From 567cfa904a3028d251ecf8c3239ab03021c01ef4 Mon Sep 17 00:00:00 2001 From: t7phy Date: Sun, 24 Mar 2024 01:58:21 +0100 Subject: [PATCH 1/4] 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 2801ac1e05bd2a929edbc81fa96df7a5d6dd5599 Mon Sep 17 00:00:00 2001 From: t7phy Date: Sun, 24 Mar 2024 15:30:59 +0100 Subject: [PATCH 2/4] integrate utils in filters --- .../ATLAS_1JET_13TEV_DIF/filter.py | 9 +- .../ATLAS_2JET_13TEV_DIF/filter.py | 10 +- .../ATLAS_TTBAR_13TEV_LJ_DIF/filter.py | 58 ++++++++++- .../ATLAS_TTBAR_8TEV_2L_DIF/filter.py | 36 ++++++- .../ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py | 89 +++++++++++++++-- .../ATLAS_TTBAR_8TEV_LJ_DIF/filter.py | 22 ++++- .../CMS_TTBAR_13TEV_2L_DIF/filter.py | 37 ++++++- .../CMS_TTBAR_13TEV_LJ_DIF/filter.py | 70 ++++++++++++- .../CMS_TTBAR_8TEV_2L_DIF/filter.py | 99 ++++++++++++++++++- .../CMS_TTBAR_8TEV_LJ_DIF/filter.py | 48 ++++++++- .../H1_1JET_319GEV_290PB-1_DIF/artUnc.py | 58 +++++++++-- .../H1_1JET_319GEV_290PB-1_DIF/filter.py | 21 +++- .../H1_1JET_319GEV_351PB-1_DIF/filter.py | 11 ++- .../H1_1JET_319GEV_351PB-1_DIF/manual_impl.py | 47 ++++++++- .../H1_2JET_319GEV_290PB-1_DIF/artUnc.py | 58 +++++++++-- .../H1_2JET_319GEV_290PB-1_DIF/filter.py | 21 +++- .../H1_2JET_319GEV_351PB-1_DIF/filter.py | 12 ++- .../H1_2JET_319GEV_351PB-1_DIF/manual_impl.py | 47 ++++++++- .../ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py | 26 ++++- .../ZEUS_1JET_319GEV_82PB-1_DIF/filter.py | 27 ++++- .../ZEUS_2JET_319GEV_374PB-1_DIF/filter.py | 26 ++++- 21 files changed, 772 insertions(+), 60 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..8a098772c7 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 +1,12 @@ import yaml -from validphys.commondata_utils import symmetrize_errors as se +from math import sqrt +def se(delta_plus, delta_minus): + + 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) + return se_delta, se_sigma 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..5c993ce57e 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 +1,13 @@ import yaml -from validphys.commondata_utils import symmetrize_errors as se +from math import sqrt + +def se(delta_plus, delta_minus): + + 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) + return se_delta, se_sigma 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..46f688a787 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 +1,59 @@ 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 +import numpy as np + +from math import sqrt +from numpy.linalg import eig + + +def se(delta_plus, delta_minus): + + 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) + return se_delta, se_sigma + +def pta(percentage, value): + + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute + +def cta(ndata, covmat_list, no_of_norm_mat=0): + + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if psd_check == False: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() 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..a83f9d3d0a 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 +1,39 @@ import yaml -from validphys.commondata_utils import covmat_to_artunc as cta +from math import sqrt +import numpy as np +from numpy.linalg import eig + +def cta(ndata, covmat_list, no_of_norm_mat=0): + + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if psd_check == False: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() 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..df10182dba 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 +1,88 @@ import yaml - +from math import sqrt 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 + + +def ctc(err_list, cormat_list): + + covmat_list = [] + for i in range(len(cormat_list)): + a = i // len(err_list) + b = i % len(err_list) + covmat_list.append(cormat_list[i] * err_list[a] * err_list[b]) + return covmat_list + +def cta(ndata, covmat_list, no_of_norm_mat=0): + + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if psd_check == False: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() + +def pta(percentage, value): + + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute + +def cm(rows, columns, list_of_matrices): + + for i in range(len(list_of_matrices)): + list_of_matrices[i] = np.array(list_of_matrices[i]) + col_list = [] + for i in range(rows): + row_list = [] + for j in range(columns): + row_list.append(list_of_matrices[j + i * columns]) + col_list.append(np.concatenate(tuple(row_list), axis=1)) + final_mat = np.concatenate(tuple(col_list), axis=0) + final_mat_list = [] + for i in range(len(final_mat)): + for j in range(len(final_mat[i])): + final_mat_list.append(final_mat[i][j]) + return final_mat_list + +def mtm(rows, columns, mat_list): + + if rows * columns == len(mat_list): + matrix = np.zeros((rows, columns)) + for i in range(rows): + for j in range(columns): + matrix[i][j] = mat_list[j + i * columns] + matrix = np.array(matrix) + return matrix + else: + raise Exception('rows * columns != len(mat_list)') 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 8d9e67f405..32317a9293 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 @@ -1,9 +1,25 @@ 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 math import sqrt + +def pta(percentage, value): + + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute + +def se(delta_plus, delta_minus): + + 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) + return se_delta, se_sigma 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..4f618ec2b7 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 +1,41 @@ import yaml -from validphys.commondata_utils import covmat_to_artunc as cta +import numpy as np + +from math import sqrt +from numpy.linalg import eig + +def cta(ndata, covmat_list, no_of_norm_mat=0): + + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if psd_check == False: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() 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..0f771e9c93 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 +1,73 @@ import yaml -from validphys.commondata_utils import covmat_to_artunc as cta +import numpy as np + +from math import sqrt +from numpy.linalg import eig + +def cta(ndata, covmat_list, no_of_norm_mat=0): + r"""Convert the covariance matrix to a matrix of + artificial uncertainties. + + Parameters + ---------- + ndata : integer + Number of data points + 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 + simplifies the implementation for the user. + no_of_norm_mat : int + Normalized covariance matrices may have an eigenvalue + of 0 due to the last data point not being linearly + independent. To allow for this, the user should input + the number of normalized matrices that are being treated + 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 + distributions), the input would be 3. The default value is + 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 + artificial uncertainties of the i^th data point. + + """ + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if psd_check == False: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() 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..5f99aaddbc 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 +1,98 @@ 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 +import numpy as np + +from math import sqrt +from numpy.linalg import eig + + +def se(delta_plus, delta_minus): + + 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) + return se_delta, se_sigma + +def pta(percentage, value): + + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute + +def ctc(err_list, cormat_list): + + covmat_list = [] + for i in range(len(cormat_list)): + a = i // len(err_list) + b = i % len(err_list) + covmat_list.append(cormat_list[i] * err_list[a] * err_list[b]) + return covmat_list + +def cta(ndata, covmat_list, no_of_norm_mat=0): + + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if psd_check == False: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() + +def ttf(mode, tri_mat_list): + + 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) + if i == j: + matrix[dim - 1 - i][dim - 1 - j] = tri_mat_list[list_el] + else: + matrix[dim - 1 - i][dim - 1 - j] = tri_mat_list[list_el] + matrix[dim - 1 - j][dim - 1 - i] = tri_mat_list[list_el] + elif mode == 1: + for i in range(dim): + for j in range(i + 1): + list_el = (i*(i + 1))//2 + j + if i == j: + matrix[i][j] = tri_mat_list[list_el] + else: + matrix[i][j] = tri_mat_list[list_el] + matrix[j][i] = tri_mat_list[list_el] + else: + raise Exception('Mode should be 0 or 1') + mat_list = [] + for i in range(dim): + for j in range(dim): + mat_list.append(matrix[i][j]) + return mat_list 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..92d524ea5b 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 +1,50 @@ import yaml -from validphys.commondata_utils import covmat_to_artunc as cta -from validphys.commondata_utils import percentage_to_absolute as pta +import numpy as np + +from math import sqrt +from numpy.linalg import eig + +def pta(percentage, value): + + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute + +def cta(ndata, covmat_list, no_of_norm_mat=0): + + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if psd_check == False: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() 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..ebb4dcbc8f 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 +1,50 @@ 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 +import numpy as np + +from math import sqrt +from numpy.linalg import eig + +def pta(percentage, value): + + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute + +def cta(ndata, covmat_list, no_of_norm_mat=0): + + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if psd_check == False: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() def artunc(): @@ -30,8 +72,8 @@ def artunc(): for i in range(96): errArr.append(pta(errPercArr[i], dataArr[i])) - covMat = numpy.zeros((96, 96)) - artUnc = numpy.zeros((96, 96)) + covMat = np.zeros((96, 96)) + artUnc = np.zeros((96, 96)) for i in range(96): for j in range(i+1): @@ -75,8 +117,8 @@ def artunc_norm(): for i in range(96): errArr.append(pta(errPercArr[i], dataArr[i])) - covMat = numpy.zeros((96, 96)) - artUnc = numpy.zeros((96, 96)) + covMat = np.zeros((96, 96)) + artUnc = np.zeros((96, 96)) for i in range(96): for j in range(i+1): 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..0cca31b201 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 @@ -1,10 +1,25 @@ 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 