Generate kfactors for polarized observables

data/kfactors/generate_ALL.py

@@ -0,0 +1,161 @@
+import lhapdf
+import pineappl
+import yaml
+from glob import glob
+from datetime import datetime as dt
+import numpy as np
+import os
+import argparse
+from typing import List, Tuple
+
+
+def get_file(folder: str, data: str, theory: str) -> Tuple[str, List[str]]:
+    """
+    Get a list of paths to PineAPPL grids in the specified folder.
+
+    Parameters
+    ----------
+    folder : str
+        The folder path where PineAPPL grids are located.
+    data : str
+        Name of the commondata set.
+
+    Returns
+    -------
+    pdf_name : str
+        The name of the PDF dataset.
+    gpaths : List[str]
+        List of paths to PineAPPL grid files.
+    """
+    file = glob(folder + f"/{data}_F1.pineappl.lz4")[0]
+    return file
+
+
+def get_prediction(file: str, pdf_name: str, folder: str) -> np.ndarray:
+    """
+    Get predictions by convoluting a PineAPPL grid with a LHAPDF PDF.
+
+    Parameters
+    ----------
+
+    file : str
+        Path to the PineAPPL grid file.
+    pdf_name : str
+        The name of the LHAPDF dataset.
+    folder: str
+        Path to the kinematics.yaml grid file.
+
+    Returns
+    -------
+    prediction : np.ndarray
+        Computed predictions.
+    """
+
+    # Load the PineAPPL grid
+    grid = pineappl.grid.Grid.read(file)
+
+    # Load the LHAPDF
+    pdf = lhapdf.mkPDF(pdf_name)
+
+    # Proton reference number
+    nr = 2212
+
+    # Perform the convolution
+    convolution = grid.convolute_with_one(
+        nr,  # Type of target
+        pdf.xfxQ2,  # The PDF callable pdf.xfxQ2(pid, x, Q2) -> xfx
+        pdf.alphasQ2,  # The alpha_s callable pdf.alpha_s(Q2) -> alpha_s
+    )
+
+    with open(folder + "/kinematics.yaml", "r") as file:
+        data = yaml.safe_load(file)
+
+    bins = data["bins"]
+    prediction = np.zeros(len(bins))
+    for i, bin in enumerate(bins):
+        prediction[i] = (
+            bin["y"]["mid"]
+            * (2 - bin["y"]["mid"])
+            / (bin["y"]["mid"] ** 2 + 2 * (1 - bin["y"]["mid"]))
+        )
+        prediction[i] = prediction[i] / convolution[i]
+
+    return prediction
+
+
+def save_data(
+    data: np.ndarray,
+    dataset_name: str,
+    author_name: str,
+    theory_name: str,
+    output_name: str = "results",
+):
+    """
+    Save computed data to a file with metadata.
+
+    Parameters
+    ----------
+    data : np.ndarray
+        Computed data.
+    dataset_name : str
+        Name of the dataset.
+    author_name : str
+        Name of the author.
+    theory_name : str
+        Name of the theory.
+    output_name : str, optional
+        Output folder name, default is "results".
+    """
+    strf_data = ""
+    for i in range(data.shape[0]):
+        strf_data += f"{data[i]}  0.0\n"
+
+    date = dt.now().date()
+    string = (
+        f"""********************************************************************************
+SetName: {dataset_name}
+Author: {author_name}
+Date: {date}
+CodesUsed: https://github.com/NNPDF/yadism
+TheoryInput: {theory_name}
+Warnings: D(y)/2xF1 normalization for {dataset_name}
+********************************************************************************
+"""
+        + strf_data
+    )
+
+    os.makedirs(output_name, exist_ok=True)
+    with open(output_name + f"/CF_NRM_{dataset_name}_G1.dat", "w") as file:
+        file.write(string)
+
+
+# Create an argument parser
+parser = argparse.ArgumentParser()
+parser.add_argument("pdf", help="The name of the PDF dataset of LHAPDF")
+parser.add_argument("gpath", help="The folder name of the F1 pineapple grids")
+parser.add_argument("data", help="Name of the commondata set")
+parser.add_argument("folder", help="The folder name of the commondata set")
+parser.add_argument("--author", help="The name of the author", default="A.J. Hasenack")
+parser.add_argument(
+    "--theory", help="The theory used, formatted as 'theory_'+int", default="theory_800"
+)
+parser.add_argument("--output", help="The name of the output folder", default="results")
+args = parser.parse_args()
+
+# Extract command line arguments
+pdf = args.pdf
+gpath = args.gpath
+data = args.data
+folder = args.folder
+author = args.author
+theory = args.theory
+output = args.output
+
+dataset_name = os.path.splitext(
+    os.path.splitext(os.path.basename(os.path.normpath(folder)))[0]
+)[0]
+
+# Get predictions and save data
+file = get_file(gpath, data, theory)
+data = get_prediction(file, pdf, folder)
+save_data(data, dataset_name, author, theory, output)

