 'Syst_10': {'description': 'norm', 'treatment': 'MULT', 'type': 'CORR'}, + } for i in range(48): - error_definition['ArtUnc_'+str(i+1)] = {'description': 'artificial uncertainty ' + str(i+1), 'treatment': 'ADD', 'type': 'H1JETS14064709unc'+str(i+1)} - + error_definition['ArtUnc_' + str(i + 1)] = { + 'description': 'artificial uncertainty ' + str(i + 1), + 'treatment': 'ADD', + 'type': 'H1JETS14064709unc' + str(i + 1), + } data_central_yaml = {'data_central': data_central} kinematics_yaml = {'bins': kin} uncertainties_yaml = {'definitions': error_definition, 'bins': error} with open('data.yaml', 'w') as file: - yaml.dump(data_central_yaml, file, sort_keys=False) + yaml.dump(data_central_yaml, file, sort_keys=False) with open('kinematics.yaml', 'w') as file: - yaml.dump(kinematics_yaml, file, sort_keys=False) + yaml.dump(kinematics_yaml, file, sort_keys=False) with open('uncertainties.yaml', 'w') as file: yaml.dump(uncertainties_yaml, file, sort_keys=False) -# dijet_norm data + # dijet_norm data - hepdata_tables="rawdata/Table"+str(tables_norm[0])+".yaml" + hepdata_tables = "rawdata/Table" + str(tables_norm[0]) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -92,26 +102,38 @@ def processData(): Q2_min = input['independent_variables'][0]['values'][i]['low'] pT_max = input['independent_variables'][1]['values'][i]['high'] pT_min = input['independent_variables'][1]['values'][i]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'pT': {'min': pT_min, 'mid': None, 'max': pT_max}, + } kin_norm.append(kin_value) error_value = {} error_value['stat'] = pta(values[i]['errors'][0]['symerror'], data_central_value) error_value['sys'] = pta(values[i]['errors'][1]['symerror'], data_central_value) error_norm.append(error_value) - error_definition_norm = {'stat':{'description': 'total statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, 'sys':{'description': 'total systematic uncertainty', 'treatment':'MULT' , 'type': 'CORR'}} + error_definition_norm = { + 'stat': { + 'description': 'total statistical uncertainty', + 'treatment': 'ADD', + 'type': 'UNCORR', + }, + 'sys': {'description': 'total systematic uncertainty', 'treatment': 'MULT', 'type': 'CORR'}, + } data_central_norm_yaml = {'data_central': data_central_norm} kinematics_norm_yaml = {'bins': kin_norm} uncertainties_norm_yaml = {'definitions': error_definition_norm, 'bins': error_norm} with open('data_norm.yaml', 'w') as file: - yaml.dump(data_central_norm_yaml, file, sort_keys=False) + yaml.dump(data_central_norm_yaml, file, sort_keys=False) with open('kinematics_norm.yaml', 'w') as file: - yaml.dump(kinematics_norm_yaml, file, sort_keys=False) + yaml.dump(kinematics_norm_yaml, file, sort_keys=False) with open('uncertainties_norm.yaml', 'w') as file: yaml.dump(uncertainties_norm_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py index d1554b3822..50e3f03421 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py +++ b/nnpdf_data/nnpdf_data/new_commondata/H1_2JET_319GEV_351PB-1_DIF/manual_impl.py @@ -1,5 +1,5 @@ from math import sqrt -# use #1693 + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import cormat_to_covmat as ctc from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc as cta diff --git a/nnpdf_data/nnpdf_data/new_commondata/HERMES97_NC_7GEV_EN/filter.py b/nnpdf_data/nnpdf_data/new_commondata/HERMES97_NC_7GEV_EN/filter.py index ac80f85f61..953512b38e 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/HERMES97_NC_7GEV_EN/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/HERMES97_NC_7GEV_EN/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -77,23 +78,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/HERMES_NC_7GEV_ED/filter.py b/nnpdf_data/nnpdf_data/new_commondata/HERMES_NC_7GEV_ED/filter.py index 4e07ab25c7..38e3209d55 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/HERMES_NC_7GEV_ED/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/HERMES_NC_7GEV_ED/filter.py @@ -1,11 +1,10 @@ -import pandas as pd -import yaml import glob -import numpy as np import pathlib -import pandas as pd import sys -import pathlib + +import numpy as np +import pandas as pd +import yaml HERE = pathlib.Path(__file__).parent sys.path = [str(HERE.parent / "HERMES_NC_7GEV_EP")] + sys.path @@ -95,12 +94,8 @@ def write_data(df): for j in range(ndata_points): e[f"sys_{j}"] = art_sys[i][j] - e[ - "stat" - ] = 0 # This is set to 0 as the stat unc is correlated and reported in sys_0 - e["exp"] = float( - df.loc[i, "exp"] - ) # experimental including normalization + e["stat"] = 0 # This is set to 0 as the stat unc is correlated and reported in sys_0 + e["exp"] = float(df.loc[i, "exp"]) # experimental including normalization e["param"] = float(df.loc[i, "param"]) e["evol"] = float(df.loc[i, "evol"]) error.append(e) diff --git a/nnpdf_data/nnpdf_data/new_commondata/HERMES_NC_7GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/HERMES_NC_7GEV_EP/filter.py index b8e31e3153..7b4613de30 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/HERMES_NC_7GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/HERMES_NC_7GEV_EP/filter.py @@ -1,10 +1,10 @@ -import pandas as pd -import yaml import glob -import numpy as np import pathlib -import pandas as pd + +import numpy as np from numpy.linalg import eig +import pandas as pd +import yaml def read_data(fnames): @@ -166,12 +166,8 @@ def write_data(df): for j in