math import sqrt +def se(delta_plus, delta_minus): + + 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) + return se_delta, se_sigma + +def pta(percentage, value): + + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) 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..a12d070cd5 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 @@ -1,8 +1,15 @@ import yaml -# use #1693 -from validphys.commondata_utils import percentage_to_absolute as pta from manual_impl import jet_data, jet_sys, artunc +def pta(percentage, value): + + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute def processData(): with open('metadata.yaml', 'r') as file: 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..a0a0d280cd 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 +1,48 @@ +import numpy as np + 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 numpy.linalg import eig + +def ctc(err_list, cormat_list): + + covmat_list = [] + for i in range(len(cormat_list)): + a = i // len(err_list) + b = i % len(err_list) + covmat_list.append(cormat_list[i] * err_list[a] * err_list[b]) + return covmat_list + +def cta(ndata, covmat_list, no_of_norm_mat=0): + + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if psd_check == False: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() 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..ebb4dcbc8f 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 +1,50 @@ 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 +import numpy as np + +from math import sqrt +from numpy.linalg import eig + +def pta(percentage, value): + + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute + +def cta(ndata, covmat_list, no_of_norm_mat=0): + + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if psd_check == False: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() def artunc(): @@ -30,8 +72,8 @@ def artunc(): for i in range(96): errArr.append(pta(errPercArr[i], dataArr[i])) - covMat = numpy.zeros((96, 96)) - artUnc = numpy.zeros((96, 96)) + covMat = np.zeros((96, 96)) + artUnc = np.zeros((96, 96)) for i in range(96): for j in range(i+1): @@ -75,8 +117,8 @@ def artunc_norm(): for i in range(96): errArr.append(pta(errPercArr[i], dataArr[i])) - covMat = numpy.zeros((96, 96)) - artUnc = numpy.zeros((96, 96)) + covMat = np.zeros((96, 96)) + artUnc = np.zeros((96, 96)) for i in range(96): for j in range(i+1): 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..f02422ab0e 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 @@ -1,10 +1,25 @@ 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 math import sqrt +def se(delta_plus, delta_minus): + + 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) + return se_delta, se_sigma + +def pta(percentage, value): + + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) 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..ab835eae19 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 @@ -1,8 +1,16 @@ import yaml -# use #1693 -from validphys.commondata_utils import percentage_to_absolute as pta from manual_impl import dijet_data, dijet_sys, artunc +def pta(percentage, value): + + if type(percentage) is str: + percentage = float(percentage.replace("%", "")) + absolute = percentage * value * 0.01 + return absolute + else: + absolute = percentage * value * 0.01 + return absolute + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) 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..a0a0d280cd 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 +1,48 @@ +import numpy as np + 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 numpy.linalg import eig + +def ctc(err_list, cormat_list): + + covmat_list = [] + for i in range(len(cormat_list)): + a = i // len(err_list) + b = i % len(err_list) + covmat_list.append(cormat_list[i] * err_list[a] * err_list[b]) + return covmat_list + +def cta(ndata, covmat_list, no_of_norm_mat=0): + + epsilon = -0.0000000001 + neg_eval_count = 0 + psd_check = True + covmat = np.zeros((ndata, ndata)) + artunc = np.zeros((ndata, ndata)) + for i in range(len(covmat_list)): + a = i // ndata + b = i % ndata + covmat[a][b] = covmat_list[i] + eigval, eigvec = eig(covmat) + for j in range(len(eigval)): + if eigval[j] < epsilon: + psd_check = False + elif eigval[j] > epsilon and eigval[j] <= 0: + neg_eval_count = neg_eval_count + 1 + if neg_eval_count == (no_of_norm_mat + 1): + psd_check = False + elif eigval[j] > 0: + continue + if psd_check == False: + raise ValueError('The covariance matrix is not positive-semidefinite') + else: + for i in range(ndata): + for j in range(ndata): + if eigval[j] < 0: + continue + else: + artunc[i][j] = eigvec[i][j] * sqrt(eigval[j]) + return artunc.tolist() 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..ce482d047e 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 +1,29 @@ import yaml -from validphys.commondata_utils import symmetrize_errors as se +from math import sqrt + +def se(delta_plus, delta_minus): + r"""Compute the symmterized uncertainty and the shift in data point. + + Parameters + ---------- + delta_plus : float + The top/plus uncertainty with sign + delta_minus : float + The bottom/minus uncertainty with sign + + Returns + ------- + se_delta : float + The value to be added to the data point + se_sigma : float + The symmetrized uncertainty to be used in commondata + + """ + 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) + return se_delta, se_sigma 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..c292084054 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 +1,30 @@ import yaml -from validphys.commondata_utils import symmetrize_errors as se +from math import sqrt + +def se(delta_plus, delta_minus): + r"""Compute the symmterized uncertainty and the shift in data point. + + Parameters + ---------- + delta_plus : float + The top/plus uncertainty with sign + delta_minus : float + The bottom/minus uncertainty with sign + + Returns + ------- + se_delta : float + The value to be added to the data point + se_sigma : float + The symmetrized uncertainty to be used in commondata + + """ + 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) + return se_delta, se_sigma + 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..dcbd5cbc91 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 +1,29 @@ import yaml -from validphys.commondata_utils import symmetrize_errors as se +from math import sqrt + +def se(delta_plus, delta_minus): + r"""Compute the symmterized uncertainty and the shift in data point. + + Parameters + ---------- + delta_plus : float + The top/plus uncertainty with sign + delta_minus : float + The bottom/minus uncertainty with sign + + Returns + ------- + se_delta : float + The value to be added to the data point + se_sigma : float + The symmetrized uncertainty to be used in commondata + + """ + 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) + return se_delta, se_sigma def processData(): with open('metadata.yaml', 'r') as file: From 2f9159bf918ce019d817a1013a2487ba7b57dfdb Mon Sep 17 00:00:00 2001 From: t7phy Date: Sun, 24 Mar 2024 15:59:24 +0100 Subject: [PATCH 3/4] 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 32317a9293..52cb35b39a 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 from math import sqrt @@ -460,4 +462,12 @@ 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: 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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: 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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 - 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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 - 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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: 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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 - 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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 - 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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: 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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: 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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: 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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: 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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 d686a392d578e6055f70e49b65fed08b9b93a7fd Mon Sep 17 00:00:00 2001 From: t7phy Date: Mon, 25 Mar 2024 19:49:37 +0100 Subject: [PATCH 4/4] full names of utils functions & isort and black formatting --- .../ATLAS_1JET_13TEV_DIF/filter.py | 91 ++- .../ATLAS_2JET_13TEV_DIF/filter.py | 55 +- .../ATLAS_TTBAR_13TEV_LJ_DIF/filter.py | 447 +++++++++---- .../ATLAS_TTBAR_8TEV_2L_DIF/filter.py | 199 ++++-- .../ATLAS_TTBAR_8TEV_LJ_DIF/artunc.py | 336 +++++++--- .../ATLAS_TTBAR_8TEV_LJ_DIF/filter.py | 621 ++++++++++++++---- .../CMS_TTBAR_13TEV_2L_DIF/filter.py | 298 ++++++--- .../CMS_TTBAR_13TEV_LJ_DIF/filter.py | 557 +++++++++++----- .../CMS_TTBAR_8TEV_2L_DIF/filter.py | 377 ++++++++--- .../CMS_TTBAR_8TEV_LJ_DIF/filter.py | 290 ++++++-- .../H1_1JET_319GEV_290PB-1_DIF/artUnc.py | 45 +- .../H1_1JET_319GEV_290PB-1_DIF/filter.py | 434 ++++++++---- .../H1_1JET_319GEV_351PB-1_DIF/filter.py | 96 ++- .../H1_1JET_319GEV_351PB-1_DIF/manual_impl.py | 8 +- .../H1_2JET_319GEV_290PB-1_DIF/artUnc.py | 45 +- .../H1_2JET_319GEV_290PB-1_DIF/filter.py | 239 +++++-- .../H1_2JET_319GEV_351PB-1_DIF/filter.py | 95 ++- .../H1_2JET_319GEV_351PB-1_DIF/manual_impl.py | 13 +- .../ZEUS_1JET_300GEV_38P6PB-1_DIF/filter.py | 47 +- .../ZEUS_1JET_319GEV_82PB-1_DIF/filter.py | 46 +- .../ZEUS_2JET_319GEV_374PB-1_DIF/filter.py | 47 +- 21 files changed, 3168 insertions(+), 1218 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 8a098772c7..26e94a6437 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 @@ -1,13 +1,31 @@ -import yaml from math import sqrt -def se(delta_plus, delta_minus): - - semi_diff = (delta_plus + delta_minus)/2 - average = (delta_plus - delta_minus)/2 + +import yaml + + +def symmetrize_errors(delta_plus, delta_minus): + r"""Compute the symmterized uncertainty and the shift in data point. + Parameters + ---------- + delta_plus : float + The top/plus uncertainty with sign + delta_minus : float + The bottom/minus uncertainty with sign + + Returns + ------- + se_delta : float + The value to be added to the data point + se_sigma : float + The symmetrized uncertainty to be used in commondata + """ + 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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -22,7 +40,7 @@ def processData(): kin_altcorr1 = [] error_altcorr1 = [] -# jet data + # jet data for i in tables: if i == 1: @@ -44,7 +62,7 @@ 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) @@ -57,21 +75,32 @@ 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 = symmetrize_errors( + 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'} @@ -81,15 +110,15 @@ 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: @@ -111,7 +140,7 @@ 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) @@ -124,36 +153,52 @@ 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 = symmetrize_errors( + 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_2JET_13TEV_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_2JET_13TEV_DIF/filter.py index 5c993ce57e..25c4cd9e5a 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 @@ -1,14 +1,31 @@ -import yaml from math import sqrt -def se(delta_plus, delta_minus): - - semi_diff = (delta_plus + delta_minus)/2 - average = (delta_plus - delta_minus)/2 +import yaml + + +def symmetrize_errors(delta_plus, delta_minus): + r"""Compute the symmterized uncertainty and the shift in data point. + Parameters + ---------- + delta_plus : float + The top/plus uncertainty with sign + delta_minus : float + The bottom/minus uncertainty with sign + + Returns + ------- + se_delta : float + The value to be added to the data point + se_sigma : float + The symmetrized uncertainty to be used in commondata + """ + 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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -39,7 +56,7 @@ 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) @@ -52,21 +69,32 @@ 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 = symmetrize_errors( + 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'} @@ -76,12 +104,13 @@ 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_TTBAR_13TEV_LJ_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/ATLAS_TTBAR_13TEV_LJ_DIF/filter.py index 46f688a787..b9f78bafd1 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 @@ -1,30 +1,100 @@ -import yaml -import numpy as np - from math import sqrt + +import numpy as np from numpy.linalg import eig +import yaml + + +def symmetrize_errors(delta_plus, delta_minus): + r"""Compute the symmterized uncertainty and the shift in data point. + Parameters + ---------- + delta_plus : float + The top/plus uncertainty with sign + delta_minus : float + The bottom/minus uncertainty with sign -def se(delta_plus, delta_minus): - - semi_diff = (delta_plus + delta_minus)/2 - average = (delta_plus - delta_minus)/2 + Returns + ------- + se_delta : float + The value to be added to the data point + se_sigma : float + The symmetrized uncertainty to be used in commondata + + """ + 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 pta(percentage, value): - + +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. + 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 + the computation. + value : float + The data point + + Returns + ------- + 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 cta(ndata, covmat_list, no_of_norm_mat=0): - + +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): + r"""Convert the covariance matrix to a matrix of + artificial uncertainties. + + Parameters + ---------- + ndata : integer + Number of data points + 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 + simplifies the implementation for the user. + no_of_norm_mat : int + Normalized covariance matrices may have an eigenvalue + of 0 due to the last data point not being linearly + independent. To allow for this, the user should input + the number of normalized matrices that are being treated + 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 + distributions), the input would be 3. The default value is + 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 + artificial uncertainties of the i^th data point. + + """ epsilon = -0.0000000001 neg_eval_count = 0 psd_check = True @@ -52,9 +122,10 @@ def cta(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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -102,18 +173,18 @@ 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) + artUncMat_dSig_dmttBar = covmat_to_artunc(ndata_dSig_dmttBar, covMatArray_dSig_dmttBar) sqrts = float(input['dependent_variables'][0]['qualifiers'][0]['value']) m_t2 = 29756.25 @@ -125,20 +196,24 @@ def processData(): value_delta = 0 error_value = {} # error_label = str(values[i]['errors'][0]['label']) - # error_value[error_label] = pta(values[i]['errors'][0]['symerror'], values[i]['value']) + # error_value[error_label] = percentage_to_absolute(values[i]['errors'][0]['symerror'], values[i]['value']) # for j in range(1, len(values[i]['errors'])): - # plus = pta(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) - # minus = pta(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) - # se_delta, se_sigma = se(plus, minus) + # plus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) + # minus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) + # se_delta, se_sigma = symmetrize_errors(plus, minus) # 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 = {} @@ -147,33 +222,42 @@ 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) + artUncMat_dSig_dmttBar_norm = covmat_to_artunc( + ndata_dSig_dmttBar_norm, covMatArray_dSig_dmttBar_norm + ) sqrts = float(input['dependent_variables'][0]['qualifiers'][0]['value']) m_t2 = 29756.25 @@ -185,20 +269,24 @@ def processData(): value_delta = 0 error_value = {} # error_label = str(values[i]['errors'][0]['label']) - # error_value[error_label] = pta(values[i]['errors'][0]['symerror'], values[i]['value']) + # error_value[error_label] = percentage_to_absolute(values[i]['errors'][0]['symerror'], values[i]['value']) # for j in range(1, len(values[i]['errors'])): - # plus = pta(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) - # minus = pta(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) - # se_delta, se_sigma = se(plus, minus) + # plus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) + # minus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) + # se_delta, se_sigma = symmetrize_errors(plus, minus) # 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 = {} @@ -207,33 +295,40 @@ 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) + artUncMat_dSig_dpTt = covmat_to_artunc(ndata_dSig_dpTt, covMatArray_dSig_dpTt) sqrts = float(input['dependent_variables'][0]['qualifiers'][0]['value']) m_t2 = 29756.25 @@ -245,20 +340,24 @@ def processData(): value_delta = 0 error_value = {} # error_label = str(values[i]['errors'][0]['label']) - # error_value[error_label] = pta(values[i]['errors'][0]['symerror'], values[i]['value']) + # error_value[error_label] = percentage_to_absolute(values[i]['errors'][0]['symerror'], values[i]['value']) # for j in range(1, len(values[i]['errors'])): - # plus = pta(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) - # minus = pta(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) - # se_delta, se_sigma = se(plus, minus) + # plus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) + # minus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) + # se_delta, se_sigma = symmetrize_errors(plus, minus) # 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 = {} @@ -267,33 +366,40 @@ 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) + artUncMat_dSig_dpTt_norm = covmat_to_artunc(ndata_dSig_dpTt_norm, covMatArray_dSig_dpTt_norm) sqrts = float(input['dependent_variables'][0]['qualifiers'][0]['value']) m_t2 = 29756.25 @@ -305,20 +411,24 @@ def processData(): value_delta = 0 error_value = {} # error_label = str(values[i]['errors'][0]['label']) - # error_value[error_label] = pta(values[i]['errors'][0]['symerror'], values[i]['value']) + # error_value[error_label] = percentage_to_absolute(values[i]['errors'][0]['symerror'], values[i]['value']) # for j in range(1, len(values[i]['errors'])): - # plus = pta(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) - # minus = pta(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) - # se_delta, se_sigma = se(plus, minus) + # plus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) + # minus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) + # se_delta, se_sigma = symmetrize_errors(plus, minus) # 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 = {} @@ -327,33 +437,40 @@ 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) + artUncMat_dSig_dyt = covmat_to_artunc(ndata_dSig_dyt, covMatArray_dSig_dyt) sqrts = float(input['dependent_variables'][0]['qualifiers'][0]['value']) m_t2 = 29756.25 @@ -365,20 +482,24 @@ def processData(): value_delta = 0 error_value = {} # error_label = str(values[i]['errors'][0]['label']) - # error_value[error_label] = pta(values[i]['errors'][0]['symerror'], values[i]['value']) + # error_value[error_label] = percentage_to_absolute(values[i]['errors'][0]['symerror'], values[i]['value']) # for j in range(1, len(values[i]['errors'])): - # plus = pta(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) - # minus = pta(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) - # se_delta, se_sigma = se(plus, minus) + # plus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) + # minus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) + # se_delta, se_sigma = symmetrize_errors(plus, minus) # 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 = {} @@ -387,33 +508,37 @@ 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) + artUncMat_dSig_dyt_norm = covmat_to_artunc(ndata_dSig_dyt_norm, covMatArray_dSig_dyt_norm) sqrts = float(input['dependent_variables'][0]['qualifiers'][0]['value']) m_t2 = 29756.25 @@ -425,20 +550,24 @@ def processData(): value_delta = 0 error_value = {} # error_label = str(values[i]['errors'][0]['label']) - # error_value[error_label] = pta(values[i]['errors'][0]['symerror'], values[i]['value']) + # error_value[error_label] = percentage_to_absolute(values[i]['errors'][0]['symerror'], values[i]['value']) # for j in range(1, len(values[i]['errors'])): - # plus = pta(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) - # minus = pta(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) - # se_delta, se_sigma = se(plus, minus) + # plus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) + # minus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) + # se_delta, se_sigma = symmetrize_errors(plus, minus) # 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 = {} @@ -447,33 +576,40 @@ 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) + artUncMat_dSig_dyttBar = covmat_to_artunc(ndata_dSig_dyttBar, covMatArray_dSig_dyttBar) sqrts = float(input['dependent_variables'][0]['qualifiers'][0]['value']) m_t2 = 29756.25 @@ -485,20 +621,24 @@ def processData(): value_delta = 0 error_value = {} # error_label = str(values[i]['errors'][0]['label']) - # error_value[error_label] = pta(values[i]['errors'][0]['symerror'], values[i]['value']) + # error_value[error_label] = percentage_to_absolute(values[i]['errors'][0]['symerror'], values[i]['value']) # for j in range(1, len(values[i]['errors'])): - # plus = pta(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) - # minus = pta(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) - # se_delta, se_sigma = se(plus, minus) + # plus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) + # minus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) + # se_delta, se_sigma = symmetrize_errors(plus, minus) # 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 = {} @@ -507,33 +647,42 @@ 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) + artUncMat_dSig_dyttBar_norm = covmat_to_artunc( + ndata_dSig_dyttBar_norm, covMatArray_dSig_dyttBar_norm + ) sqrts = float(input['dependent_variables'][0]['qualifiers'][0]['value']) m_t2 = 29756.25 @@ -545,20 +694,24 @@ def processData(): value_delta = 0 error_value = {} # error_label = str(values[i]['errors'][0]['label']) - # error_value[error_label] = pta(values[i]['errors'][0]['symerror'], values[i]['value']) + # error_value[error_label] = percentage_to_absolute(values[i]['errors'][0]['symerror'], values[i]['value']) # for j in range(1, len(values[i]['errors'])): - # plus = pta(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) - # minus = pta(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) - # se_delta, se_sigma = se(plus, minus) + # plus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['plus'], values[i]['value']) + # minus = percentage_to_absolute(values[i]['errors'][j]['asymerror']['minus'], values[i]['value']) + # se_delta, se_sigma = symmetrize_errors(plus, minus) # 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 = {} @@ -567,19 +720,27 @@ 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 a83f9d3d0a..1ebcf20957 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 @@ -1,10 +1,45 @@ -import yaml from math import sqrt + import numpy as np from numpy.linalg import eig +import yaml + -def cta(ndata, covmat_list, no_of_norm_mat=0): - +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): + r"""Convert the covariance matrix to a matrix of + artificial uncertainties. + + Parameters + ---------- + ndata : integer + Number of data points + 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 + simplifies the implementation for the user. + no_of_norm_mat : int + Normalized covariance matrices may have an eigenvalue + of 0 due to the last data point not being linearly + independent. To allow for this, the user should input + the number of normalized matrices that are being treated + 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 + distributions), the input would be 3. The default value is + 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 + artificial uncertainties of the i^th data point. + + """ epsilon = -0.0000000001 neg_eval_count = 0 psd_check = True @@ -32,9 +67,10 @@ def cta(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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -57,192 +93,239 @@ 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) + artUnc_dSig_dmttBar = covmat_to_artunc(len(values), covMatArray_dSig_dmttBar) for i in range(len(values)): m_ttBar_min = input['independent_variables'][0]['values'][i]['low'] 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) - artUnc_dSig_dmttBar_norm = cta(len(values), covMatArray_dSig_dmttBar_norm, 1) + + 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 = covmat_to_artunc(len(values), covMatArray_dSig_dmttBar_norm, 1) for i in range(len(values)): m_ttBar_min = input['independent_variables'][0]['values'][i]['low'] 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) + artUnc_dSig_dyttBar = covmat_to_artunc(len(values), covMatArray_dSig_dyttBar) for i in range(len(values)): y_ttBar_min = input['independent_variables'][0]['values'][i]['low'] 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) + artUnc_dSig_dyttBar_norm = covmat_to_artunc(len(values), covMatArray_dSig_dyttBar_norm, 1) for i in range(len(values)): y_ttBar_min = input['independent_variables'][0]['values'][i]['low'] 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 df10182dba..4afeb24568 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 @@ -1,11 +1,32 @@ -import yaml from math import sqrt + import numpy as np from numpy.linalg import eig +import yaml + +def cormat_to_covmat(err_list, cormat_list): + r"""Convert correlation matrix elements to covariance + matrix elements. -def ctc(err_list, cormat_list): - + Parameters + ---------- + err_list : 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 + the correlation matrix row by row. Since experimental + 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)): a = i // len(err_list) @@ -13,8 +34,42 @@ def ctc(err_list, cormat_list): covmat_list.append(cormat_list[i] * err_list[a] * err_list[b]) return covmat_list -def cta(ndata, covmat_list, no_of_norm_mat=0): - + +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): + r"""Convert the covariance matrix to a matrix of + artificial uncertainties. + + Parameters + ---------- + ndata : integer + Number of data points + 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 + simplifies the implementation for the user. + no_of_norm_mat : int + Normalized covariance matrices may have an eigenvalue + of 0 due to the last data point not being linearly + independent. To allow for this, the user should input + the number of normalized matrices that are being treated + 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 + distributions), the input would be 3. The default value is + 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 + artificial uncertainties of the i^th data point. + + """ epsilon = -0.0000000001 neg_eval_count = 0 psd_check = True @@ -42,21 +97,77 @@ def cta(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 pta(percentage, value): - + +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. + 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 + the computation. + value : float + The data point + + Returns + ------- + 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 cm(rows, columns, list_of_matrices): - + +def concat_matrices(rows, columns, list_of_matrices): + r"""Join smaller matrices into a large matrix. + + This function aims to simplify the process of joining multiple + smaller matrices into one large matrix. Such a need could arise, + for instance, when cross variable covariance matrices are provided + but the user needs to join all the matrices to generate the full + covariance matrix corresponding to the entire dataset. + + Parameters + ---------- + rows : int + 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 + above example, this would be 3. + list_of_matrices : list + 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, + 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 + undesired behavior. + + Returns + ------- + 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]) col_list = [] @@ -72,8 +183,34 @@ def cm(rows, columns, list_of_matrices): final_mat_list.append(final_mat[i][j]) return final_mat_list -def mtm(rows, columns, mat_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 + 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 + No. of rows in the matrix + columns : int + No. of columns in the matrix + mat_list : list + A one dimensional list which contains the elements of + the matrix row by row. + + Returns + ------- + matrix : numpy.ndarray + The matrix as a numpy 2d array. + + """ if rows * columns == len(mat_list): matrix = np.zeros((rows, columns)) for i in range(rows): @@ -84,185 +221,226 @@ def mtm(rows, columns, mat_list): else: raise Exception('rows * columns != len(mat_list)') + def artunc(): statArr = [] for i in [23, 29, 31, 27]: - with open('rawdata/Table_'+str(i)+'.yaml', 'r') as file: + with open('rawdata/Table_' + str(i) + '.yaml', 'r') as file: input = yaml.safe_load(file) for j in range(len(input['dependent_variables'][0]['values'])): datval = input['dependent_variables'][0]['values'][j]['value'] statperc = input['dependent_variables'][0]['values'][j]['errors'][0]['symerror'] - statArr.append(pta(statperc, datval)) - -# mttbar(7)| pTt (8)| yt(5)| yttbar(5) -# mttbar| 179 174t 170t 177t -# pTt | 174 172 168t 173 -# yt | 170 168 167 169 -# yttbar| 177 173t 169t 176 + statArr.append(percentage_to_absolute(statperc, datval)) + + # mttbar(7)| pTt (8)| yt(5)| yttbar(5) + # mttbar| 179 174t 170t 177t + # pTt | 174 172 168t 173 + # yt | 170 168 167 169 + # yttbar| 177 173t 169t 176 ml179, ml174, ml170, ml177, ml172, ml168, ml167, ml173, ml169, ml176 = ([] for i in range(10)) - + with open('rawdata/Table_179.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml179.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_174.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml174.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_170.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml170.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_177.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml177.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_172.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml172.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_168.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml168.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_167.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml167.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_173.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml173.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_169.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml169.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_176.