From 0bc6296fb7fbdf3092cd3b35417707a35b158af1 Mon Sep 17 00:00:00 2001 From: rskuza Date: Wed, 24 Aug 2022 11:23:16 +0200 Subject: [PATCH] Python Scripts used for Angular Analysis for B+toK*+Pi0mumu --- .../Generator Level Fit/angular_fit.py | 79 + .../MC Fit/acc_3d_JpsiKstMC_4_bin.yaml | 2374 +++++++++++++++++ .../MC Fit/acc_3d_JpsiKstMC_5_bin.yaml | 2374 +++++++++++++++++ .../MC Fit/acc_3d_JpsiKstMC_6_bin.yaml | 2374 +++++++++++++++++ .../MC Fit/acc_3d_JpsiKstMC_7_bin.yaml | 2374 +++++++++++++++++ .../acc_3d_JpsiKstMC_reweighted_4_bin.yaml | 2374 +++++++++++++++++ .../MC Fit/angular_acceptance_modeling.py | 73 + .../Python Scripts/MC Fit/angular_fit.py | 77 + .../MC Fit/diff_pair_tables_average_mc.py | 83 + .../diff_pair_tables_average_mc_reweighted.py | 81 + .../MC Fit/diff_pair_tables_mc_gen.py | 81 + .../reweighted_angular_acceptance_modeling.py | 94 + .../MC Fit/reweighted_angular_fit.py | 97 + .../MC Fit/table_average_to_compare.py | 97 + Code/Scripts/Python Scripts/properties.env | 7 + Code/Scripts/Python Scripts/requirements.txt | 208 ++ .../Python Scripts/root_file_inspect.py | 29 + Code/Scripts/Python Scripts/rootio.py | 37 + Code/Scripts/hep_analytics/__init__.py | 0 .../Scripts/hep_analytics/pathing/__init__.py | 0 .../hep_analytics/pathing/aggregate.py | 13 + .../hep_analytics/processing/__init__.py | 0 .../hep_analytics/processing/extract.py | 20 + .../hep_analytics/processing/transform.py | 20 + .../hep_analytics/processing/visualisation.py | 22 + 25 files changed, 12988 insertions(+) create mode 100644 Code/Scripts/Python Scripts/Generator Level Fit/angular_fit.py create mode 100644 Code/Scripts/Python Scripts/MC Fit/acc_3d_JpsiKstMC_4_bin.yaml create mode 100644 Code/Scripts/Python Scripts/MC Fit/acc_3d_JpsiKstMC_5_bin.yaml create mode 100644 Code/Scripts/Python Scripts/MC Fit/acc_3d_JpsiKstMC_6_bin.yaml create mode 100644 Code/Scripts/Python Scripts/MC Fit/acc_3d_JpsiKstMC_7_bin.yaml create mode 100644 Code/Scripts/Python Scripts/MC Fit/acc_3d_JpsiKstMC_reweighted_4_bin.yaml create mode 100644 Code/Scripts/Python Scripts/MC Fit/angular_acceptance_modeling.py create mode 100644 Code/Scripts/Python Scripts/MC Fit/angular_fit.py create mode 100644 Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_average_mc.py create mode 100644 Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_average_mc_reweighted.py create mode 100644 Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_mc_gen.py create mode 100644 Code/Scripts/Python Scripts/MC Fit/reweighted_angular_acceptance_modeling.py create mode 100644 Code/Scripts/Python Scripts/MC Fit/reweighted_angular_fit.py create mode 100644 Code/Scripts/Python Scripts/MC Fit/table_average_to_compare.py create mode 100644 Code/Scripts/Python Scripts/properties.env create mode 100644 Code/Scripts/Python Scripts/requirements.txt create mode 100644 Code/Scripts/Python Scripts/root_file_inspect.py create mode 100755 Code/Scripts/Python Scripts/rootio.py create mode 100644 Code/Scripts/hep_analytics/__init__.py create mode 100644 Code/Scripts/hep_analytics/pathing/__init__.py create mode 100644 Code/Scripts/hep_analytics/pathing/aggregate.py create mode 100644 Code/Scripts/hep_analytics/processing/__init__.py create mode 100644 Code/Scripts/hep_analytics/processing/extract.py create mode 100644 Code/Scripts/hep_analytics/processing/transform.py create mode 100644 Code/Scripts/hep_analytics/processing/visualisation.py diff --git a/Code/Scripts/Python Scripts/Generator Level Fit/angular_fit.py b/Code/Scripts/Python Scripts/Generator Level Fit/angular_fit.py new file mode 100644 index 0000000..2915465 --- /dev/null +++ b/Code/Scripts/Python Scripts/Generator Level Fit/angular_fit.py @@ -0,0 +1,79 @@ +import os +import dotenv +import argparse +import sys +import pandas as pd +import zfit + +from prettytable import PrettyTable + +dotenv.load_dotenv('../properties.env') + +sys.path.insert(0, os.getenv('SYS_PATH')) + +from b2kstll.models.angular import B2Kstll +from b2kstll.plot import plot_distributions + +from hep_analytics.processing.extract import FileManager +from hep_analytics.processing.transform import select_feature + + +FILE = os.getenv('FILE_GEN') + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--q2bin', dest ='q2bin', default = 0) + args = parser.parse_args() + Q2BIN = int(args.q2bin) + + 