range(ndata_points): e[f"sys_{j}"] = art_sys[i][j] - e[ - "stat" - ] = 0 # This is set to 0 as the stat unc is correlated and reported in sys_0 - e["exp"] = float( - df.loc[i, "exp"] - ) # experimental including normalization + e["stat"] = 0 # This is set to 0 as the stat unc is correlated and reported in sys_0 + e["exp"] = float(df.loc[i, "exp"]) # experimental including normalization e["param"] = float(df.loc[i, "param"]) e["evol"] = float(df.loc[i, "evol"]) error.append(e) diff --git a/nnpdf_data/nnpdf_data/new_commondata/JLABE06_NC_3GEV_EN/filter.py b/nnpdf_data/nnpdf_data/new_commondata/JLABE06_NC_3GEV_EN/filter.py index 24a4e2996b..7d01af8e76 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/JLABE06_NC_3GEV_EN/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/JLABE06_NC_3GEV_EN/filter.py @@ -1,8 +1,9 @@ -import pandas as pd -import yaml import glob from io import StringIO +import pandas as pd +import yaml + def read_data(fnames): df = pd.DataFrame() @@ -58,23 +59,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "G_stat"]), - "sys": float(df.loc[i, "G_sys"]), - } + e = {"stat": float(df.loc[i, "G_stat"]), "sys": float(df.loc[i, "G_sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/JLABE97_NC_NOTFIXED_EN/filter.py b/nnpdf_data/nnpdf_data/new_commondata/JLABE97_NC_NOTFIXED_EN/filter.py index 160c64bd24..26a731d436 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/JLABE97_NC_NOTFIXED_EN/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/JLABE97_NC_NOTFIXED_EN/filter.py @@ -1,7 +1,8 @@ -import pandas as pd -import yaml import glob + import numpy as np +import pandas as pd +import yaml def read_data(fnames): @@ -31,9 +32,9 @@ def read_data(fnames): ], ignore_index=True, ) - + lengths.append(len(Qsub)) - + print(f"number of tables: {len(lengths)}") print(f"lengths: {lengths}") print(f"total length: {np.sum(lengths)}") @@ -67,23 +68,12 @@ def write_data(df): # Write unc file error = [] for idx, i in enumerate(range(len(df))): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/JLABE99_NC_3GEV_EN/filter.py b/nnpdf_data/nnpdf_data/new_commondata/JLABE99_NC_3GEV_EN/filter.py index 03a2448ca4..4955d5f2d1 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/JLABE99_NC_3GEV_EN/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/JLABE99_NC_3GEV_EN/filter.py @@ -1,7 +1,8 @@ -import pandas as pd -import yaml import glob + import numpy as np +import pandas as pd +import yaml def read_data(fnames): @@ -31,9 +32,9 @@ def read_data(fnames): ], ignore_index=True, ) - + lengths.append(len(Qsub)) - + print(f"number of tables: {len(lengths)}") print(f"lengths: {lengths}") print(f"total length: {np.sum(lengths)}") @@ -67,23 +68,12 @@ def write_data(df): # Write unc file error = [] for idx, i in enumerate(range(len(df))): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/JLABEG1B_NC_NOTFIXED_ED/filter.py b/nnpdf_data/nnpdf_data/new_commondata/JLABEG1B_NC_NOTFIXED_ED/filter.py index 74f4baae96..4933ad1f19 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/JLABEG1B_NC_NOTFIXED_ED/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/JLABEG1B_NC_NOTFIXED_ED/filter.py @@ -1,7 +1,8 @@ -import pandas as pd -import yaml import glob + import numpy as np +import pandas as pd +import yaml def read_data(fnames): @@ -31,9 +32,9 @@ def read_data(fnames): ], ignore_index=True, ) - + lengths.append(len(Qsub)) - + print(f"number of tables: {len(lengths)}") print(f"lengths: {lengths}") print(f"total length: {np.sum(lengths)}") @@ -67,23 +68,12 @@ def write_data(df): # Write unc file error = [] for idx, i in enumerate(range(len(df))): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/JLABEG1B_NC_NOTFIXED_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/JLABEG1B_NC_NOTFIXED_EP/filter.py index 74f4baae96..4933ad1f19 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/JLABEG1B_NC_NOTFIXED_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/JLABEG1B_NC_NOTFIXED_EP/filter.py @@ -1,7 +1,8 @@ -import pandas as pd -import yaml import glob + import numpy as np +import pandas as pd +import yaml def read_data(fnames): @@ -31,9 +32,9 @@ def read_data(fnames): ], ignore_index=True, ) - + lengths.append(len(Qsub)) - + print(f"number of tables: {len(lengths)}") print(f"lengths: {lengths}") print(f"total length: {np.sum(lengths)}") @@ -67,23 +68,12 @@ def write_data(df): # Write unc file error = [] for idx, i in enumerate(range(len(df))): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/JLABEG1DVCS_NC_3GEV_EP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/JLABEG1DVCS_NC_3GEV_EP/filter.py index f2bd9b5b56..047c85c050 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/JLABEG1DVCS_NC_3GEV_EP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/JLABEG1DVCS_NC_3GEV_EP/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -59,23 +60,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/JLABEG1DVCS_NC_5GEV_ED/filter.py b/nnpdf_data/nnpdf_data/new_commondata/JLABEG1DVCS_NC_5GEV_ED/filter.py index f2bd9b5b56..047c85c050 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/JLABEG1DVCS_NC_5GEV_ED/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/JLABEG1DVCS_NC_5GEV_ED/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -59,23 +60,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py index dd25eb259c..cec61ffaac 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_7TEV_MUON/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV SQRT_S = 7_000.0 # GeV @@ -82,11 +82,7 @@ def get_kinematics(hepdata: dict, bin_index: list, boson: str = "Z") -> list: ymin = float(rapbins[bins]["low"]) ymax = float(rapbins[bins]["high"]) kin_value = { - "y": { - "min": ymin, - "mid": 0.5 * (ymin + ymax), - "max": ymax, - }, + "y": {"min": ymin, "mid": 0.5 * (ymin + ymax), "max": ymax}, "M2": {"min": None, "mid": MAP_BOSON[boson] ** 2, "max": None}, "sqrts": {"min": None, "mid": SQRT_S, "max": None}, } @@ -146,12 +142,7 @@ def get_errors(hepdata: dict, bin_index: list, indx: int = 0) -> dict: sys_beam.append(NORM_FACTOR * errors[idx]["errors"][2]["symerror"]) sys_lumi.append(NORM_FACTOR * errors[idx]["errors"][3]["symerror"]) - return { - "stat": stat, - "sys_corr": sys_corr, - "sys_beam": sys_beam, - "sys_lumi": sys_lumi, - } + return {"stat": stat, "sys_corr": sys_corr, "sys_beam": sys_beam, "sys_lumi": sys_lumi} def read_corrmatrix(nb_datapoints: int) -> np.ndarray: @@ -320,11 +311,7 @@ def dump_commondata(kinematics: list, data: list, errors: list) -> None: yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter() -> None: @@ -370,11 +357,7 @@ def main_filter() -> None: # Compute the Artifical Systematics from CovMat corrmat = read_corrmatrix(nb_datapoints=nbpoints) covmat = multiply_syst(corrmat, errors_combined["sys_corr"]) - artunc = generate_artificial_unc( - ndata=nbpoints, - covmat_list=covmat.tolist(), - no_of_norm_mat=0, - ) + artunc = generate_artificial_unc(ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0) errors = format_uncertainties(errors_combined, artunc) # Generate all the necessary files diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py index a93b4056aa..4b0d1a73e5 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_DY_8TEV_MUON/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV SQRT_S = 8_000.0 # GeV @@ -82,11 +82,7 @@ def get_kinematics(hepdata: dict, bin_index: list, boson: str = "Z") -> list: ymin = float(rapbins[bins]["low"]) ymax = float(rapbins[bins]["high"]) kin_value = { - "y": { - "min": ymin, - "mid": 0.5 * (ymin + ymax), - "max": ymax, - }, + "y": {"min": ymin, "mid": 0.5 * (ymin + ymax), "max": ymax}, "M2": {"min": None, "mid": MAP_BOSON[boson] ** 2, "max": None}, "sqrts": {"min": None, "mid": SQRT_S, "max": None}, } @@ -146,12 +142,7 @@ def get_errors(hepdata: dict, bin_index: list, indx: int = 0) -> dict: sys_beam.append(NORM_FACTOR * errors[idx]["errors"][2]["symerror"]) sys_lumi.append(NORM_FACTOR * errors[idx]["errors"][3]["symerror"]) - return { - "stat": stat, - "sys_corr": sys_corr, - "sys_beam": sys_beam, - "sys_lumi": sys_lumi, - } + return {"stat": stat, "sys_corr": sys_corr, "sys_beam": sys_beam, "sys_lumi": sys_lumi} def read_corrmatrix(nb_datapoints: int) -> np.ndarray: @@ -320,11 +311,7 @@ def dump_commondata(kinematics: list, data: list, errors: list) -> None: yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter() -> None: @@ -370,11 +357,7 @@ def main_filter() -> None: # Compute the Artifical Systematics from CovMat corrmat = read_corrmatrix(nb_datapoints=nbpoints) covmat = multiply_syst(corrmat, errors_combined["sys_corr"]) - artunc = generate_artificial_unc( - ndata=nbpoints, - covmat_list=covmat.tolist(), - no_of_norm_mat=0, - ) + artunc = generate_artificial_unc(ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0) errors = format_uncertainties(errors_combined, artunc) # Generate all the necessary files diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py index 4ec8dc0723..d63c7f9cab 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_7TEV_MUON/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV SQRT_S = 7_000.0 # GeV @@ -82,11 +82,7 @@ def get_kinematics(hepdata: dict, bin_index: list, boson: str = "Z") -> list: ymin = float(rapbins[bins]["low"]) ymax = float(rapbins[bins]["high"]) kin_value = { - "y": { - "min": ymin, - "mid": 0.5 * (ymin + ymax), - "max": ymax, - }, + "y": {"min": ymin, "mid": 0.5 * (ymin + ymax), "max": ymax}, "M2": {"min": None, "mid": MAP_BOSON[boson] ** 2, "max": None}, "sqrts": {"min": None, "mid": SQRT_S, "max": None}, } @@ -146,12 +142,7 @@ def get_errors(hepdata: dict, bin_index: list, indx: int = 0) -> dict: sys_beam.append(NORM_FACTOR * errors[idx]["errors"][2]["symerror"]) sys_lumi.append(NORM_FACTOR * errors[idx]["errors"][3]["symerror"]) - return { - "stat": stat, - "sys_corr": sys_corr, - "sys_beam": sys_beam, - "sys_lumi": sys_lumi, - } + return {"stat": stat, "sys_corr": sys_corr, "sys_beam": sys_beam, "sys_lumi": sys_lumi} def read_corrmatrix(nb_datapoints: int) -> np.ndarray: @@ -326,11 +317,7 @@ def dump_commondata(kinematics: list, data: list, errors: list, nbpoints: int) - yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter(boson: str = "Z") -> None: @@ -379,11 +366,7 @@ def main_filter(boson: str = "Z") -> None: # Compute the Artifical Systematics from CovMat corrmat = read_corrmatrix(nb_datapoints=nbpoints) covmat = multiply_syst(corrmat, errors_combined["sys_corr"]) - artunc = generate_artificial_unc( - ndata=nbpoints, - covmat_list=covmat.tolist(), - no_of_norm_mat=0, - ) + artunc = generate_artificial_unc(ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0) errors = format_uncertainties(errors_combined, artunc, bslice) # Generate all the necessary files diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py index 6306ce22ac..6a2a2cb286 