CMS_Z0_13TEV

nnpdf_data/nnpdf_data/commondata/CMS_Z0_13TEV/data_AFB.yaml

@@ -0,0 +1,64 @@
+data_central:
+- -0.0086482
+- -0.033992
+- -0.021625
+- -0.012986
+- -0.0011154
+- 0.0017664
+- 0.01436
+- 0.024078
+- 0.020788
+- 0.046908
+- 0.04152
+- -0.064782
+- -0.098499
+- -0.064554
+- -0.035726
+- -0.0093437
+- 0.015155
+- 0.030693
+- 0.067548
+- 0.07893
+- 0.12633
+- 0.17564
+- -0.13178
+- -0.18003
+- -0.11602
+- -0.064338
+- -0.018044
+- 0.026687
+- 0.05825
+- 0.10674
+- 0.14654
+- 0.21216
+- 0.30394
+- -0.21858
+- -0.26521
+- -0.13269
+- 0.039435
+- 0.18622
+- 0.32014
+- 0.43738
+- -0.27888
+- -0.37527
+- -0.20469
+- 0.057624
+- 0.27664
+- 0.45724
+- 0.63255
+- -0.3762
+- -0.52712
+- -0.2774
+- 0.081106
+- 0.35895
+- 0.62059
+- 0.80039
+- -0.45736
+- 0.10689
+- 0.49997
+- -0.59639
+- 0.1251
+- 0.65723
+- -0.67574
+- 0.15638
+- 0.75026

nnpdf_data/nnpdf_data/commondata/CMS_Z0_13TEV/filter.py

@@ -0,0 +1,128 @@
+"""
+This file contains the piece of code needed to implement the CMS AFB 
+measurement at 13 TeV. Uncertainties are obtained by combining the correlation
+matrix and the total uncertainty provided on Hepdata, after which a covariance
+matrix is constructed, which is finally decomposed into Ndat artificial
+uncertainties
+"""
+
+import yaml
+from nnpdf_data.filter_utils.utils import cormat_to_covmat,covmat_to_artunc
+
+def get_tables():
+    """
+    Get the Hepdata tables, given the tables and version specified in metadata 
+    """
+    prefix = "rawdata/HEPData-ins2818125"
+    with open("metadata.yaml", "r") as file:
+        metadata = yaml.safe_load(file)
+
+    version = metadata["hepdata"]["version"]
+    tables = metadata["implemented_observables"][0]["tables"]
+    hepdata_tables = []
+
+    for table in tables:
+        hepdata_tables.append(f"{prefix}-v{version}-{table}.yaml")
+    return hepdata_tables
+
+def get_all():
+    """
+    Returns data, kinematics and uncertainties for dumping in the .yaml files
+    """
+    data_central = []
+    kinematics = []
+    uncertainties = []
+    art_uncert = []
+    art_uncertainties = []
+    correlations = []
+    hepdata_tables = get_tables()
+
+    table=hepdata_tables[0]
+    with open(table, 'r') as f:
+        input = yaml.safe_load(f)
+
+    # Central values
+    data_values = input["dependent_variables"][4]["values"]
+    for data_value in data_values:
+        data_central.append(data_value["value"])
+
+    # Kinematic bins
+    kin_values_yll_min = input["dependent_variables"][0]["values"]
+    kin_values_yll_max = input["dependent_variables"][1]["values"]
+    kin_values_mll_min = input["dependent_variables"][2]["values"]
+    kin_values_mll_max = input["dependent_variables"][3]["values"]
+
+    for i in range(len(kin_values_yll_min)):
+        kin = {
+            'y': {'min': kin_values_yll_min[i]["value"],
+                  'mid': 0.5 * (kin_values_yll_min[i]["value"] + kin_values_yll_max[i]["value"]),
+                  'max': kin_values_yll_max[i]["value"]},
+            'mll': {'min': kin_values_mll_min[i]["value"],
+                    'mid': 0.5 * (kin_values_mll_min[i]["value"] + kin_values_mll_max[i]["value"]),
+                    'max': kin_values_mll_max[i]["value"]}}
+        kinematics.append(kin)
+
+    # Artificial uncertainties (from correlation matrix)
+    # Errors
+    for data_value in data_values:
+        errors = data_value["errors"]
+        uncertainty = {}
+        for error in errors:
+            uncertainties.append(error["symerror"])
+
+    # Correlation coefficients
+    table_corr = hepdata_tables[1]
+    with open(table_corr, 'r') as f:
+        input = yaml.safe_load(f)
+    corr_coeffs = input["dependent_variables"][0]["values"]
+    for corr_coeff in corr_coeffs:
+        correlations.append(corr_coeff["value"])
+
+    covmat = cormat_to_covmat(uncertainties,correlations)
+    art_uncertainties = covmat_to_artunc(len(uncertainties),covmat,1)
+
+    # Remap uncertainties
+    for uncertainties in art_uncertainties:
+        j = 1
+        artificial_unc={}
+        for uncertainty in uncertainties:
+            key = "artificial uncertainty " + str(j)
+            art_unc = {key: uncertainty}
+            j = j + 1
+
+            artificial_unc.update(art_unc)
+
+        art_uncert.append(artificial_unc)
+
+    return(data_central, kinematics, art_uncert)
+
+def filter_CMS_Z0_13TEV_PT():
+    """
+    Dumps data, kinematics, and uncertainties on .yaml files
+    """
+    central_values, kinematics, uncertainties = get_all()
+    # Central values
+    data_central_yaml = {"data_central": central_values}
+    # Kinematics
+    kinematics_yaml = {"bins": kinematics}
+    # Uncertainties
+    treatment = {"artificial uncertainty": "ADD"}
+    correlation = {"artificial uncertainty": "CORR"}
+    definitions = {}
+    for key,value in uncertainties[0].items():
+        definition = {key :
+                      {"description": key,
+                       "treatment": "ADD",
+                       "type": "CORR"}}
+        definitions.update(definition)
+    uncertainties_yaml = {"definitions": definitions,"bins": uncertainties}
+
+    with open("data_AFB.yaml", "w") as file:
+        yaml.dump(data_central_yaml, file, sort_keys=False)  
+    with open("kinematics_AFB.yaml", "w") as file:
+        yaml.dump(kinematics_yaml, file, sort_keys=False)
+    with open("uncertainties_AFB.yaml", "w") as file:
+        yaml.dump(uncertainties_yaml, file, sort_keys=False)
+
+if __name__ == "__main__":
+    filter_CMS_Z0_13TEV_PT()