ATLAS_WCHARM_13TEV

nnpdf_data/nnpdf_data/commondata/ATLAS_WCHARM_13TEV/data.yaml

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+data_central:
+- 12.27
+- 11.57
+- 10.41
+- 9.09
+- 6.85
+- 11.87
+- 11.55
+- 10.09
+- 8.6
+- 6.25
+- 12.18
+- 11.77
+- 10.61
+- 8.85
+- 7.22
+- 12.52
+- 12.14
+- 10.29
+- 8.38
+- 6.55

nnpdf_data/nnpdf_data/commondata/ATLAS_WCHARM_13TEV/filter.py

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+"""
+When running `python filter.py` the relevant data yaml
+file will be created in the `nnpdf_data/commondata/ATLAS_WPWM_7TEV_46FB` directory.
+"""
+
+import yaml
+from filter_utils import get_data_values, get_kinematics
+from nnpdf_data.filter_utils.utils import prettify_float
+
+yaml.add_representer(float, prettify_float)
+
+
+def filter_ATLAS_WCHARM_13TEV_data_kinematic():
+    """
+    This function writes the systematics to yaml files.
+    """
+
+    central_values = get_data_values()
+
+    kin = get_kinematics()
+
+    data_central_yaml = {"data_central": central_values}
+
+    kinematics_yaml = {"bins": kin}
+
+    # write central values and kinematics to yaml file
+    with open("data.yaml", "w") as file:
+        yaml.dump(data_central_yaml, file, sort_keys=False)
+
+    with open("kinematics.yaml", "w") as file:
+        yaml.dump(kinematics_yaml, file, sort_keys=False)
+
+
+if __name__ == "__main__":
+    filter_ATLAS_WCHARM_13TEV_data_kinematic()

nnpdf_data/nnpdf_data/commondata/ATLAS_WCHARM_13TEV/filter_utils.py

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+"""
+This module contains helper functions that are used to extract the data values 
+from the rawdata files.
+"""
+
+import yaml
+import pandas as pd
+import numpy as np
+
+
+def get_data_values():
+    """
+    returns the central data values in the form of a list.
+    """
+
+    data_central = []
+
+    for i in range(19, 23):
+        hepdata_table = f"rawdata/HEPData-ins2628732-v1-Table_{i}.yaml"
+
+        with open(hepdata_table, 'r') as file:
+            input = yaml.safe_load(file)
+
+        values = input['dependent_variables'][0]['values']
+
+        for value in values:
+            # store data central and convert the units and apply the correction factor
+            data_central.append(value['value'])
+
+    return data_central
+
+
+def get_kinematics():
+    """
+    returns the kinematics in the form of a list of dictionaries.
+    """
+    kin = []
+
+    for i in range(19, 23):
+        hepdata_table = f"rawdata/HEPData-ins2628732-v1-Table_{i}.yaml"
+
+        with open(hepdata_table, 'r') as file:
+            input = yaml.safe_load(file)
+
+        for i, M in enumerate(input["independent_variables"][0]['values']):
+            kin_value = {
+                'abs_eta': {'min': None, 'mid': (0.5 * (M['low'] + M['high'])), 'max': None},
+                'm_W2': {'min': None, 'mid': 6.46046213e03, 'max': None},
+                'sqrts': {'min': None, 'mid': 13000.0, 'max': None},
+            }
+            kin.append(kin_value)
+
+    return kin
+
+def decompose_covmat(covmat):
+    """Given a covmat it return an array sys with shape (ndat,ndat)
+    giving ndat correlated systematics for each of the ndat point.
+    The original covmat is obtained by doing sys@sys.T"""
+
+    lamb, mat = np.linalg.eig(covmat)
+    sys = np.multiply(np.sqrt(lamb), mat)
+    return sys
+
+def get_uncertainties():
+    """
+    returns the uncertainties.
+    """
+
+    ndat = 5
+    # Produce covmat of form [[W-/W+],[0],
+    #                        [0],[W-*/W+*]]
+    covmat = np.zeros((4*ndat, 4*ndat)) # Multiply by 4 because of W+/- and */not *
+
+    def edit_covmat(filename, offset):
+        with open(filename) as f:
+            data = yaml.safe_load(f)
+        flat_values = [v["value"] for v in data["dependent_variables"][0]["values"]]
+        matrix = np.array(flat_values).reshape((2 * ndat, 2 * ndat))
+        covmat[offset:offset + 2 * ndat, offset:offset + 2 * ndat] = matrix
+
+    edit_covmat("rawdata/HEPData-ins2628732-v1-Table_16.yaml", offset=0)
+    edit_covmat("rawdata/HEPData-ins2628732-v1-Table_18.yaml", offset=2 * ndat)
+
+    sys = decompose_covmat(covmat)

nnpdf_data/nnpdf_data/commondata/ATLAS_WCHARM_13TEV/metadata.yaml

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+setname: ATLAS_WCHARM_13TEV
+
+nnpdf_metadata:
+  nnpdf31_process: DY CC
+  experiment: ATLAS
+
+arXiv:
+  url: https://arxiv.org/abs/2302.00336
+  journal: 	Phys. Rev. D 108 (2023) 032012
+iNSPIRE:
+  url: https://inspirehep.net/literature/2628732
+hepdata:
+  url: https://www.hepdata.net/record/ins2628732
+  version: 1
+
+version: 1
+version_comment: Implementation 
+
+implemented_observables:
+- observable_name: 
+  observable:
+    description: 
+    label: ATLAS $W^-+c$ 13 TeV
+    units: '[pb]'
+  process_type: 
+  tables: [19,20,21,22]  # 5/19 (W−+D+), 6/20(W++D−), 9/21(W−+D∗+), 10/22(W++D∗−) 
+  ndata: 
+  plotting:
+    dataset_label: ATLAS $W^-+c$ 13 TeV
+    y_label: 'Differential fiducial cross-section times the single-lepton-flavor W boson branching ratio' #In Latex terms?
+    x_label: $|\eta^\ell|$
+    plot_x: abs_eta
+  kinematic_coverage: 
+  kinematics:
+    variables:
+      abs_eta:
+        description:
+        label:
+        units: ''
+      m_W2:
+        description: 
+        label: 
+        units: 
+    file: 
+  data_uncertainties:
+  data_central: 
+  variants:
+    legacy:
+      data_uncertainties: