New metadata format

buildmaster/CMS_TTBAR_8TEV_LJ_DIF/data_dSig_dmttBar_norm.yaml

@@ -0,0 +1,8 @@
+data_central:
+- 0.00469
+- 0.0043
+- 0.00267
+- 0.00117
+- 0.000466
+- 0.000114
+- 1.1e-05

buildmaster/CMS_TTBAR_8TEV_LJ_DIF/data_dSig_dpTt_norm.yaml

@@ -0,0 +1,9 @@
+data_central:
+- 0.00414
+- 0.00669
+- 0.00496
+- 0.00266
+- 0.00106
+- 0.000399
+- 0.00013
+- 3.7e-05

buildmaster/CMS_TTBAR_8TEV_LJ_DIF/data_dSig_dyt_norm.yaml

@@ -0,0 +1,11 @@
+data_central:
+- 0.0736
+- 0.175
+- 0.261
+- 0.3
+- 0.333
+- 0.331
+- 0.3
+- 0.247
+- 0.188
+- 0.0777

buildmaster/CMS_TTBAR_8TEV_LJ_DIF/data_dSig_dyttBar_norm.yaml

@@ -0,0 +1,11 @@
+data_central:
+- 0.0607
+- 0.22
+- 0.327
+- 0.373
+- 0.427
+- 0.413
+- 0.374
+- 0.317
+- 0.23
+- 0.0641

