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97 lines
4.2 KiB
97 lines
4.2 KiB
import os
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import dotenv
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import sys
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import argparse
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import pandas as pd
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import mplhep
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import zfit
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from prettytable import PrettyTable
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dotenv.load_dotenv('../properties.env')
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sys.path.insert(0, os.getenv('SYS_PATH'))
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from b2kstll.models.angular import B2Kstll
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from b2kstll.plot import plot_distributions
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from hep_analytics.processing.extract import FileManager
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from hep_analytics.processing.transform import select_feature, reweight_feature
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from hep_analytics.processing.visualisation import reweight_comparing_plot
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FILE_MC = os.getenv('MC_FILE')
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FILE_GEN = os.getenv('GEN_FILE')
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--q2bin', dest ='q2bin', default = 0)
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args = parser.parse_args()
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Q2BIN = int(args.q2bin)
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mplhep.style.use("LHCb2")
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bin_ranges = [(0.25, 4.00), (4.00, 8.00), (11.00, 12.50), (15.00, 18.00), (1.10, 6.00), (1.1, 2.5), (2.5, 4.0), (4.0, 6.0), (6.0, 8.0)]
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print(f"Selected Q2 Bin Range is {bin_ranges[Q2BIN]}")
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bin_labels = [r"$0.25 < q^2 < 4.00$", r"$4.00 < q^2 < 8.00$", r"$11.00 < q^2 < 12.50$",
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r"$15.00 < q^2 < 18.00$", r"$1.10 < q^2 < 6.00$",
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r"$1.1 < q^2 < 2.5$", r"$2.5 < q^2 < 4.0$", r"$4.0 < q^2 < 6.0$", r"$6.0 < q^2 < 8.0$"]
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filemanager = FileManager(file = FILE_MC, tree = "Events", branches = ["q2", "costhetak", "costhetal", "phi"])
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mc_data = filemanager.extract_data()
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q2_mc, theta_k_mc, theta_l_mc, phi_mc = mc_data[0], mc_data[1], mc_data[2], mc_data[3]
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q2_mc, indices = select_feature(feature = q2_mc, limits = bin_ranges[Q2BIN])
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phi_mc = phi_mc[indices]
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theta_l_mc = theta_l_mc[indices]
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theta_k_mc = theta_k_mc[indices]
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lower_costhetak_cut = float(os.getenv('LOWER_COSTHETAK_CUT'))
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upper_costhetak_cut = float(os.getenv('UPPER_COSTHETAK_CUT'))
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theta_k_mc, indices = select_feature(feature = theta_k_mc, limits = (lower_costhetak_cut, upper_costhetak_cut))
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q2_mc = q2_mc[indices]
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phi_mc = phi_mc[indices]
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theta_l_mc = theta_l_mc[indices]
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filemanager = FileManager(file = FILE_GEN, tree = "Events", branches = ["q2", "costhetak", "costhetal", "phi"])
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gen_data = filemanager.extract_data()
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q2_gen, theta_k_gen, theta_l_gen, phi_gen = gen_data[0], gen_data[1], gen_data[2], gen_data[3]
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q2_gen, indices = select_feature(feature = q2_gen, limits = bin_ranges[Q2BIN])
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phi_gen = phi_gen[indices]
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theta_l_gen = theta_l_gen[indices]
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theta_k_gen = theta_k_gen[indices]
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theta_k_gen, indices = select_feature(feature = theta_k_gen, limits = (lower_costhetak_cut, upper_costhetak_cut))
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q2_gen = q2_gen[indices]
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phi_gen = phi_gen[indices]
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theta_l_gen = theta_l_gen[indices]
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angular_data = pd.DataFrame({'ctl': theta_l_mc, 'ctk': theta_k_mc, 'phi': phi_mc})
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angular_data.to_csv(f"ang_fit_mc_q2_bins_{Q2BIN}_bin.csv", index = False)
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x = B2Kstll('ctl','ctk','phi')
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x.set_acc(f"./acc_3d_JpsiKstMC_reweighted_4_bin.yaml")
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obs, pdf, _ = x.get_pdf('PWave')
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q2_mc_weights = reweight_feature(original_feature = q2_mc, target_feature = q2_gen, n_bins = 25)
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reweight_comparing_plot(original_feature = q2_mc, target_feature = q2_gen, weights = q2_mc_weights,
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n_bins = 25, suptitle = f"{bin_labels[Q2BIN]}", titles = ["Q2 MC", "Q2 MC (reweighted)", "Q2 Gen"], save = "reweighted_q2_gen_mc.png")
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datazfit = zfit.Data.from_pandas(df = angular_data, obs = obs, weights = q2_mc_weights)
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nll = zfit.loss.UnbinnedNLL(model = pdf, data = datazfit)
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minimizer = zfit.minimize.Minuit()
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result = minimizer.minimize(nll)
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param_errors, _ = result.errors()
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print(param_errors)
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info_table = PrettyTable(["Variable", "Value", "Lower Error", "Upper Error"])
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fit_labels = ["AFB", "FL", "S3", "S4", "S5", "S7", "S8", "S9"]
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for name in fit_labels:
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value = result.params[name]["value"]
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lower = result.params[name]["minuit_minos"]["lower"]
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upper = result.params[name]["minuit_minos"]["upper"]
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info_table.add_row([name, value, lower, upper])
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print(info_table)
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plot_distributions(result, suffix = f"accPwavePDF_{Q2BIN}_bin_reweighted_mc")
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if __name__ == "__main__":
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main()
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