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)] + print(f"Selected Q2 Bin Range is {bin_ranges[Q2BIN]}") + + file_manager = FileManager(file = FILE, tree = "Events", + branches = ["q2", "costhetak", "costhetal", "phi"]) + data = file_manager.extract_data() + q2, costhetak, costhetal, phi = data[0], data[1], data[2], data[3] + + q2, indices = select_feature(feature = q2, limits = bin_ranges[Q2BIN]) + costhetak = costhetak[indices] + costhetal = costhetal[indices] + phi = phi[indices] + + lower_costhetak_cut = float(os.getenv('LOWER_COSTHETAK_CUT')) + upper_costhetak_cut = float(os.getenv('UPPER_COSTHETAK_CUT')) + costhetak, indices = select_feature(feature = costhetak, limits = (lower_costhetak_cut, upper_costhetak_cut)) + q2 = q2[indices] + costhetal = costhetal[indices] + phi = phi[indices] + + angular_data = pd.DataFrame({'ctl': costhetal, 'ctk': costhetak, 'phi': phi}) + angular_data.to_csv(f"angular_fit_generator_bin_{Q2BIN}.csv", index = False) + x = B2Kstll('ctl','ctk','phi') + obs, pdf, _ = x.get_pdf('PWave') + datazfit = zfit.Data.from_pandas(df = angular_data, obs = obs) + nll = zfit.loss.UnbinnedNLL(model = pdf, data = datazfit) + minimizer = zfit.minimize.Minuit() + + result = minimizer.minimize(nll) + print(result) + print(result.params, pdf) + + param_errors, _ = result.errors() + print(param_errors) + + info_table = PrettyTable(["Variable", "Value", "Lower Error", "Upper Error"]) + fit_labels = ["AFB", "FL", "S3", "S4", "S5", "S7", "S8", "S9"] + for name in fit_labels: + value = result.params[name]["value"] + lower = result.params[name]["minuit_minos"]["lower"] + upper = result.params[name]["minuit_minos"]["upper"] + info_table.add_row([name, value, lower, upper]) + + print(info_table) + + plot_distributions(result, suffix = f"accPwavePDF_generator_level_bin_{Q2BIN}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Code/Scripts/Python Scripts/MC Fit/acc_3d_JpsiKstMC_4_bin.yaml b/Code/Scripts/Python Scripts/MC Fit/acc_3d_JpsiKstMC_4_bin.yaml new file mode 100644 index 0000000..0557a51 --- /dev/null +++ b/Code/Scripts/Python Scripts/MC Fit/acc_3d_JpsiKstMC_4_bin.yaml @@ -0,0 +1,2374 @@ +model: legendre +parameters: + coefficients: + - 0.125 + - -4.7239800356654364e-05 + - -0.06355261988563715 + - -0.0016556488199799336 + - -0.05050092730342982 + - -0.0019672336528664845 + - -0.003939047086179465 + - 0.0032132767684415306 + - -0.00019194285676483948 + - -0.003107664344968938 + - 0.0030233300049737786 + - 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0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + pol-orders: + ctk: 6 + ctl: 4 + phi: 2 + ranges: + ctk: + - -1.0 + - 0.851 + ctl: + - -1.0 + - 1.0 + phi: + - -3.141592653589793 + - 3.141592653589793 +variables: +- ctl +- ctk +- phi diff --git a/Code/Scripts/Python Scripts/MC Fit/angular_acceptance_modeling.py b/Code/Scripts/Python Scripts/MC Fit/angular_acceptance_modeling.py new file mode 100644 index 0000000..9be83ef --- /dev/null +++ b/Code/Scripts/Python Scripts/MC Fit/angular_acceptance_modeling.py @@ -0,0 +1,73 @@ +import os +import dotenv +import sys +import argparse +import mplhep +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +dotenv.load_dotenv('../properties.env') + +sys.path.insert(0, os.getenv('SYS_PATH')) + +from analysis.efficiency import get_efficiency_model_class + +from hep_analytics.processing.extract import FileManager +from hep_analytics.processing.transform import select_feature + +FILE_MC_PHSP = os.getenv('MC_PHSP_FILE') + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--q2bin', dest ='q2bin', default = 0) + args = parser.parse_args() + Q2BIN = int(args.q2bin) + + mplhep.style.use("LHCb2") + + 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)] + print(f"Selected Q2 Bin Range is {bin_ranges[Q2BIN]}") + + filemanager = FileManager(file = FILE_MC_PHSP, tree = "Events", branches = ["q2", "costhetak", "costhetal", "phi"]) + mc_phsp_data = filemanager.extract_data() + 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] + q2_mc_phsp, indices = select_feature(feature = q2_mc_phsp, limits = bin_ranges[Q2BIN]) + phi_mc_phsp = phi_mc_phsp[indices] + theta_l_mc_phsp = theta_l_mc_phsp[indices] + theta_k_mc_phsp = theta_k_mc_phsp[indices] + lower_costhetak_cut = float(os.getenv('LOWER_COSTHETAK_CUT')) + upper_costhetak_cut = float(os.getenv('UPPER_COSTHETAK_CUT')) + theta_k_mc_phsp, indices = select_feature(feature = theta_k_mc_phsp, limits = (lower_costhetak_cut, upper_costhetak_cut)) + q2_mc_phsp = q2_mc_phsp[indices] + phi_mc_phsp = phi_mc_phsp[indices] + theta_l_mc_phsp = theta_l_mc_phsp[indices] + data_mc_phsp = [phi_mc_phsp, theta_l_mc_phsp, theta_k_mc_phsp, q2_mc_phsp] + + df = pd.DataFrame({'ctl': data_mc_phsp[1], 'ctk': data_mc_phsp[2], 'phi': data_mc_phsp[0], 'q2': q2_mc_phsp}) + orders = {"ctl": 4, "ctk": 6, "phi": 2} + ranges = {"ctl": [-1.0, 1.0], "ctk": [lower_costhetak_cut, upper_costhetak_cut], "phi": [-np.pi, np.pi]} + + EffClass = get_efficiency_model_class('legendre') + eff = EffClass.fit(df, ['ctl', 'ctk', 'phi'], ranges = ranges, + legendre_orders = orders, calculate_cov = False, chunk_size = 2000) + + out_file = eff.write_to_disk(f'acc_3d_JpsiKstMC_{Q2BIN}_bin.yaml') + print(out_file) + + labels = {'ctl': r'$\cos \theta_L$', 'ctk': r'$\cos \theta_K$', 'phi': '$\phi$', 'q2': '$q^2$ [GeV$^2$]'} + for v in ['ctl', 'ctk', 'phi']: + plt.subplots(figsize = (15, 10)) + plt.xlim(*ranges[v]) + x, y = eff.project_efficiency(v, n_points = 1000) + plt.plot(x, y, 'b-') + plt.hist(df[v], density = True, bins = 50, color = 'grey', alpha = 0.5) + plt.ylabel("a.u.", horizontalalignment = 'right', y = 1.0) + plt.xlabel(labels[v], horizontalalignment = 'right', x = 1.0) + plt.savefig(f'acc_3d_JpsiKstMC_phsp_mc_{v}_{Q2BIN}_bin.pdf') + plt.close() + + +if __name__ == "__main__": + main() diff --git a/Code/Scripts/Python Scripts/MC Fit/angular_fit.py b/Code/Scripts/Python Scripts/MC Fit/angular_fit.py new file mode 100644 index 0000000..450d6e9 --- /dev/null +++ b/Code/Scripts/Python Scripts/MC Fit/angular_fit.py @@ -0,0 +1,77 @@ +import os +import dotenv +import sys +import argparse +import pandas as pd +import mplhep +import zfit + +from prettytable import PrettyTable + +dotenv.load_dotenv('../properties.env') + +sys.path.insert(0, os.getenv('SYS_PATH')) + +from b2kstll.models.angular import B2Kstll +from b2kstll.plot import plot_distributions + +from hep_analytics.processing.extract import FileManager +from hep_analytics.processing.transform import select_feature + +FILE_MC = os.getenv('MC_FILE') + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--q2bin', dest ='q2bin', default = 0) + args = parser.parse_args() + Q2BIN = int(args.q2bin) + + mplhep.style.use("LHCb2") + + 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)] + print(f"Selected Q2 Bin Range is {bin_ranges[Q2BIN]}") + + filemanager = FileManager(file = FILE_MC, tree = "Events", branches = ["q2", "costhetak", "costhetal", "phi"]) + mc_data = filemanager.extract_data() + q2_mc, theta_k_mc, theta_l_mc, phi_mc = mc_data[0], mc_data[1], mc_data[2], mc_data[3] + q2_mc, indices = select_feature(feature = q2_mc, limits = bin_ranges[Q2BIN]) + phi_mc = phi_mc[indices] + theta_l_mc = theta_l_mc[indices] + theta_k_mc = theta_k_mc[indices] + lower_costhetak_cut = float(os.getenv('LOWER_COSTHETAK_CUT')) + upper_costhetak_cut = float(os.getenv('UPPER_COSTHETAK_CUT')) + theta_k_mc, indices = select_feature(feature = theta_k_mc, limits = (lower_costhetak_cut, upper_costhetak_cut)) + q2_mc = q2_mc[indices] + phi_mc = phi_mc[indices] + theta_l_mc = theta_l_mc[indices] + + angular_data = pd.DataFrame({'ctl': theta_l_mc, 'ctk': theta_k_mc, 'phi': phi_mc}) + angular_data.to_csv(f"ang_fit_mc_{Q2BIN}_bin.csv", index = False) + x = B2Kstll('ctl','ctk','phi') + x.set_acc(f"./acc_3d_JpsiKstMC_{Q2BIN}_bin.yaml") + obs, pdf, params = x.get_pdf('PWave') + + datazfit = zfit.Data.from_pandas(df = angular_data, obs = obs) + nll = zfit.loss.UnbinnedNLL(model = pdf, data = datazfit) + minimizer = zfit.minimize.Minuit() + result = minimizer.minimize(nll) + + param_errors, _ = result.errors() + print(param_errors) + + info_table = PrettyTable(["Variable", "Value", "Lower Error", "Upper Error"]) + fit_labels = ["AFB", "FL", "S3", "S4", "S5", "S7", "S8", "S9"] + for name in fit_labels: + value = result.params[name]["value"] + lower = result.params[name]["minuit_minos"]["lower"] + upper = result.params[name]["minuit_minos"]["upper"] + info_table.add_row([name, value, lower, upper]) + + print(info_table) + + plot_distributions(result, suffix = f"accPwavePDF_mc_ang_fit_{Q2BIN}_bin") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_average_mc.py