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_WPWM_8TEV_MUON/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV SQRT_S = 8_000.0 # GeV @@ -142,12 +142,7 @@ def get_errors(hepdata: dict, bin_index: list, indx: int = 0) -> dict: sys_beam.append(NORM_FACTOR * errors[idx]["errors"][2]["symerror"]) sys_lumi.append(NORM_FACTOR * errors[idx]["errors"][3]["symerror"]) - return { - "stat": stat, - "sys_corr": sys_corr, - "sys_beam": sys_beam, - "sys_lumi": sys_lumi, - } + return {"stat": stat, "sys_corr": sys_corr, "sys_beam": sys_beam, "sys_lumi": sys_lumi} def read_corrmatrix(nb_datapoints: int) -> np.ndarray: @@ -272,9 +267,7 @@ def format_uncertainties(uncs: dict, artunc: np.ndarray, bslice: slice) -> list: return combined_errors -def dump_commondata( - kinematics: list, data: list, errors: list, nbpoints: int -) -> None: +def dump_commondata(kinematics: list, data: list, errors: list, nbpoints: int) -> None: """Function that generates and writes the commondata files. Parameters @@ -324,11 +317,7 @@ def dump_commondata( yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter(boson: str = "Z") -> None: @@ -362,9 +351,7 @@ def main_filter(boson: str = "Z") -> None: yaml_content = load_yaml(table_id=tabid, version=version) # Extract the kinematic, data, and uncertainties - kinematics = get_kinematics( - yaml_content, bin_index, MAP_TABLE[tabid] - ) + kinematics = get_kinematics(yaml_content, bin_index, MAP_TABLE[tabid]) data_central = get_data_values(yaml_content, bin_index, indx=idx) uncertainties = get_errors(yaml_content, bin_index, indx=idx) @@ -379,15 +366,11 @@ def main_filter(boson: str = "Z") -> None: # Compute the Artifical Systematics from CovMat corrmat = read_corrmatrix(nb_datapoints=nbpoints) covmat = multiply_syst(corrmat, errors_combined["sys_corr"]) - artunc = generate_artificial_unc( - ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0 - ) + artunc = generate_artificial_unc(ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0) errors = format_uncertainties(errors_combined, artunc, bslice) # Generate all the necessary files - dump_commondata( - comb_kins[bslice], comb_data[bslice], errors[bslice], nbpoints - ) + dump_commondata(comb_kins[bslice], comb_data[bslice], errors[bslice], nbpoints) return diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py index 61afd3dfca..d485a62c81 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_13TEV/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - MZ_VALUE = 91.1876 # GeV SQRT_S = 13_000.0 # GeV NORM_FACTOR = 1_000.0 # from pb -> fb @@ -80,11 +80,7 @@ def get_kinematics(hepdata: dict, bin_index: list) -> list: for idx in bin_index: ymin, ymax = [float(i) for i in rapbins[idx]['value'].split("-")] kin_value = { - "y": { - "min": ymin, - "mid": 0.5 * (ymin + ymax), - "max": ymax, - }, + "y": {"min": ymin, "mid": 0.5 * (ymin + ymax), "max": ymax}, "M2": {"min": None, "mid": MZ_VALUE**2, "max": None}, "sqrts": {"min": None, "mid": SQRT_S, "max": None}, } @@ -139,11 +135,7 @@ def get_errors(hepdata: dict, bin_index: list) -> dict: sys_corr.append(NORM_FACTOR * errors[idx]["errors"][1]["symerror"]) sys_lumi.append(NORM_FACTOR * errors[idx]["errors"][2]["symerror"]) - return { - "stat": stat, - "sys_corr": sys_corr, - "sys_lumi": sys_lumi, - } + return {"stat": stat, "sys_corr": sys_corr, "sys_lumi": sys_lumi} def read_corrmatrix(nb_datapoints: int, state: str) -> np.ndarray: @@ -309,11 +301,7 @@ def dump_commondata(kinematics: list, data: list, errors: list, state: str) -> N yaml.dump({"bins": kinematics}, file, sort_keys=False) with open(f"uncertainties_{state}.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter(): @@ -357,9 +345,7 @@ def main_filter(): corrmat = read_corrmatrix(nb_datapoints=nbpoints, state=state) covmat = multiply_syst(corrmat, errors_combined["sys_corr"]) artunc = generate_artificial_unc( - ndata=nbpoints, - covmat_list=covmat.tolist(), - no_of_norm_mat=0, + ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0 ) errors = format_uncertainties(errors_combined, artunc) diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py index 1a8d0c467a..ddab8f5a32 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_DIELECTRON/filter.py @@ -1,10 +1,13 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml -from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc, percentage_to_absolute - +from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import ( + covmat_to_artunc, + percentage_to_absolute, +) MZ_VALUE = 91.1876 # GeV SQRT_S = 7_000.0 # GeV @@ -125,18 +128,10 @@ def get_errors(hepdata: dict, central: list) -> dict: sys_corr.append(NORM_FACTOR * np.sqrt(sys_cor**2 + sys_fsr**2)) # Convert systematic Luminosity into absolute - syslumi = percentage_to_absolute( - err["errors"][4]["symerror"], # [%] - central[idx], - ) + syslumi = percentage_to_absolute(err["errors"][4]["symerror"], central[idx]) # [%] sys_lumi.append(syslumi) - return { - "stat": stat, - "sys_uncorr": sys_uncorr, - "sys_corr": sys_corr, - "sys_lumi": sys_lumi, - } + return {"stat": stat, "sys_uncorr": sys_uncorr, "sys_corr": sys_corr, "sys_lumi": sys_lumi} def read_corrmatrix(nb_datapoints: int) -> np.ndarray: @@ -305,11 +300,7 @@ def dump_commondata(kinematics: list, data: list, errors: list) -> None: yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter(): @@ -349,11 +340,7 @@ def main_filter(): # Compute