buildmaster/CMS_TTBAR_8TEV_LJ_DIF/filter.py

@@ -0,0 +1,278 @@
+# implemented by Tanishq Sharma
+
+import yaml
+from utils import covMat_to_artUnc as cta
+from utils import percentage_to_absolute as pta
+
+def processData():
+    with open('metadata.yaml', 'r') as file:
+        metadata = yaml.safe_load(file)
+
+    ndata_dSig_dpTt = metadata['implemented_observables'][0]['ndata']
+    ndata_dSig_dyt = metadata['implemented_observables'][1]['ndata']
+    ndata_dSig_dyttBar = metadata['implemented_observables'][2]['ndata']
+    ndata_dSig_dmttBar = metadata['implemented_observables'][3]['ndata']
+
+    data_central_dSig_dpTt = []
+    kin_dSig_dpTt = []
+    error_dSig_dpTt = []
+    data_central_dSig_dyt = []
+    kin_dSig_dyt = []
+    error_dSig_dyt = []
+    data_central_dSig_dyttBar = []
+    kin_dSig_dyttBar = []
+    error_dSig_dyttBar = []
+    data_central_dSig_dmttBar = []
+    kin_dSig_dmttBar = []
+    error_dSig_dmttBar = []
+
+    covMatArray_dSig_dpTt = []
+    covMatArray_dSig_dyt = []
+    covMatArray_dSig_dyttBar = []
+    covMatArray_dSig_dmttBar = []
+
+# dSig_dpTt data
+
+    hepdata_tables="rawdata/Table15.yaml"
+    with open(hepdata_tables, 'r') as file:
+        input = yaml.safe_load(file)
+
+    covariance_matrix="rawdata/Table16.yaml"
+    with open(covariance_matrix, 'r') as file2:
+        input2 = yaml.safe_load(file2)
+
+    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):
+        covMatEl = input2['dependent_variables'][0]['values'][i]['value']
+        covMatArray_dSig_dpTt.append(covMatEl)
+    artUncMat_dSig_dpTt = cta(ndata_dSig_dpTt, covMatArray_dSig_dpTt, True)
+
+    sqrt_s = float(input['dependent_variables'][0]['qualifiers'][2]['value'])
+    mt_sqr = 29846.0176
+    values = input['dependent_variables'][0]['values']
+
+    for i in range(len(values)):
+        pT_t_min = input['independent_variables'][0]['values'][i]['low']
+        pT_t_mid = input['independent_variables'][1]['values'][i]['value']
+        pT_t_max = input['independent_variables'][0]['values'][i]['high']
+        error_value = {}
+        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])
+        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 = {'sqrt_s': {'min': None, 'mid': sqrt_s, 'max': None}, 'mt_sqr': {'min': None, 'mid': mt_sqr, '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['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'}
+    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'}
+
+    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}
+
+    with open('data_dSig_dpTt_norm.yaml', 'w') as file:
+         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)
+
+    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/Table21.yaml"
+    with open(hepdata_tables, 'r') as file:
+        input = yaml.safe_load(file)
+
+    covariance_matrix="rawdata/Table22.yaml"
+    with open(covariance_matrix, 'r') as file2:
+        input2 = yaml.safe_load(file2)
+
+    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):
+        covMatEl = input2['dependent_variables'][0]['values'][i]['value']
+        covMatArray_dSig_dyt.append(covMatEl)
+    artUncMat_dSig_dyt = cta(ndata_dSig_dyt, covMatArray_dSig_dyt, True)
+
+    sqrt_s = float(input['dependent_variables'][0]['qualifiers'][2]['value'])
+    mt_sqr = 29846.0176
+    values = input['dependent_variables'][0]['values']
+
+    for i in range(len(values)):
+        y_t_min = input['independent_variables'][0]['values'][i]['low']
+        y_t_mid = input['independent_variables'][1]['values'][i]['value']
+        y_t_max = input['independent_variables'][0]['values'][i]['high']
+        error_value = {}
+        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])
+        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 = {'sqrt_s': {'min': None, 'mid': sqrt_s, 'max': None}, 'mt_sqr': {'min': None, 'mid': mt_sqr, '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['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'}
+    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'}
+
+    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}
+
+    with open('data_dSig_dyt_norm.yaml', 'w') as file:
+         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)
+
+    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/Table36.yaml"
+    with open(hepdata_tables, 'r') as file:
+        input = yaml.safe_load(file)
+
+    covariance_matrix="rawdata/Table37.yaml"
+    with open(covariance_matrix, 'r') as file2:
+        input2 = yaml.safe_load(file2)
+
+    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):
+        covMatEl = input2['dependent_variables'][0]['values'][i]['value']
+        covMatArray_dSig_dyttBar.append(covMatEl)
+    artUncMat_dSig_dyttBar = cta(ndata_dSig_dyttBar, covMatArray_dSig_dyttBar, True)
+
+    sqrt_s = float(input['dependent_variables'][0]['qualifiers'][2]['value'])
+    mt_sqr = 29846.0176
+    values = input['dependent_variables'][0]['values']
+
+    for i in range(len(values)):
+        y_ttBar_min = input['independent_variables'][0]['values'][i]['low']
+        y_ttBar_mid = input['independent_variables'][1]['values'][i]['value']
+        y_ttBar_max = input['independent_variables'][0]['values'][i]['high']
+        error_value = {}
+        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])
+        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 = {'sqrt_s': {'min': None, 'mid': sqrt_s, 'max': None}, 'mt_sqr': {'min': None, 'mid': mt_sqr, '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['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'}
+    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'}
+
+    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}
+
+    with open('data_dSig_dyttBar_norm.yaml', 'w') as file:
+         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)
+
+    with open('uncertainties_dSig_dyttBar_norm.yaml', 'w') as file:
+        yaml.dump(uncertainties_dSig_dyttBar_norm_yaml, file, sort_keys=False)
+
+# dSig_dmttBar data
+
+    hepdata_tables="rawdata/Table39.yaml"
+    with open(hepdata_tables, 'r') as file:
+        input = yaml.safe_load(file)
+
+    covariance_matrix="rawdata/Table40.yaml"
+    with open(covariance_matrix, 'r') as file2:
+        input2 = yaml.safe_load(file2)
+
+    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):
+        covMatEl = input2['dependent_variables'][0]['values'][i]['value']
+        covMatArray_dSig_dmttBar.append(covMatEl)
+    artUncMat_dSig_dmttBar = cta(ndata_dSig_dmttBar, covMatArray_dSig_dmttBar, True)
+
+    sqrt_s = float(input['dependent_variables'][0]['qualifiers'][2]['value'])
+    mt_sqr = 29846.0176
+    values = input['dependent_variables'][0]['values']
+
+    for i in range(len(values)):
+        m_ttBar_min = input['independent_variables'][0]['values'][i]['low']
+        m_ttBar_mid = input['independent_variables'][1]['values'][i]['value']
+        m_ttBar_max = input['independent_variables'][0]['values'][i]['high']
+        error_value = {}
+        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])
+        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 = {'sqrt_s': {'min': None, 'mid': sqrt_s, 'max': None}, 'mt_sqr': {'min': None, 'mid': mt_sqr, '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['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'}
+    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'}
+
+    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}
+
+    with open('data_dSig_dmttBar_norm.yaml', 'w') as file:
+         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)
+
+    with open('uncertainties_dSig_dmttBar_norm.yaml', 'w') as file:
+        yaml.dump(uncertainties_dSig_dmttBar_norm_yaml, file, sort_keys=False)
+
+processData()