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml176.append(input['dependent_variables'][0]['values'][i]['value']) - - mat179 = mtm(7, 7, ml179) - mat174 = mtm(8, 7, ml174) + + mat179 = matlist_to_matrix(7, 7, ml179) + mat174 = matlist_to_matrix(8, 7, ml174) mat174t = mat174.transpose() - mat170 = mtm(5, 7, ml170) + mat170 = matlist_to_matrix(5, 7, ml170) mat170t = mat170.transpose() - mat177 = mtm(5, 7, ml177) + mat177 = matlist_to_matrix(5, 7, ml177) mat177t = mat177.transpose() - mat172 = mtm(8, 8, ml172) - mat168 = mtm(5, 8, ml168) + mat172 = matlist_to_matrix(8, 8, ml172) + mat168 = matlist_to_matrix(5, 8, ml168) mat168t = mat168.transpose() - mat167 = mtm(5, 5, ml167) - mat173 = mtm(8, 5, ml173) + mat167 = matlist_to_matrix(5, 5, ml167) + mat173 = matlist_to_matrix(8, 5, ml173) mat173t = mat173.transpose() - mat169 = mtm(5, 5, ml169) + mat169 = matlist_to_matrix(5, 5, ml169) mat169t = mat169.transpose() - mat176 = mtm(5, 5, ml176) + mat176 = matlist_to_matrix(5, 5, ml176) - cormatlist = cm(4, 4, [mat179, mat174t, mat170t, mat177t, mat174, mat172, mat168t, mat173, - mat170, mat168, mat167, mat169, mat177, mat173t, mat169t, mat176]) + cormatlist = concat_matrices( + 4, + 4, + [ + mat179, + mat174t, + mat170t, + mat177t, + mat174, + mat172, + mat168t, + mat173, + mat170, + mat168, + mat167, + mat169, + mat177, + mat173t, + mat169t, + mat176, + ], + ) - covmatlist = ctc(statArr, cormatlist) - artunc = cta(25, covmatlist) + covmatlist = cormat_to_covmat(statArr, cormatlist) + artunc = covmat_to_artunc(25, covmatlist) return artunc def artunc_norm(): statArr = [] for i in [24, 30, 32, 28]: - with open('rawdata/Table_'+str(i)+'.yaml', 'r') as file: + with open('rawdata/Table_' + str(i) + '.yaml', 'r') as file: input = yaml.safe_load(file) for j in range(len(input['dependent_variables'][0]['values'])): datval = input['dependent_variables'][0]['values'][j]['value'] statperc = input['dependent_variables'][0]['values'][j]['errors'][0]['symerror'] - statArr.append(pta(statperc, datval)) - -# mttbar(7)| pTt (8)| yt(5)| yttbar(5) -# mttbar| 234 229t 225t 232t -# pTt | 229 227 223t 228 -# yt | 225 223 222 224 -# yttbar| 232 228t 224t 231 + statArr.append(percentage_to_absolute(statperc, datval)) + + # mttbar(7)| pTt (8)| yt(5)| yttbar(5) + # mttbar| 234 229t 225t 232t + # pTt | 229 227 223t 228 + # yt | 225 223 222 224 + # yttbar| 232 228t 224t 231 ml234, ml229, ml225, ml232, ml227, ml223, ml222, ml228, ml224, ml231 = ([] for i in range(10)) - + with open('rawdata/Table_234.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml234.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_229.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml229.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_225.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml225.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_232.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml232.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_227.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml227.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_223.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml223.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_222.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml222.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_228.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml228.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_224.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml224.append(input['dependent_variables'][0]['values'][i]['value']) - + with open('rawdata/Table_231.yaml', 'r') as file: input = yaml.safe_load(file) for i in range(len(input['dependent_variables'][0]['values'])): ml231.append(input['dependent_variables'][0]['values'][i]['value']) - - mat234 = mtm(7, 7, ml234) - mat229 = mtm(8, 7, ml229) + + mat234 = matlist_to_matrix(7, 7, ml234) + mat229 = matlist_to_matrix(8, 7, ml229) mat229t = mat229.transpose() - mat225 = mtm(5, 7, ml225) + mat225 = matlist_to_matrix(5, 7, ml225) mat225t = mat225.transpose() - mat232 = mtm(5, 7, ml232) + mat232 = matlist_to_matrix(5, 7, ml232) mat232t = mat232.transpose() - mat227 = mtm(8, 8, ml227) - mat223 = mtm(5, 8, ml223) + mat227 = matlist_to_matrix(8, 8, ml227) + mat223 = matlist_to_matrix(5, 8, ml223) mat223t = mat223.transpose() - mat222 = mtm(5, 5, ml222) - mat228 = mtm(8, 5, ml228) + mat222 = matlist_to_matrix(5, 5, ml222) + mat228 = matlist_to_matrix(8, 5, ml228) mat228t = mat228.transpose() - mat224 = mtm(5, 5, ml224) + mat224 = matlist_to_matrix(5, 5, ml224) mat224t = mat224.transpose() - mat231 = mtm(5, 5, ml231) + mat231 = matlist_to_matrix(5, 5, ml231) - cormatlist = cm(4, 4, [mat234, mat229t, mat225t, mat232t, mat229, mat227, mat223t, mat228, - mat225, mat223, mat222, mat224, mat232, mat228t, mat224t, mat231]) + cormatlist = concat_matrices( + 4, + 4, + [ + mat234, + mat229t, + mat225t, + mat232t, + mat229, + mat227, + mat223t, + mat228, + mat225, + mat223, + mat222, + mat224, + mat232, + mat228t, + mat224t, + mat231, + ], + ) - covmatlist = ctc(statArr, cormatlist) - artunc = cta(25, covmatlist, 4) + covmatlist = cormat_to_covmat(statArr, cormatlist) + artunc = covmat_to_artunc(25, covmatlist, 4) return artunc 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 52cb35b39a..7a7c83d896 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 @@ -1,28 +1,66 @@ +from math import sqrt +from pathlib import Path +import re + import artunc import yaml -import re -from pathlib import Path -from math import sqrt -def pta(percentage, value): - +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. + 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 + the computation. + value : float + The data point + + Returns + ------- + 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 se(delta_plus, delta_minus): - - semi_diff = (delta_plus + delta_minus)/2 - average = (delta_plus - delta_minus)/2 + +def symmetrize_errors(delta_plus, delta_minus): + r"""Compute the symmterized uncertainty and the shift in data point. + + Parameters + ---------- + delta_plus : float + The top/plus uncertainty with sign + delta_minus : float + The bottom/minus uncertainty with sign + + Returns + ------- + se_delta : float + The value to be added to the data point + se_sigma : float + The symmetrized uncertainty to be used in commondata + + """ + 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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -54,12 +92,12 @@ 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'] @@ -70,48 +108,91 @@ 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( + " ", "" + ) + ] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + percentage_to_absolute( + 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) + error_value['lumi'] = percentage_to_absolute( + 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'] @@ -122,46 +203,83 @@ 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( + " ", "" + ) + ] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + percentage_to_absolute( + 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'] @@ -172,48 +290,91 @@ 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( + " ", "" + ) + ] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + percentage_to_absolute( + 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) + error_value['lumi'] = percentage_to_absolute( + 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'] @@ -224,46 +385,83 @@ 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( + " ", "" + ) + ] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + percentage_to_absolute( + 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'] @@ -274,48 +472,88 @@ 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( + " ", "" + ) + ] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + percentage_to_absolute( + 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) + error_value['lumi'] = percentage_to_absolute( + 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'] @@ -326,46 +564,83 @@ 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( + " ", "" + ) + ] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + percentage_to_absolute( + 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'] @@ -376,48 +651,91 @@ 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( + " ", "" + ) + ] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + percentage_to_absolute( + 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) + error_value['lumi'] = percentage_to_absolute( + 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'] @@ -428,40 +746,78 @@ 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( + " ", "" + ) + ] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + input['dependent_variables'][1]['values'][i]['errors'][j]['asymerror'][ + 'plus' + ], + data_central_value, + ), + percentage_to_absolute( + 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,"})') @@ -469,5 +825,6 @@ 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_TTBAR_13TEV_2L_DIF/filter.py b/nnpdf_data/nnpdf_data/new_commondata/CMS_TTBAR_13TEV_2L_DIF/filter.py index 4f618ec2b7..f252a5b517 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 @@ -1,12 +1,45 @@ -import yaml +from math import sqrt import numpy as np - -from math import sqrt from numpy.linalg import eig +import yaml + -def cta(ndata, covmat_list, no_of_norm_mat=0): - +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): + r"""Convert the covariance matrix to a matrix of + artificial uncertainties. + + Parameters + ---------- + ndata : integer + Number of data points + 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 + simplifies the implementation for the user. + no_of_norm_mat : int + Normalized covariance matrices may have an eigenvalue + of 0 due to the last data point not being linearly + independent. To allow for this, the user should input + the number of normalized matrices that are being treated + 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 + distributions), the input would be 3. The default value is + 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 + artificial uncertainties of the i^th data point. + + """ epsilon = -0.0000000001 neg_eval_count = 0 psd_check = True @@ -34,9 +67,10 @@ def cta(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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -75,18 +109,17 @@ 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) - + artunc_dSig_dpTt = covmat_to_artunc(6, covmat_dSig_dpTt, 0) sqrts = 13000.0 m_t2 = 29756.25 @@ -98,41 +131,51 @@ 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) - + artunc_dSig_dpTt_norm = covmat_to_artunc(5, covmat_dSig_dpTt_norm, 1) sqrts = 13000.0 m_t2 = 29756.25 @@ -144,41 +187,51 @@ 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) - + artunc_dSig_dmttBar = covmat_to_artunc(7, covmat_dSig_dmttBar, 0) sqrts = 13000.0 m_t2 = 29756.25 @@ -190,41 +243,51 @@ 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) - + artunc_dSig_dmttBar_norm = covmat_to_artunc(6, covmat_dSig_dmttBar_norm, 1) sqrts = 13000.0 m_t2 = 29756.25 @@ -236,41 +299,51 @@ 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) - + artunc_dSig_dyt = covmat_to_artunc(10, covmat_dSig_dyt, 0) sqrts = 13000.0 m_t2 = 29756.25 @@ -282,41 +355,48 @@ 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) - + artunc_dSig_dyt_norm = covmat_to_artunc(9, covmat_dSig_dyt_norm, 1) sqrts = 13000.0 m_t2 = 29756.25 @@ -328,41 +408,51 @@ 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) - + artunc_dSig_dyttBar = covmat_to_artunc(10, covmat_dSig_dyttBar, 0) sqrts = 13000.0 m_t2 = 29756.25 @@ -374,41 +464,51 @@ 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) - + artunc_dSig_dyttBar_norm = covmat_to_artunc(9, covmat_dSig_dyttBar_norm, 1) sqrts = 13000.0 m_t2 = 29756.25 @@ -420,27 +520,39 @@ 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 0f771e9c93..4a5ee5fb5b 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 @@ -1,11 +1,12 @@ -import yaml -import numpy as np - from math import sqrt + +import numpy as np from numpy.linalg import eig +import yaml + -def cta(ndata, covmat_list, no_of_norm_mat=0): - r"""Convert the covariance matrix to a matrix of +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): + r"""Convert the covariance matrix to a matrix of artificial uncertainties. Parameters @@ -15,7 +16,7 @@ def cta(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 @@ -25,19 +26,19 @@ def cta(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 @@ -66,9 +67,10 @@ def cta(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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -137,69 +139,89 @@ 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) + artUncMat_dSig_dmttBar = covmat_to_artunc(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) + artUncMat_dSig_dmttBar_norm = covmat_to_artunc( + ndata_dSig_dmttBar_norm, covMatArray_dSig_dmttBar_norm, 1 + ) sqrts = 13000 m_t2 = 29756.25 @@ -210,47 +232,66 @@ 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) + artUncMat_dSig_dyttBar = covmat_to_artunc(ndata_dSig_dyttBar, covMatArray_dSig_dyttBar, 0) sqrts = 13000 m_t2 = 29756.25 @@ -261,47 +302,68 @@ 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) + artUncMat_dSig_dyttBar_norm = covmat_to_artunc( + ndata_dSig_dyttBar_norm, covMatArray_dSig_dyttBar_norm, 1 + ) sqrts = 13000 m_t2 = 29756.25 @@ -312,46 +374,67 @@ 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 = covmat_to_artunc( + 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) @@ -359,53 +442,75 @@ 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 = covmat_to_artunc( + 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) @@ -413,237 +518,333 @@ 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) + artUncMat_dSig_dpTt = covmat_to_artunc(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) + artUncMat_dSig_dpTt_norm = covmat_to_artunc(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) + artUncMat_dSig_dyt = covmat_to_artunc(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) + artUncMat_dSig_dyt_norm = covmat_to_artunc(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 5f99aaddbc..47fe718be2 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 @@ -1,30 +1,87 @@ -import yaml -import numpy as np - from math import sqrt + +import numpy as np from numpy.linalg import eig +import yaml -def se(delta_plus, delta_minus): - - semi_diff = (delta_plus + delta_minus)/2 - average = (delta_plus - delta_minus)/2 +def symmetrize_errors(delta_plus, delta_minus): + r"""Compute the symmterized uncertainty and the shift in data point. + + Parameters + ---------- + delta_plus : float + The top/plus uncertainty with sign + delta_minus : float + The bottom/minus uncertainty with sign + + Returns + ------- + se_delta : float + The value to be added to the data point + se_sigma : float + The symmetrized uncertainty to be used in commondata + + """ + 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 pta(percentage, value): - + +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. + 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 + the computation. + value : float + The data point + + Returns + ------- + 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 ctc(err_list, cormat_list): - + +def cormat_to_covmat(err_list, cormat_list): + r"""Convert correlation matrix elements to covariance + matrix elements. + + Parameters + ---------- + err_list : 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 + the correlation matrix row by row. Since experimental + 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)): a = i // len(err_list) @@ -32,8 +89,42 @@ def ctc(err_list, cormat_list): covmat_list.append(cormat_list[i] * err_list[a] * err_list[b]) return covmat_list -def cta(ndata, covmat_list, no_of_norm_mat=0): - + +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): + r"""Convert the covariance matrix to a matrix of + artificial uncertainties. + + Parameters + ---------- + ndata : integer + Number of data points + 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 + simplifies the implementation for the user. + no_of_norm_mat : int + Normalized covariance matrices may have an eigenvalue + of 0 due to the last data point not being linearly + independent. To allow for this, the user should input + the number of normalized matrices that are being treated + 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 + distributions), the input would be 3. The default value is + 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 + artificial uncertainties of the i^th data point. + + """ epsilon = -0.0000000001 neg_eval_count = 0 psd_check = True @@ -61,17 +152,58 @@ def cta(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 ttf(mode, tri_mat_list): - - dim = int((np.sqrt(1 + 8*len(tri_mat_list)) - 1)/2) + +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 + correlation or covariance matrices as a triangular + 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 + row such as: + 0 1 2 3 + 4 5 6 + 7 8 + 9 + Use mode 1 if the matrix entries are given column + by column such as: + 0 1 3 6 + 2 4 7 + 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 + 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] + + Returns + ------- + mat_list : list + A one dimensional list which contains the elements of + the fully populated, symmetric matrix row by row. + + """ + 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: @@ -80,7 +212,7 @@ def ttf(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: @@ -94,6 +226,7 @@ def ttf(mode, tri_mat_list): mat_list.append(matrix[i][j]) return mat_list + def processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -108,31 +241,32 @@ 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 values = input['dependent_variables'][0]['values'] statlist1 = [] for i in range(len(values)): - statlist1.append(pta(str(values[i]['errors'][0]['symerror']), values[i]['value'])) + statlist1.append( + percentage_to_absolute(str(values[i]['errors'][0]['symerror']), values[i]['value']) + ) trimatlist1 = [] for i in range(len(input2['dependent_variables'][0]['values'])): trimatlist1.append(input2['dependent_variables'][0]['values'][i]['value']) - cormatlist1 = ttf(0, trimatlist1) - covmatlist1 = ctc(statlist1, cormatlist1) - artunc1 = cta(len(values), covmatlist1, 1) - + cormatlist1 = trimat_to_fullmat(0, trimatlist1) + covmatlist1 = cormat_to_covmat(statlist1, cormatlist1) + artunc1 = covmat_to_artunc(len(values), covmatlist1, 1) for i in range(len(values)): data_central_value = values[i]['value'] @@ -141,61 +275,82 @@ def processData(): pT_t_min = input['independent_variables'][1]['values'][i]['low'] pT_t_max = input['independent_variables'][1]['values'][i]['high'] error_value = {} - plus = pta(str(values[i]['errors'][1]['asymerror']['plus']), data_central_value) - minus = pta(str(values[i]['errors'][1]['asymerror']['minus']), data_central_value) - se_delta, se_sigma = se(plus, minus) + plus = percentage_to_absolute( + str(values[i]['errors'][1]['asymerror']['plus']), data_central_value + ) + minus = percentage_to_absolute( + str(values[i]['errors'][1]['asymerror']['minus']), data_central_value + ) + se_delta, se_sigma = symmetrize_errors(plus, minus) 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 values = input['dependent_variables'][0]['values'] statlist2 = [] for i in range(len(values)): - statlist2.append(pta(str(values[i]['errors'][0]['symerror']), values[i]['value'])) + statlist2.append( + percentage_to_absolute(str(values[i]['errors'][0]['symerror']), values[i]['value']) + ) trimatlist2 = [] for i in range(len(input2['dependent_variables'][0]['values'])): trimatlist2.append(input2['dependent_variables'][0]['values'][i]['value']) - cormatlist2 = ttf(0, trimatlist2) - covmatlist2 = ctc(statlist2, cormatlist2) - artunc2 = cta(len(values), covmatlist2, 1) - + cormatlist2 = trimat_to_fullmat(0, trimatlist2) + covmatlist2 = cormat_to_covmat(statlist2, cormatlist2) + artunc2 = covmat_to_artunc(len(values), covmatlist2, 1) for i in range(len(values)): data_central_value = values[i]['value'] @@ -204,61 +359,82 @@ def processData(): y_t_min = input['independent_variables'][1]['values'][i]['low'] y_t_max = input['independent_variables'][1]['values'][i]['high'] error_value = {} - plus = pta(str(values[i]['errors'][1]['asymerror']['plus']), data_central_value) - minus = pta(str(values[i]['errors'][1]['asymerror']['minus']), data_central_value) - se_delta, se_sigma = se(plus, minus) + plus = percentage_to_absolute( + str(values[i]['errors'][1]['asymerror']['plus']), data_central_value + ) + minus = percentage_to_absolute( + str(values[i]['errors'][1]['asymerror']['minus']), data_central_value + ) + se_delta, se_sigma = symmetrize_errors(plus, minus) 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 values = input['dependent_variables'][0]['values'] statlist3 = [] for i in range(len(values)): - statlist3.append(pta(str(values[i]['errors'][0]['symerror']), values[i]['value'])) + statlist3.append( + percentage_to_absolute(str(values[i]['errors'][0]['symerror']), values[i]['value']) + ) trimatlist3 = [] for i in range(len(input2['dependent_variables'][0]['values'])): trimatlist3.append(input2['dependent_variables'][0]['values'][i]['value']) - cormatlist3 = ttf(0, trimatlist3) - covmatlist3 = ctc(statlist3, cormatlist3) - artunc3 = cta(len(values), covmatlist3, 1) - + cormatlist3 = trimat_to_fullmat(0, trimatlist3) + covmatlist3 = cormat_to_covmat(statlist3, cormatlist3) + artunc3 = covmat_to_artunc(len(values), covmatlist3, 1) for i in range(len(values)): data_central_value = values[i]['value'] @@ -267,34 +443,57 @@ def processData(): y_ttBar_min = input['independent_variables'][1]['values'][i]['low'] y_ttBar_max = input['independent_variables'][1]['values'][i]['high'] error_value = {} - plus = pta(str(values[i]['errors'][1]['asymerror']['plus']), data_central_value) - minus = pta(str(values[i]['errors'][1]['asymerror']['minus']), data_central_value) - se_delta, se_sigma = se(plus, minus) + plus = percentage_to_absolute( + str(values[i]['errors'][1]['asymerror']['plus']), data_central_value + ) + minus = percentage_to_absolute( + str(values[i]['errors'][1]['asymerror']['minus']), data_central_value + ) + se_delta, se_sigma = symmetrize_errors(plus, minus) 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 92d524ea5b..4ac580504d 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 @@ -1,21 +1,75 @@ -import yaml -import numpy as np - from math import sqrt + +import numpy as np from numpy.linalg import eig +import yaml + -def pta(percentage, value): - +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. + 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 + the computation. + value : float + The data point + + Returns + ------- + 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 cta(ndata, covmat_list, no_of_norm_mat=0): - + +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): + r"""Convert the covariance matrix to a matrix of + artificial uncertainties. + + Parameters + ---------- + ndata : integer + Number of data points + 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 + simplifies the implementation for the user. + no_of_norm_mat : int + Normalized covariance matrices may have an eigenvalue + of 0 due to the last data point not being linearly + independent. To allow for this, the user should input + the number of normalized matrices that are being treated + 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 + distributions), the input would be 3. The default value is + 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 + artificial uncertainties of the i^th data point. + + """ epsilon = -0.0000000001 neg_eval_count = 0 psd_check = True @@ -43,9 +97,10 @@ def cta(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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -73,24 +128,24 @@ 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) + artUncMat_dSig_dpTt = covmat_to_artunc(ndata_dSig_dpTt, covMatArray_dSig_dpTt, 1) sqrts = float(input['dependent_variables'][0]['qualifiers'][2]['value']) m_t2 = 29756.25 @@ -104,54 +159,78 @@ 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']] = ( + percentage_to_absolute( + 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) + artUncMat_dSig_dyt = covmat_to_artunc(ndata_dSig_dyt, covMatArray_dSig_dyt, 1) sqrts = float(input['dependent_variables'][0]['qualifiers'][2]['value']) m_t2 = 29756.25 @@ -165,54 +244,78 @@ 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']] = ( + percentage_to_absolute( + 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) + artUncMat_dSig_dyttBar = covmat_to_artunc(ndata_dSig_dyttBar, covMatArray_dSig_dyttBar, 1) sqrts = float(input['dependent_variables'][0]['qualifiers'][2]['value']) m_t2 = 29756.25 @@ -226,54 +329,78 @@ 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']] = ( + percentage_to_absolute( + 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) + artUncMat_dSig_dmttBar = covmat_to_artunc(ndata_dSig_dmttBar, covMatArray_dSig_dmttBar, 1) sqrts = float(input['dependent_variables'][0]['qualifiers'][2]['value']) m_t2 = 29756.25 @@ -287,34 +414,59 @@ 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']] = ( + percentage_to_absolute( + 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/H1_1JET_319GEV_290PB-1_DIF/artUnc.py b/nnpdf_data/nnpdf_data/new_commondata/H1_1JET_319GEV_290PB-1_DIF/artUnc.py index ebb4dcbc8f..706e17dd90 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 @@ -1,21 +1,23 @@ -import yaml -import numpy as np - from math import sqrt + +import numpy as np from numpy.linalg import eig +import yaml + + +def percentage_to_absolute(percentage, value): -def pta(percentage, value): - 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 cta(ndata, covmat_list, no_of_norm_mat=0): - + +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): + epsilon = -0.0000000001 neg_eval_count = 0 psd_check = True @@ -43,9 +45,10 @@ def cta(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 artunc(): with open('rawdata/data49.yaml', 'r') as file: @@ -58,7 +61,7 @@ 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'] @@ -67,17 +70,16 @@ def artunc(): errPercArr.append(errPerc) dataArr.append(float(values[j]['value'])) - errArr = [] for i in range(96): - errArr.append(pta(errPercArr[i], dataArr[i])) + errArr.append(percentage_to_absolute(errPercArr[i], dataArr[i])) covMat = np.zeros((96, 96)) artUnc = np.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: @@ -88,12 +90,13 @@ def artunc(): for i in range(96): for j in range(96): covMatList.append(covMat[i][j]) - artUnc = cta(96, covMatList, 0) + artUnc = covmat_to_artunc(96, covMatList, 0) return artUnc + def artunc_norm(): - + with open('rawdata/data50.yaml', 'r') as file: corMatFile = yaml.safe_load(file) @@ -104,7 +107,7 @@ 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'] @@ -115,14 +118,14 @@ def artunc_norm(): errArr = [] for i in range(96): - errArr.append(pta(errPercArr[i], dataArr[i])) + errArr.append(percentage_to_absolute(errPercArr[i], dataArr[i])) covMat = np.zeros((96, 96)) artUnc = np.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: @@ -133,6 +136,6 @@ def artunc_norm(): for i in range(96): for j in range(96): covMatList.append(covMat[i][j]) - artUnc = cta(96, covMatList, 1) + artUnc = covmat_to_artunc(96, covMatList, 1) return 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 0cca31b201..2ac801f886 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 @@ -1,25 +1,29 @@ +from math import sqrt + import artUnc import yaml -from math import sqrt -def se(delta_plus, delta_minus): - - semi_diff = (delta_plus + delta_minus)/2 - average = (delta_plus - delta_minus)/2 + +def symmetrize_errors(delta_plus, delta_minus): + + 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 pta(percentage, value): - + +def percentage_to_absolute(percentage, value): + 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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -45,7 +49,7 @@ def processData(): artUncMatr = artUnc.artunc() artUncMatr_norm = artUnc.artunc_norm() -# jet data + # jet data for i in tables: if i == 1: @@ -73,7 +77,7 @@ 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) @@ -84,53 +88,107 @@ 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']] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ), + percentage_to_absolute( + 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'] = percentage_to_absolute( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ) + / sqrt(2), + percentage_to_absolute( + 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} @@ -145,7 +203,7 @@ 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: @@ -173,7 +231,7 @@ 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) @@ -184,53 +242,107 @@ 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']] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ), + percentage_to_absolute( + 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'] = percentage_to_absolute( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ) + / sqrt(2), + percentage_to_absolute( + 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} @@ -245,12 +357,12 @@ 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 @@ -260,49 +372,90 @@ 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']] = percentage_to_absolute( + values[j]['errors'][k]['symerror'], data_central_value + ) + else: + se_delta, se_sigma = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ), + percentage_to_absolute( + 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'] = percentage_to_absolute( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ) + / sqrt(2), + percentage_to_absolute( + 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) @@ -312,12 +465,12 @@ 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 @@ -327,46 +480,90 @@ 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']] = percentage_to_absolute( + values[j]['errors'][k]['symerror'], data_central_value + ) + else: + se_delta, se_sigma = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ), + percentage_to_absolute( + 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'] = percentage_to_absolute( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ) + / sqrt(2), + percentage_to_absolute( + 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) @@ -377,4 +574,5 @@ 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 a12d070cd5..a4eed2bdfb 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 @@ -1,16 +1,18 @@ +from manual_impl import artunc, jet_data, jet_sys import yaml -from manual_impl import jet_data, jet_sys, artunc -def pta(percentage, value): - + +def percentage_to_absolute(percentage, value): + 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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -28,9 +30,9 @@ 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) @@ -45,49 +47,57 @@ 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) + # error_value['stat'] = percentage_to_absolute(values[i]['errors'][0]['symerror'], data_central_value) + # error_value['sys'] = percentage_to_absolute(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) @@ -101,26 +111,42 @@ 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_value['stat'] = percentage_to_absolute( + values[i]['errors'][0]['symerror'], data_central_value + ) + error_value['sys'] = percentage_to_absolute( + 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 a0a0d280cd..aee379d2a1 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 @@ -3,7 +3,7 @@ from math import sqrt from numpy.linalg import eig -def ctc(err_list, cormat_list): +def cormat_to_covmat(err_list, cormat_list): covmat_list = [] for i in range(len(cormat_list)): @@ -12,7 +12,7 @@ def ctc(err_list, cormat_list): covmat_list.append(cormat_list[i] * err_list[a] * err_list[b]) return