the Artifical Systematics from CovMat corrmat = read_corrmatrix(nb_datapoints=nbpoints) covmat = multiply_syst(corrmat, errors_combined["sys_corr"]) - artunc = generate_artificial_unc( - ndata=nbpoints, - covmat_list=covmat.tolist(), - no_of_norm_mat=0, - ) + artunc = generate_artificial_unc(ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0) errors = format_uncertainties(errors_combined, artunc) # Generate all the necessary files diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py index 786ef157e6..a603f11e5b 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_7TEV_MUON/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV SQRT_S = 7_000.0 # GeV @@ -142,12 +142,7 @@ def get_errors(hepdata: dict, bin_index: list, indx: int = 0) -> dict: sys_beam.append(NORM_FACTOR * errors[idx]["errors"][2]["symerror"]) sys_lumi.append(NORM_FACTOR * errors[idx]["errors"][3]["symerror"]) - return { - "stat": stat, - "sys_corr": sys_corr, - "sys_beam": sys_beam, - "sys_lumi": sys_lumi, - } + return {"stat": stat, "sys_corr": sys_corr, "sys_beam": sys_beam, "sys_lumi": sys_lumi} def read_corrmatrix(nb_datapoints: int) -> np.ndarray: @@ -273,9 +268,7 @@ def format_uncertainties(uncs: dict, artunc: np.ndarray, bslice: slice) -> list: return combined_errors -def dump_commondata( - kinematics: list, data: list, errors: list, nbpoints: int -) -> None: +def dump_commondata(kinematics: list, data: list, errors: list, nbpoints: int) -> None: """Function that generates and writes the commondata files. Parameters @@ -324,11 +317,7 @@ def dump_commondata( yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter(boson: str = "Z") -> None: @@ -362,9 +351,7 @@ def main_filter(boson: str = "Z") -> None: yaml_content = load_yaml(table_id=tabid, version=version) # Extract the kinematic, data, and uncertainties - kinematics = get_kinematics( - yaml_content, bin_index, MAP_TABLE[tabid] - ) + kinematics = get_kinematics(yaml_content, bin_index, MAP_TABLE[tabid]) data_central = get_data_values(yaml_content, bin_index, indx=idx) uncertainties = get_errors(yaml_content, bin_index, indx=idx) @@ -379,15 +366,11 @@ def main_filter(boson: str = "Z") -> None: # Compute the Artifical Systematics from CovMat corrmat = read_corrmatrix(nb_datapoints=nbpoints) covmat = multiply_syst(corrmat, errors_combined["sys_corr"]) - artunc = generate_artificial_unc( - ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0 - ) + artunc = generate_artificial_unc(ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0) errors = format_uncertainties(errors_combined, artunc, bslice) # Generate all the necessary files - dump_commondata( - comb_kins[bslice], comb_data[bslice], errors[bslice], nbpoints - ) + dump_commondata(comb_kins[bslice], comb_data[bslice], errors[bslice], nbpoints) return diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py index 7e94621771..8edf001649 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_DIELECTRON/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - MZ_VALUE = 91.1876 # GeV SQRT_S = 8_000.0 # GeV NORM_FACTOR = 1_000.0 # from pb -> fb @@ -124,12 +124,7 @@ def get_errors(hepdata: dict) -> dict: sys_corr.append(NORM_FACTOR * err["errors"][2]["symerror"]) sys_lumi.append(NORM_FACTOR * err["errors"][3]["symerror"]) - return { - "stat": stat, - "sys_uncorr": sys_uncorr, - "sys_corr": sys_corr, - "sys_lumi": sys_lumi, - } + return {"stat": stat, "sys_uncorr": sys_uncorr, "sys_corr": sys_corr, "sys_lumi": sys_lumi} def read_corrmatrix(nb_datapoints: int) -> np.ndarray: @@ -298,11 +293,7 @@ def dump_commondata(kinematics: list, data: list, errors: list) -> None: yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter(): @@ -342,11 +333,7 @@ def main_filter(): # Compute the Artifical Systematics from CovMat corrmat = read_corrmatrix(nb_datapoints=nbpoints) covmat = multiply_syst(corrmat, errors_combined["sys_corr"]) - artunc = generate_artificial_unc( - ndata=nbpoints, - covmat_list=covmat.tolist(), - no_of_norm_mat=0, - ) + artunc = generate_artificial_unc(ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0) errors = format_uncertainties(errors_combined, artunc) # Generate all the necessary files diff --git a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py index 14b17b6780..11f1899ddb 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/LHCB_Z0_8TEV_MUON/filter.py @@ -1,11 +1,11 @@ +import pathlib + import numpy as np import pandas as pd -import pathlib import yaml from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import covmat_to_artunc - MZ_VALUE = 91.1876 # GeV MW_VALUE = 80.398 # GeV SQRT_S = 8_000.0 # GeV @@ -142,12 +142,7 @@ def get_errors(hepdata: dict, bin_index: list, indx: int = 0) -> dict: sys_beam.append(NORM_FACTOR * errors[idx]["errors"][2]["symerror"]) sys_lumi.append(NORM_FACTOR * errors[idx]["errors"][3]["symerror"]) - return { - "stat": stat, - "sys_corr": sys_corr, - "sys_beam": sys_beam, - "sys_lumi": sys_lumi, - } + return {"stat": stat, "sys_corr": sys_corr, "sys_beam": sys_beam, "sys_lumi": sys_lumi} def read_corrmatrix(nb_datapoints: int) -> np.ndarray: @@ -272,9 +267,7 @@ def format_uncertainties(uncs: dict, artunc: np.ndarray, bslice: slice) -> list: return combined_errors -def dump_commondata( - kinematics: list, data: list, errors: list, nbpoints: int -) -> None: +def dump_commondata(kinematics: list, data: list, errors: list, nbpoints: int) -> None: """Function that generates and writes the commondata files. Parameters @@ -324,11 +317,7 @@ def dump_commondata( yaml.dump({"bins": kinematics}, file, sort_keys=False) with open("uncertainties.yaml", "w") as file: - yaml.dump( - {"definitions": error_definition, "bins": errors}, - file, - sort_keys=False, - ) + yaml.dump({"definitions": error_definition, "bins": errors}, file, sort_keys=False) def main_filter(boson: str = "Z") -> None: @@ -362,9 +351,7 @@ def main_filter(boson: str = "Z") -> None: yaml_content = load_yaml(table_id=tabid, version=version) # Extract the kinematic, data, and uncertainties - kinematics = get_kinematics( - yaml_content, bin_index, MAP_TABLE[tabid] - ) + kinematics = get_kinematics(yaml_content, bin_index, MAP_TABLE[tabid]) data_central = get_data_values(yaml_content, bin_index, indx=idx) uncertainties = get_errors(yaml_content, bin_index, indx=idx) @@ -379,15 +366,11 @@ def main_filter(boson: str = "Z") -> None: # Compute the Artifical Systematics from CovMat corrmat = read_corrmatrix(nb_datapoints=nbpoints) covmat = multiply_syst(corrmat, errors_combined["sys_corr"]) - artunc = generate_artificial_unc( - ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0 - ) + artunc = generate_artificial_unc(ndata=nbpoints, covmat_list=covmat.tolist(), no_of_norm_mat=0) errors = format_uncertainties(errors_combined, artunc, bslice) # Generate all the necessary files - dump_commondata( - comb_kins[bslice], comb_data[bslice], errors[bslice], nbpoints - ) + dump_commondata(comb_kins[bslice], comb_data[bslice], errors[bslice], nbpoints) return diff --git a/nnpdf_data/nnpdf_data/new_commondata/SMCSX_NC_17GEV_MUP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/SMCSX_NC_17GEV_MUP/filter.py index a83570a63b..2421b152c4 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/SMCSX_NC_17GEV_MUP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/SMCSX_NC_17GEV_MUP/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -23,7 +24,11 @@ def write_data(df): kin = [] for i in range(len(df["g1"])): kin_value = { - "x": {"min": float(df.loc[i, "xmin"]), "mid": float(df.loc[i, "x"]),"max": float(df.loc[i,"xmax"])}, + "x": { + "min": float(df.loc[i, "xmin"]), + "mid": float(df.loc[i, "x"]), + "max": float(df.loc[i, "xmax"]), + }, "Q2": {"min": None, "mid": float(df.loc[i, "Q2"]), "max": None}, } kin.append(kin_value) @@ -36,23 +41,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/SMCSX_NC_24GEV_MUD/filter.py b/nnpdf_data/nnpdf_data/new_commondata/SMCSX_NC_24GEV_MUD/filter.py index a83570a63b..2421b152c4 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/SMCSX_NC_24GEV_MUD/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/SMCSX_NC_24GEV_MUD/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -23,7 +24,11 @@ def write_data(df): kin = [] for i in range(len(df["g1"])): kin_value = { - "x": {"min": float(df.loc[i, "xmin"]), "mid": float(df.loc[i, "x"]),"max": float(df.loc[i,"xmax"])}, + "x": { + "min": float(df.loc[i, "xmin"]), + "mid": float(df.loc[i, "x"]), + "max": float(df.loc[i, "xmax"]), + }, "Q2": {"min": None, "mid": float(df.loc[i, "Q2"]), "max": None}, } kin.append(kin_value) @@ -36,23 +41,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/SMC_NC_NOTFIXED_MUD/filter.py b/nnpdf_data/nnpdf_data/new_commondata/SMC_NC_NOTFIXED_MUD/filter.py index 1a5578a3b2..7d90ac71ee 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/SMC_NC_NOTFIXED_MUD/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/SMC_NC_NOTFIXED_MUD/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -77,23 +78,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/SMC_NC_NOTFIXED_MUP/filter.py b/nnpdf_data/nnpdf_data/new_commondata/SMC_NC_NOTFIXED_MUP/filter.py index 1a5578a3b2..7d90ac71ee 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/SMC_NC_NOTFIXED_MUP/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/SMC_NC_NOTFIXED_MUP/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob def read_data(fnames): @@ -77,23 +78,12 @@ def write_data(df): # Write unc file error = [] for i in range(len(df)): - e = { - "stat": float(df.loc[i, "stat"]), - "sys": float(df.loc[i, "sys"]), - } + e = {"stat": float(df.loc[i, "stat"]), "sys": float(df.loc[i, "sys"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/STAR_WM_510GEV/filter.py b/nnpdf_data/nnpdf_data/new_commondata/STAR_WM_510GEV/filter.py index b029ac8ab1..55841646fc 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/STAR_WM_510GEV/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/STAR_WM_510GEV/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob ECM = 510 MW = 80.398 @@ -25,17 +26,9 @@ def write_data(df): kin = [] for i in range(len(df)): kin_value = { - "eta": { - "min": None, - "mid": float(df.loc[i, "$\eta_e$"]), - "max": None, - }, + "eta": {"min": None, "mid": float(df.loc[i, "$\eta_e$"]), "max": None}, "M2": {"min": None, "mid": float(df.loc[i, "M2"]), "max": None}, - "sqrts": { - "min": None, - "mid": float(df.loc[i, "sqrts"]), - "max": None, - }, + "sqrts": {"min": None, "mid": float(df.loc[i, "sqrts"]), "max": None}, } kin.append(kin_value) kinematics_yaml = {"bins": kin} @@ -47,23 +40,12 @@ def write_data(df): error = [] for i in range(len(df)): # here uncertainties are symmetric - e = { - "stat": float(df.loc[i, "stat +"]), - "sys": float(df.loc[i, "syst +"]), - } + e = {"stat": float(df.loc[i, "stat +"]), "sys": float(df.loc[i, "syst +"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/STAR_WP_510GEV/filter.py b/nnpdf_data/nnpdf_data/new_commondata/STAR_WP_510GEV/filter.py index b029ac8ab1..55841646fc 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/STAR_WP_510GEV/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/STAR_WP_510GEV/filter.py @@ -1,6 +1,7 @@ +import glob + import pandas as pd import yaml -import glob ECM = 510 MW = 80.398 @@ -25,17 +26,9 @@ def write_data(df): kin = [] for i in range(len(df)): kin_value = { - "eta": { - "min": None, - "mid": float(df.loc[i, "$\eta_e$"]), - "max": None, - }, + "eta": {"min": None, "mid": float(df.loc[i, "$\eta_e$"]), "max": None}, "M2": {"min": None, "mid": float(df.loc[i, "M2"]), "max": None}, - "sqrts": { - "min": None, - "mid": float(df.loc[i, "sqrts"]), - "max": None, - }, + "sqrts": {"min": None, "mid": float(df.loc[i, "sqrts"]), "max": None}, } kin.append(kin_value) kinematics_yaml = {"bins": kin} @@ -47,23 +40,12 @@ def write_data(df): error = [] for i in range(len(df)): # here uncertainties are symmetric - e = { - "stat": float(df.loc[i, "stat +"]), - "sys": float(df.loc[i, "syst +"]), - } + e = {"stat": float(df.loc[i, "stat +"]), "sys": float(df.loc[i, "syst +"])} error.append(e) error_definition = { - "stat": { - "description": "statistical uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, - "sys": { - "description": "systematic uncertainty", - "treatment": "ADD", - "type": "UNCORR", - }, + "stat": {"description": "statistical uncertainty", "treatment": "ADD", "type": "UNCORR"}, + "sys": {"description": "systematic uncertainty", "treatment": "ADD", "type": "UNCORR"}, } uncertainties_yaml = {"definitions": error_definition, "bins": error} diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py index 3c50ac6a81..e6c5b3fa0b 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py @@ -1,6 +1,8 @@ import yaml + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -11,11 +13,11 @@ def processData(): kin_q2_et = [] error_q2_et = [] -# q2_et data + # q2_et data for i in tables_q2_et: - hepdata_tables="rawdata/Table"+str(i)+".yaml" + hepdata_tables = "rawdata/Table" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -49,26 +51,39 @@ def processData(): data_central_value = values[k]['value'] ET_max = input['independent_variables'][0]['values'][k]['high'] ET_min = input['independent_variables'][0]['values'][k]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, 'ET': {'min': ET_min, 'mid': None, 'max': ET_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'ET': {'min': ET_min, 'mid': None, 'max': ET_max}, + } kin_q2_et.append(kin_value) value_delta = 0 error_value = {} if 'symerror' in values[k]['errors'][0]: error_value['stat'] = values[k]['errors'][0]['symerror'] else: - se_delta, se_sigma = se(values[k]['errors'][0]['asymerror']['plus'], values[k]['errors'][0]['asymerror']['minus']) + se_delta, se_sigma = se( + values[k]['errors'][0]['asymerror']['plus'], + values[k]['errors'][0]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_value['stat'] = se_sigma if 'symerror' in values[k]['errors'][1]: error_value['sys'] = values[k]['errors'][1]['symerror'] else: - se_delta, se_sigma = se(values[k]['errors'][1]['asymerror']['plus'], values[k]['errors'][1]['asymerror']['minus']) + se_delta, se_sigma = se( + values[k]['errors'][1]['asymerror']['plus'], + values[k]['errors'][1]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_value['sys'] = se_sigma if 'symerror' in values[k]['errors'][2]: error_value['jet_es'] = values[k]['errors'][2]['symerror'] else: - se_delta, se_sigma = se(values[k]['errors'][2]['asymerror']['plus'], values[k]['errors'][2]['asymerror']['minus']) + se_delta, se_sigma = se( + values[k]['errors'][2]['asymerror']['plus'], + values[k]['errors'][2]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_value['jet_es'] = se_sigma data_central_value = data_central_value + value_delta @@ -78,7 +93,11 @@ def processData(): error_definition_q2_et = { 'stat': {'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, 'sys': {'description': 'systematic uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'jet_es': {'description': 'jet energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + 'jet_es': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, } data_central_q2_et_yaml = {'data_central': data_central_q2_et} @@ -94,4 +113,5 @@ def processData(): with open('uncertainties_q2_et.yaml', 'w') as file: yaml.dump(uncertainties_q2_et_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py index 810355dc2d..1804a8e969 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_1JET_319GEV_82PB-1_DIF/filter.py @@ -1,6 +1,8 @@ import yaml + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -11,7 +13,7 @@ def processData(): kin_q2_et = [] error_q2_et = [] -# q2_et data + # q2_et data for i in tables_q2_et: if i == 12: @@ -33,7 +35,7 @@ def processData(): Q2_min = 5000 Q2_max = 10000 - hepdata_tables="rawdata/Table"+str(i)+".yaml" + hepdata_tables = "rawdata/Table" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -44,26 +46,39 @@ def processData(): data_central_value = values[j]['value'] ET_max = input['independent_variables'][0]['values'][j]['high'] ET_min = input['independent_variables'][0]['values'][j]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, 'ET': {'min': ET_min, 'mid': None, 