data/kfactors/generate_g1.py

@@ -0,0 +1,108 @@
+import lhapdf
+import pineappl
+import yaml
+from glob import glob
+from datetime import datetime as dt
+import numpy as np
+import os
+import argparse
+from typing import List, Tuple
+
+
+def get_prediction(folder: str) -> np.ndarray:
+    """
+    Get predictions by convoluting a PineAPPL grid with a LHAPDF PDF.
+
+    Parameters
+    ----------
+    folder: str
+        Path to the kinematics.yaml grid file.
+    pdf_name : str
+        The name of the LHAPDF dataset.
+
+    Returns
+    -------
+    prediction : np.ndarray
+        Computed predictions.
+    """
+
+    with open(folder + "/kinematics.yaml", "r") as file:
+        data = yaml.safe_load(file)
+
+    bins = data["bins"]
+    prediction = np.zeros(len(bins))
+    for i, bin in enumerate(bins):
+        prediction[i] = 1 / (2 * bin["x"]["mid"])
+
+    return prediction
+
+
+def save_data(
+    data: np.ndarray,
+    dataset_name: str,
+    author_name: str,
+    theory_name: str,
+    output_name: str = "results",
+):
+    """
+    Save computed data to a file with metadata.
+
+    Parameters
+    ----------
+    data : np.ndarray
+        Computed data.
+    dataset_name : str
+        Name of the dataset.
+    author_name : str
+        Name of the author.
+    theory_name : str
+        Name of the theory.
+    output_name : str, optional
+        Output folder name, default is "results".
+    """
+    strf_data = ""
+    for i in range(data.shape[0]):
+        strf_data += f"{data[i]}  0.0\n"
+
+    date = dt.now().date()
+    string = (
+        f"""********************************************************************************
+SetName: {dataset_name}
+Author: {author_name}
+Date: {date}
+CodesUsed: https://github.com/NNPDF/yadism
+TheoryInput: {theory_name}
+Warnings: 1/2x normalization for {dataset_name}
+********************************************************************************
+"""
+        + strf_data
+    )
+
+    os.makedirs(output_name, exist_ok=True)
+    with open(output_name + f"/CF_NRM_{dataset_name}_G1.dat", "w") as file:
+        file.write(string)
+
+
+# Create an argument parser
+parser = argparse.ArgumentParser()
+parser.add_argument("folder", help="The folder name of the commondata set")
+parser.add_argument("--author", help="The name of the author", default="A.J. Hasenack")
+parser.add_argument(
+    "--theory", help="The theory used, formatted as 'theory_'+int", default="theory_800"
+)
+parser.add_argument("--output", help="The name of the output folder", default="results")
+args = parser.parse_args()
+
+# Extract command line arguments
+folder_name = args.folder
+author = args.author
+theory = args.theory
+output = args.output
+
+dataset_name = os.path.splitext(
+    os.path.splitext(os.path.basename(os.path.normpath(folder_name)))[0]
+)[0]
+
+# Get predictions and save data
+data = get_prediction(folder_name)
+save_data(data, dataset_name, author, theory, output)