HERMES SIDIS per scattering

nnpdf_data/nnpdf_data/commondata/HERMES_SIDIS_10P36GEV_D-POSITRON-KPLUS/data.yaml

@@ -0,0 +1,37 @@
+data_central:
+- 0.3039
+- 0.2876
+- 0.2773
+- 0.2337
+- 0.2437
+- 0.235
+- 0.2587
+- 0.2673
+- 0.3101
+- 0.2095
+- 0.2068
+- 0.1914
+- 0.1718
+- 0.1658
+- 0.1733
+- 0.1745
+- 0.1936
+- 0.1622
+- 0.1147
+- 0.1111
+- 0.1107
+- 0.1128
+- 0.1052
+- 0.1079
+- 0.1069
+- 0.1203
+- 0.1007
+- 0.0495
+- 0.0469
+- 0.0503
+- 0.0521
+- 0.053
+- 0.0473
+- 0.0487
+- 0.0494
+- 0.0372

nnpdf_data/nnpdf_data/commondata/HERMES_SIDIS_10P36GEV_D-POSITRON-KPLUS/filter.py

@@ -0,0 +1,136 @@
+import yaml
+import os
+
+ECM = 10.3  # sqrt(s)=sqrt(2p_lep*m_hadron+m_hadron^2)=sqrt(2*27.6*1.876+1.876^2)= 10.3 in GeV
+
+
+def read_data(folder_path: str, tables: list, htype: int):
+    """
+    htype: 0 for proton, 1 for deutron
+    """
+    collected_data = dict()
+    collected_data["values"] = list()
+    collected_data["errors_stat"] = list()
+    collected_data["errors_sys"] = list()
+    collected_data["kinematics_x"] = list()
+    collected_data["kinematics_z"] = list()
+
+    metadata_dict = {"htype": htype, "tables": tables, "ndata_points": list()}
+
+    for table in tables:
+        with open(folder_path + f"Table{table}.yaml", "r", encoding="utf-8") as file:
+            file_dict = yaml.safe_load(file)
+        z_str = file_dict["dependent_variables"][htype]["qualifiers"][2]["value"]
+        z_min, z_max = map(float, z_str.split("-"))
+        values = file_dict["dependent_variables"][htype]["values"]
+        n_values = len(values)
+        metadata_dict["ndata_points"].append(n_values)
+        for i in range(n_values):
+            collected_data["values"] = collected_data["values"] + [values[i]["value"]]
+            collected_data["errors_stat"] = collected_data["errors_stat"] + [
+                values[i]["errors"][0]["symerror"]
+            ]
+            collected_data["errors_sys"] = collected_data["errors_sys"] + [
+                values[i]["errors"][1]["symerror"]
+            ]
+            collected_data["kinematics_x"] = collected_data["kinematics_x"] + [
+                file_dict["independent_variables"][htype]["values"][i]["value"]
+            ]
+            collected_data["kinematics_z"] = collected_data["kinematics_z"] + [
+                [
+                    z_min,
+                    z_max,
+                ]
+            ]
+    return collected_data, metadata_dict
+
+
+def write_data(collected_data: dict, folder_path: str):
+    data_central_yaml = {"data_central": collected_data["values"]}
+    with open(folder_path + f"data.yaml", "w", encoding="utf-8") as file:
+        yaml.dump(data_central_yaml, file, sort_keys=False)
+
+    n_items = len(collected_data["values"])
+
+    # Write kin file
+    kin = []
+    for i in range(n_items):
+        kin_value = {
+            "z": {
+                "min": collected_data["kinematics_z"][i][0],
+                "mid": None,
+                "max": collected_data["kinematics_z"][i][1],
+            },
+            "x": {"min": None, "mid": collected_data["kinematics_x"][i], "max": None},
+            "sqrts": {"min": None, "mid": ECM, "max": None},
+        }
+        kin.append(kin_value)
+    kinematics_yaml = {"bins": kin}
+
+    with open(folder_path + f"kinematics.yaml", "w", encoding="utf-8") as file:
+        yaml.dump(kinematics_yaml, file, sort_keys=False)
+
+    # Write unc file
+    error = []
+    for i in range(n_items):
+        # here uncertainties are symmetric
+        e = {
+            "stat": collected_data["errors_stat"][i],
+            "sys": collected_data["errors_sys"][i],
+        }
+        error.append(e)
+
+    error_definition = {
+        "stat": {
+            "description": "statistical uncertainty",
+            "treatment": "ADD",
+            "type": "UNCORR",
+        },
+        "sys": {
+            "description": "systematic uncertainty",
+            "treatment": "ADD",
+            "type": "UNCORR",
+        },
+    }
+
+    uncertainties_yaml = {"definitions": error_definition, "bins": error}
+
+    with open(folder_path + f"uncertainties.yaml", "w", encoding="utf-8") as file:
+        yaml.dump(uncertainties_yaml, file, sort_keys=False)
+
+
+if __name__ == "__main__":
+
+    # Get the path of the current file
+    folder_path = os.path.dirname(os.path.abspath(__file__)) + "/"
+
+    # TODO Create a dict for easy running
+    naming_dict = {
+        # "PiPProton-MLTP": [33, 34, 35, 36],
+        # "PiPDeutron-MLTP": [33, 34, 35, 36],
+        # "PiMProton-MLTP": [37, 38, 39, 40],
+        # "PiMDeutron-MLTP": [37, 38, 39, 40],
+        # "KaMProton-MLTP": [45, 46, 47, 48],
+        # "KaMDeutron-MLTP": [45, 46, 47, 48],
+        # "KaPProton-MLTP": [41, 42, 43, 44],
+        "KaPDeutron-MLTP": [41, 42, 43, 44],
+    }
+
+    # Wp
+    for name, tables in naming_dict.items():
+        if "Proton" in name:
+            htype = 0
+        else:
+            htype = 1
+
+        if name.upper() in folder_path:
+            a = 1
+
+        collected_data, metadata_dict = read_data(
+            folder_path + "rawdata/", tables, htype
+        )
+        print(name.split("-")[0].lower(), metadata_dict)
+        write_data(
+            collected_data,
+            folder_path=folder_path,
+        )