'max': ET_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'ET': {'min': ET_min, 'mid': None, 'max': ET_max}, + } kin_q2_et.append(kin_value) value_delta = 0 error_value = {} if 'symerror' in values[j]['errors'][0]: error_value['stat'] = values[j]['errors'][0]['symerror'] else: - se_delta, se_sigma = se(values[j]['errors'][0]['asymerror']['plus'], values[j]['errors'][0]['asymerror']['minus']) + se_delta, se_sigma = se( + values[j]['errors'][0]['asymerror']['plus'], + values[j]['errors'][0]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_value['stat'] = se_sigma if 'symerror' in values[j]['errors'][1]: error_value['sys'] = values[j]['errors'][1]['symerror'] else: - se_delta, se_sigma = se(values[j]['errors'][1]['asymerror']['plus'], values[j]['errors'][1]['asymerror']['minus']) + se_delta, se_sigma = se( + values[j]['errors'][1]['asymerror']['plus'], + values[j]['errors'][1]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_value['sys'] = se_sigma if 'symerror' in values[j]['errors'][2]: error_value['jet_es'] = values[j]['errors'][2]['symerror'] else: - se_delta, se_sigma = se(values[j]['errors'][2]['asymerror']['plus'], values[j]['errors'][2]['asymerror']['minus']) + se_delta, se_sigma = se( + values[j]['errors'][2]['asymerror']['plus'], + values[j]['errors'][2]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_value['jet_es'] = se_sigma data_central_value = data_central_value + value_delta @@ -73,7 +88,11 @@ def processData(): error_definition_q2_et = { 'stat': {'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, 'sys': {'description': 'systematic uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'jet_es': {'description': 'jet energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + 'jet_es': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, } data_central_q2_et_yaml = {'data_central': data_central_q2_et} @@ -89,4 +108,5 @@ def processData(): with open('uncertainties_q2_et.yaml', 'w') as file: yaml.dump(uncertainties_q2_et_yaml, file, sort_keys=False) + processData() diff --git a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py index 1939fce20c..ce56dd4dab 100644 --- a/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py +++ b/nnpdf_data/nnpdf_data/new_commondata/ZEUS_2JET_319GEV_374PB-1_DIF/filter.py @@ -1,6 +1,8 @@ import yaml + from nnpdf_data.new_commondata.ATLAS_TTBAR_13TEV_HADR_DIF.utils import symmetrize_errors as se + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -11,7 +13,7 @@ def processData(): kin_q2_et = [] error_q2_et = [] -# q2_et data + # q2_et data for i in tables_q2_et: if i == 13: @@ -33,7 +35,7 @@ def processData(): Q2_min = 5000 Q2_max = 20000 - hepdata_tables="rawdata/Table"+str(i)+".yaml" + hepdata_tables = "rawdata/Table" + str(i) + ".yaml" with open(hepdata_tables, 'r') as file: input = yaml.safe_load(file) @@ -44,26 +46,39 @@ def processData(): data_central_value = values[j]['value'] ET_max = input['independent_variables'][0]['values'][j]['high'] ET_min = input['independent_variables'][0]['values'][j]['low'] - kin_value = {'sqrts': {'min': None, 'mid': sqrts, 'max': None}, 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, 'ET': {'min': ET_min, 'mid': None, 'max': ET_max}} + kin_value = { + 'sqrts': {'min': None, 'mid': sqrts, 'max': None}, + 'Q2': {'min': Q2_min, 'mid': None, 'max': Q2_max}, + 'ET': {'min': ET_min, 'mid': None, 'max': ET_max}, + } kin_q2_et.append(kin_value) value_delta = 0 error_value = {} if 'symerror' in values[j]['errors'][0]: error_value['stat'] = values[j]['errors'][0]['symerror'] else: - se_delta, se_sigma = se(values[j]['errors'][0]['asymerror']['plus'], values[j]['errors'][0]['asymerror']['minus']) + se_delta, se_sigma = se( + values[j]['errors'][0]['asymerror']['plus'], + values[j]['errors'][0]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_value['stat'] = se_sigma if 'symerror' in values[j]['errors'][1]: error_value['sys'] = values[j]['errors'][1]['symerror'] else: - se_delta, se_sigma = se(values[j]['errors'][1]['asymerror']['plus'], values[j]['errors'][1]['asymerror']['minus']) + se_delta, se_sigma = se( + values[j]['errors'][1]['asymerror']['plus'], + values[j]['errors'][1]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_value['sys'] = se_sigma if 'symerror' in values[j]['errors'][2]: error_value['jet_es'] = values[j]['errors'][2]['symerror'] else: - se_delta, se_sigma = se(values[j]['errors'][2]['asymerror']['plus'], values[j]['errors'][2]['asymerror']['minus']) + se_delta, se_sigma = se( + values[j]['errors'][2]['asymerror']['plus'], + values[j]['errors'][2]['asymerror']['minus'], + ) value_delta = value_delta + se_delta error_value['jet_es'] = se_sigma data_central_value = data_central_value + value_delta @@ -73,7 +88,11 @@ def processData(): error_definition_q2_et = { 'stat': {'description': 'statistical uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, 'sys': {'description': 'systematic uncertainty', 'treatment': 'ADD', 'type': 'UNCORR'}, - 'jet_es': {'description': 'jet energy scale uncertainty', 'treatment': 'MULT', 'type': 'CORR'} + 'jet_es': { + 'description': 'jet energy scale uncertainty', + 'treatment': 'MULT', + 'type': 'CORR', + }, } data_central_q2_et_yaml = {'data_central': data_central_q2_et} @@ -89,4 +108,5 @@ def processData(): with open('uncertainties_q2_et.yaml', 'w') as file: yaml.dump(uncertainties_q2_et_yaml, file, sort_keys=False) + processData()