covmat_list -def cta(ndata, covmat_list, no_of_norm_mat=0): +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): epsilon = -0.0000000001 neg_eval_count = 0 @@ -135,7 +135,7 @@ def stat_lists(): jet_sys = sys_breakdown(jet_old_impl_list, True) dijet_sys = sys_breakdown(dijet_old_impl_list, False) -covmat = ctc(jet_stat + dijet_stat, [a/100 for a in corMatArray]) +covmat = cormat_to_covmat(jet_stat + dijet_stat, [a/100 for a in corMatArray]) -artunc = cta(48, covmat, ) +artunc = covmat_to_artunc(48, covmat, ) 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 ebb4dcbc8f..706e17dd90 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 @@ -1,21 +1,23 @@ -import yaml -import numpy as np - from math import sqrt + +import numpy as np from numpy.linalg import eig +import yaml + + +def percentage_to_absolute(percentage, value): -def pta(percentage, value): - 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 cta(ndata, covmat_list, no_of_norm_mat=0): - + +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): + epsilon = -0.0000000001 neg_eval_count = 0 psd_check = True @@ -43,9 +45,10 @@ def cta(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 artunc(): with open('rawdata/data49.yaml', 'r') as file: @@ -58,7 +61,7 @@ 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'] @@ -67,17 +70,16 @@ def artunc(): errPercArr.append(errPerc) dataArr.append(float(values[j]['value'])) - errArr = [] for i in range(96): - errArr.append(pta(errPercArr[i], dataArr[i])) + errArr.append(percentage_to_absolute(errPercArr[i], dataArr[i])) covMat = np.zeros((96, 96)) artUnc = np.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: @@ -88,12 +90,13 @@ def artunc(): for i in range(96): for j in range(96): covMatList.append(covMat[i][j]) - artUnc = cta(96, covMatList, 0) + artUnc = covmat_to_artunc(96, covMatList, 0) return artUnc + def artunc_norm(): - + with open('rawdata/data50.yaml', 'r') as file: corMatFile = yaml.safe_load(file) @@ -104,7 +107,7 @@ 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'] @@ -115,14 +118,14 @@ def artunc_norm(): errArr = [] for i in range(96): - errArr.append(pta(errPercArr[i], dataArr[i])) + errArr.append(percentage_to_absolute(errPercArr[i], dataArr[i])) covMat = np.zeros((96, 96)) artUnc = np.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: @@ -133,6 +136,6 @@ def artunc_norm(): for i in range(96): for j in range(96): covMatList.append(covMat[i][j]) - artUnc = cta(96, covMatList, 1) + artUnc = covmat_to_artunc(96, covMatList, 1) return 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 f02422ab0e..8b231f11b7 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 @@ -1,25 +1,29 @@ +from math import sqrt + import artUnc import yaml -from math import sqrt -def se(delta_plus, delta_minus): - - semi_diff = (delta_plus + delta_minus)/2 - average = (delta_plus - delta_minus)/2 + +def symmetrize_errors(delta_plus, delta_minus): + + 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 pta(percentage, value): - + +def percentage_to_absolute(percentage, value): + 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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -37,7 +41,7 @@ def processData(): artUncMatr = artUnc.artunc() artUncMatr_norm = artUnc.artunc_norm() -# dijet data + # dijet data for i in tables: if i == 9: @@ -65,7 +69,7 @@ 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) @@ -76,53 +80,107 @@ 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']] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ), + percentage_to_absolute( + 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'] = percentage_to_absolute( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ) + / sqrt(2), + percentage_to_absolute( + 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} @@ -137,7 +195,7 @@ 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: @@ -165,7 +223,7 @@ 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) @@ -176,52 +234,106 @@ 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']] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ), + percentage_to_absolute( + 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'] = percentage_to_absolute( + values[j]['errors'][k]['symerror'], data_central_value + ) / sqrt(2) + error_value[values[j]['errors'][k]['label'] + '_2'] = percentage_to_absolute( + 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 = symmetrize_errors( + percentage_to_absolute( + values[j]['errors'][k]['asymerror']['plus'], data_central_value + ) + / sqrt(2), + percentage_to_absolute( + 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} @@ -236,4 +348,5 @@ 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 ab835eae19..cf6210f643 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 @@ -1,16 +1,18 @@ +from manual_impl import artunc, dijet_data, dijet_sys import yaml -from manual_impl import dijet_data, dijet_sys, artunc -def pta(percentage, value): - + +def percentage_to_absolute(percentage, value): + 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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -28,9 +30,9 @@ 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) @@ -45,48 +47,57 @@ 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) + # error_value['stat'] = percentage_to_absolute(values[i]['errors'][0]['symerror'], data_central_value) + # error_value['sys'] = percentage_to_absolute(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) @@ -100,26 +111,42 @@ 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_value['stat'] = percentage_to_absolute( + values[i]['errors'][0]['symerror'], data_central_value + ) + error_value['sys'] = percentage_to_absolute( + 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 a0a0d280cd..4bb958d5da 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 @@ -1,9 +1,10 @@ -import numpy as np - from math import sqrt + +import numpy as np from numpy.linalg import eig -def ctc(err_list, cormat_list): + +def cormat_to_covmat(err_list, cormat_list): covmat_list = [] for i in range(len(cormat_list)): @@ -12,7 +13,7 @@ def ctc(err_list, cormat_list): covmat_list.append(cormat_list[i] * err_list[a] * err_list[b]) return covmat_list -def cta(ndata, covmat_list, no_of_norm_mat=0): +def covmat_to_artunc(ndata, covmat_list, no_of_norm_mat=0): epsilon = -0.0000000001 neg_eval_count = 0 @@ -135,7 +136,7 @@ def stat_lists(): jet_sys = sys_breakdown(jet_old_impl_list, True) dijet_sys = sys_breakdown(dijet_old_impl_list, False) -covmat = ctc(jet_stat + dijet_stat, [a/100 for a in corMatArray]) +covmat = cormat_to_covmat(jet_stat + dijet_stat, [a/100 for a in corMatArray]) -artunc = cta(48, covmat, ) +artunc = covmat_to_artunc(48, covmat, ) 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 ce482d047e..a4a25e1b9e 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 @@ -1,7 +1,9 @@ -import yaml from math import sqrt -def se(delta_plus, delta_minus): +import yaml + + +def symmetrize_errors(delta_plus, delta_minus): r"""Compute the symmterized uncertainty and the shift in data point. Parameters @@ -10,7 +12,7 @@ def se(delta_plus, delta_minus): The top/plus uncertainty with sign delta_minus : float The bottom/minus uncertainty with sign - + Returns ------- se_delta : float @@ -19,12 +21,13 @@ def se(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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -35,11 +38,11 @@ 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) @@ -73,26 +76,39 @@ 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 = symmetrize_errors( + 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 = symmetrize_errors( + 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 = symmetrize_errors( + 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 @@ -102,7 +118,11 @@ 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} @@ -118,4 +138,5 @@ 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 c292084054..d791f9ae38 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 @@ -1,7 +1,9 @@ -import yaml from math import sqrt -def se(delta_plus, delta_minus): +import yaml + + +def symmetrize_errors(delta_plus, delta_minus): r"""Compute the symmterized uncertainty and the shift in data point. Parameters @@ -10,7 +12,7 @@ def se(delta_plus, delta_minus): The top/plus uncertainty with sign delta_minus : float The bottom/minus uncertainty with sign - + Returns ------- se_delta : float @@ -19,10 +21,10 @@ def se(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 @@ -36,7 +38,7 @@ def processData(): kin_q2_et = [] error_q2_et = [] -# q2_et data + # q2_et data for i in tables_q2_et: if i == 12: @@ -58,7 +60,7 @@ 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) @@ -69,26 +71,39 @@ 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 = symmetrize_errors( + 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 = symmetrize_errors( + 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 = symmetrize_errors( + 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 @@ -98,7 +113,11 @@ 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} @@ -114,4 +133,5 @@ 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 dcbd5cbc91..e562d26090 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 @@ -1,7 +1,9 @@ -import yaml from math import sqrt -def se(delta_plus, delta_minus): +import yaml + + +def symmetrize_errors(delta_plus, delta_minus): r"""Compute the symmterized uncertainty and the shift in data point. Parameters @@ -10,7 +12,7 @@ def se(delta_plus, delta_minus): The top/plus uncertainty with sign delta_minus : float The bottom/minus uncertainty with sign - + Returns ------- se_delta : float @@ -19,12 +21,13 @@ def se(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 processData(): with open('metadata.yaml', 'r') as file: metadata = yaml.safe_load(file) @@ -35,7 +38,7 @@ def processData(): kin_q2_et = [] error_q2_et = [] -# q2_et data + # q2_et data for i in tables_q2_et: if i == 13: @@ -57,7 +60,7 @@ 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) @@ -68,26 +71,39 @@ 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 = symmetrize_errors( + 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 = symmetrize_errors( + 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 = symmetrize_errors( + 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 @@ -97,7 +113,11 @@ 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} @@ -113,4 +133,5 @@ def processData(): with open('uncertainties_q2_et.yaml', 'w') as file: yaml.dump(uncertainties_q2_et_yaml, file, sort_keys=False) + processData()