74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
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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 mplhep
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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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 analysis.efficiency import get_efficiency_model_class
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from hep_analytics.processing.extract import FileManager
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from hep_analytics.processing.transform import select_feature
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FILE_MC_PHSP = os.getenv('MC_PHSP_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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filemanager = FileManager(file = FILE_MC_PHSP, tree = "Events", branches = ["q2", "costhetak", "costhetal", "phi"])
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mc_phsp_data = filemanager.extract_data()
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q2_mc_phsp, theta_k_mc_phsp, theta_l_mc_phsp, phi_mc_phsp = mc_phsp_data[0], mc_phsp_data[1], mc_phsp_data[2], mc_phsp_data[3]
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q2_mc_phsp, indices = select_feature(feature = q2_mc_phsp, limits = bin_ranges[Q2BIN])
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phi_mc_phsp = phi_mc_phsp[indices]
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theta_l_mc_phsp = theta_l_mc_phsp[indices]
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theta_k_mc_phsp = theta_k_mc_phsp[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_phsp, indices = select_feature(feature = theta_k_mc_phsp, limits = (lower_costhetak_cut, upper_costhetak_cut))
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q2_mc_phsp = q2_mc_phsp[indices]
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phi_mc_phsp = phi_mc_phsp[indices]
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theta_l_mc_phsp = theta_l_mc_phsp[indices]
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data_mc_phsp = [phi_mc_phsp, theta_l_mc_phsp, theta_k_mc_phsp, q2_mc_phsp]
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df = pd.DataFrame({'ctl': data_mc_phsp[1], 'ctk': data_mc_phsp[2], 'phi': data_mc_phsp[0], 'q2': q2_mc_phsp})
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orders = {"ctl": 4, "ctk": 6, "phi": 2}
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ranges = {"ctl": [-1.0, 1.0], "ctk": [lower_costhetak_cut, upper_costhetak_cut], "phi": [-np.pi, np.pi]}
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EffClass = get_efficiency_model_class('legendre')
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eff = EffClass.fit(df, ['ctl', 'ctk', 'phi'], ranges = ranges,
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legendre_orders = orders, calculate_cov = False, chunk_size = 2000)
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out_file = eff.write_to_disk(f'acc_3d_JpsiKstMC_{Q2BIN}_bin.yaml')
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print(out_file)
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labels = {'ctl': r'$\cos \theta_L$', 'ctk': r'$\cos \theta_K$', 'phi': '$\phi$', 'q2': '$q^2$ [GeV$^2$]'}
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for v in ['ctl', 'ctk', 'phi']:
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plt.subplots(figsize = (15, 10))
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plt.xlim(*ranges[v])
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x, y = eff.project_efficiency(v, n_points = 1000)
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plt.plot(x, y, 'b-')
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plt.hist(df[v], density = True, bins = 50, color = 'grey', alpha = 0.5)
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plt.ylabel("a.u.", horizontalalignment = 'right', y = 1.0)
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plt.xlabel(labels[v], horizontalalignment = 'right', x = 1.0)
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plt.savefig(f'acc_3d_JpsiKstMC_phsp_mc_{v}_{Q2BIN}_bin.pdf')
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plt.close()
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if __name__ == "__main__":
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main()
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