src/utils/inference/pandas_helpers.py

import gzip
import pickle
import mplhep as hep

hep.style.use("CMS")
import matplotlib

matplotlib.rc("font", size=25)
import numpy as np
import pandas as pd


def open_hgcal(path_hgcal, neutrals_only):
    with gzip.open(
        path_hgcal,
        "rb",
    ) as f:
        data = pickle.load(f)
    sd = data["showers_dataframe"]
    if neutrals_only:
        sd = pd.concat(
            [
                data[data["pid"] == 130],
                data[data["pid"] == 2112],
                data[data["pid"] == 22],
            ]
        )
    else:
        sd = data
    matched = sd.dropna()
    ms = data["matched_showers"]

    return sd, ms


def open_mlpf_dataframe(path_mlpf, neutrals_only=False):
    data = pd.read_pickle(path_mlpf)
    if neutrals_only:
        sd = pd.concat(
            [
                data[data["pid"] == 130],
                data[data["pid"] == 2112],
                data[data["pid"] == 211],
            ]
        )
    else:
        sd = data
    pid_conversion_dict = {11: 0, -11: 0, 211: 1, -211: 1, 130: 2, -130: 2, 2112: 2, -2112: 2, 22: 3}
    mask = (~np.isnan(sd["pred_showers_E"])) * (~np.isnan(sd["reco_showers_E"]))
    sd["pid_4_class_true"] = sd["pid"].map(pid_conversion_dict)
    if "pred_pid_matched" in sd.columns:
        sd.loc[sd["pred_pid_matched"] < -1, "pred_pid_matched"] = np.nan
    matched = sd[mask]
    return sd, matched