b/Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_average_mc.py new file mode 100644 index 0000000..100a520 --- /dev/null +++ b/Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_average_mc.py @@ -0,0 +1,83 @@ +import re +import numpy as np + +from prettytable import PrettyTable + + +def main(): + bin_labels = r"$1.10 < q^2 < 6.00$" + + sample_1 = """+----------+------------------------+------------------------+-----------------------+\n + | Variable | Value | Lower Error | Upper Error |\n + +----------+------------------------+------------------------+-----------------------+\n + | AFB | -0.030693939006905927 | -0.0027448398251912667 | 0.0026690127045430573 |\n + | FL | 0.7641860897390124 | -0.0031519177028962353 | 0.00318403722094425 |\n + | S3 | -0.009793642767841643 | -0.0039500538800350165 | 0.003962284039325228 |\n + | S4 | -0.11948833798312776 | -0.005018301784506872 | 0.005112053156795224 |\n + | S5 | -0.14672854148123884 | -0.005077800231656763 | 0.0051383664423323315 |\n + | S7 | -0.0009713979621990259 | -0.005287904531482017 | 0.005275166744474821 |\n + | S8 | 0.003178551357900045 | -0.005257961171467131 | 0.005258879438751017 |\n + | S9 | 0.005757808359963191 | -0.004016272402049568 | 0.004019873761938199 |\n + +----------+------------------------+------------------------+-----------------------+""" # GENERATOR LEVEL + + sample_2 = """+---------------------------------------------------------------------------------+\n + | q2_split_$1.10 < q^2 < 6.00$_average |\n + +----------+-----------------------+-----------------------+----------------------+\n + | Variable | Value | Lower Error | Upper Error |\n + +----------+-----------------------+-----------------------+----------------------+\n + | AFB | -0.03611162259371692 | -0.008731742137981694 | 0.008731742137981694 |\n + | FL | 0.7816031469957773 | -0.010041813091402643 | 0.010041813091402643 |\n + | S3 | -0.004913319742985065 | -0.010997797016445892 | 0.010997797016445892 |\n + | S4 | -0.11446696701061239 | -0.0177373887414066 | 0.0177373887414066 |\n + | S5 | -0.1162615465324004 | -0.016777187198089353 | 0.016777187198089353 |\n + | S7 | 0.013450446832457594 | -0.017351060768029697 | 0.017351060768029697 |\n + | S8 | 0.012943806403734165 | -0.018166933762978635 | 0.018166933762978635 |\n + | S9 | -0.015991133298640343 | -0.011753160273233804 | 0.011753160273233804 |\n + +----------+-----------------------+-----------------------+----------------------+""" # MC LEVEL + + variables = ["AFB", "FL", "S3", "S4", "S5", "S7", "S8", "S9"] + select_regex = [".*AFB.*\d+", ".*FL.*\d+", ".*S3.*\d+", ".*S4.*\d+", ".*S5.*\d+", ".*S7.*\d+", ".*S8.*\d+", ".*S9.*\d+"] + value_regex = "[- ]\d+.\d+" + sample_table_1 = {'1': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None}} + sample_table_2 = {'1': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None}} + samples = [sample_1, sample_2] + + for variable, regex in zip(variables, select_regex): + sample_1_splitted = samples[0].splitlines() + for line in sample_1_splitted: + subresult = re.match(regex, line) + if subresult != None: + substring = subresult.group(0) + result = re.findall(value_regex, substring) + value = np.float64(result[0]) + lower_err = np.float64(result[1]) + upper_err = np.float64(result[2]) + + sample_table_1['1'][variable] = (value, lower_err, upper_err) + + sample_2_splitted = samples[1].splitlines() + for line in sample_2_splitted: + subresult = re.match(regex, line) + if subresult != None: + substring = subresult.group(0) + result = re.findall(value_regex, substring) + value = np.float64(result[0]) + lower_err = np.float64(result[1]) + upper_err = np.float64(result[2]) + + sample_table_2['1'][variable] = (value, lower_err, upper_err) + + info_table = PrettyTable(["Variable", r"|MC Average - Gen|", "Lower Error", "Upper Error", "Stat. Significance"]) + for name in variables: + index = '1' + delta_value = np.abs(sample_table_1[index][name][0] - sample_table_2[index][name][0]) + quadratic_lower = -np.sqrt(sample_table_1[index][name][1]**2 + sample_table_2[index][name][1]**2) + quadratic_upper = np.sqrt(sample_table_1[index][name][2]**2 + sample_table_2[index][name][2]**2) + stat_significance = np.abs(delta_value / quadratic_lower) + info_table.add_row([name, delta_value, quadratic_lower, quadratic_upper, stat_significance]) + info_table.title = f"{bin_labels} Average over 3 Bins in MC" + print(info_table) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_average_mc_reweighted.py b/Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_average_mc_reweighted.py new file mode 100644 index 0000000..0f68760 --- /dev/null +++ b/Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_average_mc_reweighted.py @@ -0,0 +1,81 @@ +import re +import numpy as np + +from prettytable import PrettyTable + + +def main(): + bin_labels = r"$1.10 < q^2 < 6.00$" + + sample_1 = """+----------+------------------------+------------------------+-----------------------+\n + | Variable | Value | Lower Error | Upper Error |\n + +----------+------------------------+------------------------+-----------------------+\n + | AFB | -0.030693939006905927 | -0.0027448398251912667 | 0.0026690127045430573 |\n + | FL | 0.7641860897390124 | -0.0031519177028962353 | 0.00318403722094425 |\n + | S3 | -0.009793642767841643 | -0.0039500538800350165 | 0.003962284039325228 |\n + | S4 | -0.11948833798312776 | -0.005018301784506872 | 0.005112053156795224 |\n + | S5 | -0.14672854148123884 | -0.005077800231656763 | 0.0051383664423323315 |\n + | S7 | -0.0009713979621990259 | -0.005287904531482017 | 0.005275166744474821 |\n + | S8 | 0.003178551357900045 | -0.005257961171467131 | 0.005258879438751017 |\n + | S9 | 0.005757808359963191 | -0.004016272402049568 | 0.004019873761938199 |\n + +----------+------------------------+------------------------+-----------------------+""" # GENERATOR LEVEL + + sample_2 = """+----------+-----------------------+------------------------+-----------------------+\n + | Variable | Value | Lower Error | Upper Error |\n + +----------+-----------------------+------------------------+-----------------------+\n + | AFB | -0.012550006150333942 | -0.0026956457681952418 | 0.0026965400452885245 |\n + | FL | 0.775244817820803 | -0.0030583204477219544 | 0.003052276555621123 |\n + | S3 | -0.004731791649125892 | -0.0035876206689923096 | 0.0035671656402230033 |\n + | S4 | -0.12872329698226329 | -0.0054353577466807685 | 0.005452103886085265 |\n + | S5 | -0.13102847916212362 | -0.005094396678852025 | 0.005083576918431511 |\n + | S7 | 0.014829855067600298 | -0.005251337297972407 | 0.005258433080994564 |\n + | S8 | 0.011919411650166327 | -0.005666331650738382 | 0.005691209183046813 |\n + | S9 | -0.013513044362093558 | -0.0036909184587611017 | 0.0036915155625497006 |\n + +----------+-----------------------+------------------------+-----------------------+""" # MC LEVEL + + variables = ["AFB", "FL", "S3", "S4", "S5", "S7", "S8", "S9"] + select_regex = [".*AFB.*\d+", ".*FL.*\d+", ".*S3.*\d+", ".*S4.*\d+", ".*S5.*\d+", ".*S7.*\d+", ".*S8.*\d+", ".*S9.*\d+"] + value_regex = "[- ]\d+.\d+" + sample_table_1 = {'1': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None}} + sample_table_2 = {'1': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None}} + samples = [sample_1, sample_2] + + for variable, regex in zip(variables, select_regex): + sample_1_splitted = samples[0].splitlines() + for line in sample_1_splitted: + subresult = re.match(regex, line) + if subresult != None: + substring = subresult.group(0) + result = re.findall(value_regex, substring) + value = np.float64(result[0]) + lower_err = np.float64(result[1]) + upper_err = np.float64(result[2]) + + sample_table_1['1'][variable] = (value, lower_err, upper_err) + + sample_2_splitted = samples[1].splitlines() + for line in sample_2_splitted: + subresult = re.match(regex, line) + if subresult != None: + substring = subresult.group(0) + result = re.findall(value_regex, substring) + value = np.float64(result[0]) + lower_err = np.float64(result[1]) + upper_err = np.float64(result[2]) + + sample_table_2['1'][variable] = (value, lower_err, upper_err) + + info_table = PrettyTable(["Variable", r"|MC reweighted - Gen|", "Lower Error", "Upper Error", "Stat. Significance"]) + for name in variables: + index = '1' + delta_value = np.abs(sample_table_1[index][name][0] - sample_table_2[index][name][0]) + quadratic_lower = -np.sqrt(sample_table_1[index][name][1]**2 + sample_table_2[index][name][1]**2) + quadratic_upper = np.sqrt(sample_table_1[index][name][2]**2 + sample_table_2[index][name][2]**2) + stat_significance = np.abs(delta_value / quadratic_lower) + info_table.add_row([name, delta_value, quadratic_lower, quadratic_upper, stat_significance]) + info_table.title = f"{bin_labels}" + print(info_table) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_mc_gen.py b/Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_mc_gen.py new file mode 100644 index 0000000..5b82fe5 --- /dev/null +++ b/Code/Scripts/Python Scripts/MC Fit/diff_pair_tables_mc_gen.py @@ -0,0 +1,81 @@ +import re +import numpy as np + +from prettytable import PrettyTable + + +def main(): + bin_labels = r"$1.10 < q^2 < 6.00$" + + sample_1 = """+----------+------------------------+------------------------+-----------------------+\n + | Variable | Value | Lower Error | Upper Error |\n + +----------+------------------------+------------------------+-----------------------+\n + | AFB | -0.030693939006905927 | -0.0027448398251912667 | 0.0026690127045430573 |\n + | FL | 0.7641860897390124 | -0.0031519177028962353 | 0.00318403722094425 |\n + | S3 | -0.009793642767841643 | -0.0039500538800350165 | 0.003962284039325228 |\n + | S4 | -0.11948833798312776 | -0.005018301784506872 | 0.005112053156795224 |\n + | S5 | -0.14672854148123884 | -0.005077800231656763 | 0.0051383664423323315 |\n + | S7 | -0.0009713979621990259 | -0.005287904531482017 | 0.005275166744474821 |\n + | S8 | 0.003178551357900045 | -0.005257961171467131 | 0.005258879438751017 |\n + | S9 | 0.005757808359963191 | -0.004016272402049568 | 0.004019873761938199 |\n + +----------+------------------------+------------------------+-----------------------+""" # GENERATOR LEVEL + + sample_2 = """+----------+-----------------------+-----------------------+----------------------+\n + | Variable | Value | Lower Error | Upper Error |\n + +----------+-----------------------+-----------------------+----------------------+\n + | AFB | -0.006858788562096526 | -0.006288887141153866 | 0.006202211516385528 |\n + | FL | 0.7741933027966281 | -0.007196527356496095 | 0.006980839734409447 |\n + | S3 | -0.005794055258693013 | -0.008257409210173627 | 0.008381420647898805 |\n + | S4 | -0.13252141252161223 | -0.012754778513953539 | 0.012393845350580537 |\n + | S5 | -0.13982805972042112 | -0.011883305368342971 | 0.011682110709131568 |\n + | S7 | 0.01499315459684105 | -0.012227400419826324 | 0.012191295064893278 |\n + | S8 | 0.010750628153590385 | -0.013096041377945507 | 0.013227158788846905 |\n + | S9 | -0.013084904237743692 | -0.00855475117009418 | 0.008616146883219716 |\n + +----------+-----------------------+-----------------------+----------------------+""" # MC LEVEL + + variables = ["AFB", "FL", "S3", "S4", "S5", "S7", "S8", "S9"] + select_regex = [".*AFB.*\d+", ".*FL.*\d+", ".*S3.*\d+", ".*S4.*\d+", ".*S5.*\d+", ".*S7.*\d+", ".*S8.*\d+", ".*S9.*\d+"] + value_regex = "[- ]\d+.\d+" + sample_table_1 = {'1': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None}} + sample_table_2 = {'1': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None}} + samples = [sample_1, sample_2] + + for variable, regex in zip(variables, select_regex): + sample_1_splitted = samples[0].splitlines() + for line in sample_1_splitted: + subresult = re.match(regex, line) + if subresult != None: + substring = subresult.group(0) + result = re.findall(value_regex, substring) + value = np.float64(result[0]) + lower_err = np.float64(result[1]) + upper_err = np.float64(result[2]) + + sample_table_1['1'][variable] = (value, lower_err, upper_err) + + sample_2_splitted = samples[1].splitlines() + for line in sample_2_splitted: + subresult = re.match(regex, line) + if subresult != None: + substring = subresult.group(0) + result = re.findall(value_regex, substring) + value = np.float64(result[0]) + lower_err = np.float64(result[1]) + upper_err = np.float64(result[2]) + + sample_table_2['1'][variable] = (value, lower_err, upper_err) + + info_table = PrettyTable(["Variable", r"|MC - Gen|", "Lower Error", "Upper Error", "Stat. Significance"]) + for name in variables: + index = '1' + delta_value = np.abs(sample_table_1[index][name][0] - sample_table_2[index][name][0]) + quadratic_lower = -np.sqrt(sample_table_1[index][name][1]**2 + sample_table_2[index][name][1]**2) + quadratic_upper = np.sqrt(sample_table_1[index][name][2]**2 + sample_table_2[index][name][2]**2) + stat_significance = np.abs(delta_value / quadratic_lower) + info_table.add_row([name, delta_value, quadratic_lower, quadratic_upper, stat_significance]) + info_table.title = f"{bin_labels}" + print(info_table) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Code/Scripts/Python Scripts/MC Fit/reweighted_angular_acceptance_modeling.py b/Code/Scripts/Python Scripts/MC Fit/reweighted_angular_acceptance_modeling.py new file mode 100644 index 0000000..7bfad11 --- /dev/null +++ b/Code/Scripts/Python Scripts/MC Fit/reweighted_angular_acceptance_modeling.py @@ -0,0 +1,94 @@ +import os +import dotenv +import sys +import argparse +import mplhep +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +dotenv.load_dotenv('../properties.env') + +sys.path.insert(0, os.getenv('SYS_PATH')) + +from analysis.efficiency import get_efficiency_model_class + +from hep_analytics.processing.extract import FileManager +from hep_analytics.processing.transform import select_feature, reweight_feature +from hep_analytics.processing.visualisation import reweight_comparing_plot + +FILE_GEN = os.getenv('GEN_FILE') +FILE_MC_PHSP = os.getenv('MC_PHSP_FILE') + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--q2bin', dest ='q2bin', default = 0) + args = parser.parse_args() + Q2BIN = int(args.q2bin) + + mplhep.style.use("LHCb2") + + 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)] + print(f"Selected Q2 Bin Range is {bin_ranges[Q2BIN]}") + + bin_labels = [r"$0.25 < q^2 < 4.00$", r"$4.00 < q^2 < 8.00$", r"$11.00 < q^2 < 12.50$", + r"$15.00 < q^2 < 18.00$", r"$1.10 < q^2 < 6.00$", + r"$1.1 < q^2 < 2.5$", r"$2.5 < q^2 < 4.0$", r"$4.0 < q^2 < 6.0$", r"$6.0 < q^2 < 8.0$"] + + filemanager = FileManager(file = FILE_MC_PHSP, tree = "Events", branches = ["q2", "costhetak", "costhetal", "phi"]) + mc_phsp_data = filemanager.extract_data() + 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] + q2_mc_phsp, indices = select_feature(feature = q2_mc_phsp, limits = bin_ranges[Q2BIN]) + phi_mc_phsp = phi_mc_phsp[indices] + theta_l_mc_phsp = theta_l_mc_phsp[indices] + theta_k_mc_phsp = theta_k_mc_phsp[indices] + lower_costhetak_cut = float(os.getenv('LOWER_COSTHETAK_CUT')) + upper_costhetak_cut = float(os.getenv('UPPER_COSTHETAK_CUT')) + theta_k_mc_phsp, indices = select_feature(feature = theta_k_mc_phsp, limits = (lower_costhetak_cut, upper_costhetak_cut)) + q2_mc_phsp = q2_mc_phsp[indices] + phi_mc_phsp = phi_mc_phsp[indices] + theta_l_mc_phsp = theta_l_mc_phsp[indices] + + filemanager = FileManager(file = FILE_GEN, tree = "Events", branches = ["q2", "costhetak", "costhetal", "phi"]) + gen_data = filemanager.extract_data() + q2_gen, theta_k_gen, theta_l_gen, phi_gen = gen_data[0], gen_data[1], gen_data[2], gen_data[3] + q2_gen, indices = select_feature(feature = q2_gen, limits = bin_ranges[Q2BIN]) + phi_gen = phi_gen[indices] + theta_l_gen = theta_l_gen[indices] + theta_k_gen = theta_k_gen[indices] + theta_k_gen, indices = select_feature(feature = theta_k_gen, limits = (lower_costhetak_cut, upper_costhetak_cut)) + q2_gen = q2_gen[indices] + phi_gen = phi_gen[indices] + theta_l_gen = theta_l_gen[indices] + + q2_mc_phsp_weights = reweight_feature(original_feature = q2_mc_phsp, target_feature = q2_gen, n_bins = 25) + reweight_comparing_plot(original_feature = q2_mc_phsp, target_feature = q2_gen, weights = q2_mc_phsp_weights, + n_bins = 25, suptitle = f"{bin_labels[Q2BIN]}", + titles = ["Q2 MC PHSP", "Q2 MC PHSP (reweighted)", "Q2 Gen"], save = f"reweighted_q2_gen_mc_phsp_{Q2BIN}_bin.png") + + df = pd.DataFrame({'ctl': theta_l_mc_phsp, 'ctk': theta_k_mc_phsp, 'phi': phi_mc_phsp, 'q2': q2_mc_phsp, 'weights': q2_mc_phsp_weights}) + orders = {"ctl": 4, "ctk": 6, "phi": 2} + ranges = {"ctl": [-1.0, 1.0], "ctk": [lower_costhetak_cut, upper_costhetak_cut], "phi": [-np.pi, np.pi]} + EffClass = get_efficiency_model_class('legendre') + eff = EffClass.fit(df, ['ctl', 'ctk', 'phi'], weight_var = "weights", ranges = ranges, + legendre_orders = orders, calculate_cov = False, chunk_size = 2000) + + out_file = eff.write_to_disk(f'acc_3d_JpsiKstMC_reweighted_{Q2BIN}_bin.yaml') + print(out_file) + + labels = {'ctl': r'$\cos \theta_L$', 'ctk': r'$\cos \theta_K$', 'phi': '$\phi$', 'q2': '$q^2$ [GeV$^2$]'} + for v in ['ctl', 'ctk', 'phi']: + fig, ax = plt.subplots(figsize = (15, 10)) + plt.xlim(*ranges[v]) + x, y = eff.project_efficiency(v, n_points = 1000) + plt.plot(x, y, 'b-') + plt.hist(df[v], density = True, bins = 50, color = 'grey', alpha = 0.5) + plt.ylabel("a.u.", horizontalalignment = 'right', y = 1.0) + plt.xlabel(labels[v], horizontalalignment = 'right', x = 1.0) + plt.savefig(f'acc_3d_JpsiKstMC_{v}_{Q2BIN}_bin.pdf') + plt.close() + + +if __name__ == "__main__": + main() diff --git a/Code/Scripts/Python Scripts/MC Fit/reweighted_angular_fit.py b/Code/Scripts/Python Scripts/MC Fit/reweighted_angular_fit.py new file mode 100644 index 0000000..8499494 --- /dev/null +++ b/Code/Scripts/Python Scripts/MC Fit/reweighted_angular_fit.py @@ -0,0 +1,97 @@ +import os +import dotenv +import sys +import argparse +import pandas as pd +import mplhep +import zfit +from prettytable import PrettyTable + +dotenv.load_dotenv('../properties.env') + +sys.path.insert(0, os.getenv('SYS_PATH')) + +from b2kstll.models.angular import B2Kstll +from b2kstll.plot import plot_distributions + +from hep_analytics.processing.extract import FileManager +from hep_analytics.processing.transform import select_feature, reweight_feature +from hep_analytics.processing.visualisation import reweight_comparing_plot + +FILE_MC = os.getenv('MC_FILE') +FILE_GEN = os.getenv('GEN_FILE') + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--q2bin', dest ='q2bin', default = 0) + args = parser.parse_args() + Q2BIN = int(args.q2bin) + + mplhep.style.use("LHCb2") + + 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)] + print(f"Selected Q2 Bin Range is {bin_ranges[Q2BIN]}") + + bin_labels = [r"$0.25 < q^2 < 4.00$", r"$4.00 < q^2 < 8.00$", r"$11.00 < q^2 < 12.50$", + r"$15.00 < q^2 < 18.00$", r"$1.10 < q^2 < 6.00$", + r"$1.1 < q^2 < 2.5$", r"$2.5 < q^2 < 4.0$", r"$4.0 < q^2 < 6.0$", r"$6.0 < q^2 < 8.0$"] + + filemanager = FileManager(file = FILE_MC, tree = "Events", branches = ["q2", "costhetak", "costhetal", "phi"]) + mc_data = filemanager.extract_data() + q2_mc, theta_k_mc, theta_l_mc, phi_mc = mc_data[0], mc_data[1], mc_data[2], mc_data[3] + q2_mc, indices = select_feature(feature = q2_mc, limits = bin_ranges[Q2BIN]) + phi_mc = phi_mc[indices] + theta_l_mc = theta_l_mc[indices] + theta_k_mc = theta_k_mc[indices] + lower_costhetak_cut = float(os.getenv('LOWER_COSTHETAK_CUT')) + upper_costhetak_cut = float(os.getenv('UPPER_COSTHETAK_CUT')) + theta_k_mc, indices = select_feature(feature = theta_k_mc, limits = (lower_costhetak_cut, upper_costhetak_cut)) + q2_mc = q2_mc[indices] + phi_mc = phi_mc[indices] + theta_l_mc = theta_l_mc[indices] + + filemanager = FileManager(file = FILE_GEN, tree = "Events", branches = ["q2", "costhetak", "costhetal", "phi"]) + gen_data = filemanager.extract_data() + q2_gen, theta_k_gen, theta_l_gen, phi_gen = gen_data[0], gen_data[1], gen_data[2], gen_data[3] + q2_gen, indices = select_feature(feature = q2_gen, limits = bin_ranges[Q2BIN]) + phi_gen = phi_gen[indices] + theta_l_gen = theta_l_gen[indices] + theta_k_gen = theta_k_gen[indices] + theta_k_gen, indices = select_feature(feature = theta_k_gen, limits = (lower_costhetak_cut, upper_costhetak_cut)) + q2_gen = q2_gen[indices] + phi_gen = phi_gen[indices] + theta_l_gen = theta_l_gen[indices] + + angular_data = pd.DataFrame({'ctl': theta_l_mc, 'ctk': theta_k_mc, 'phi': phi_mc}) + angular_data.to_csv(f"ang_fit_mc_q2_bins_{Q2BIN}_bin.csv", index = False) + x = B2Kstll('ctl','ctk','phi') + x.set_acc(f"./acc_3d_JpsiKstMC_reweighted_4_bin.yaml") + obs, pdf, _ = x.get_pdf('PWave') + + q2_mc_weights = reweight_feature(original_feature = q2_mc, target_feature = q2_gen, n_bins = 25) + reweight_comparing_plot(original_feature = q2_mc, target_feature = q2_gen, weights = q2_mc_weights, + n_bins = 25, suptitle = f"{bin_labels[Q2BIN]}", titles = ["Q2 MC", "Q2 MC (reweighted)", "Q2 Gen"], save = "reweighted_q2_gen_mc.png") + + datazfit = zfit.Data.from_pandas(df = angular_data, obs = obs, weights = q2_mc_weights) + nll = zfit.loss.UnbinnedNLL(model = pdf, data = datazfit) + minimizer = zfit.minimize.Minuit() + result = minimizer.minimize(nll) + param_errors, _ = result.errors() + print(param_errors) + + info_table = PrettyTable(["Variable", "Value", "Lower Error", "Upper Error"]) + fit_labels = ["AFB", "FL", "S3", "S4", "S5", "S7", "S8", "S9"] + for name in fit_labels: + value = result.params[name]["value"] + lower = result.params[name]["minuit_minos"]["lower"] + upper = result.params[name]["minuit_minos"]["upper"] + info_table.add_row([name, value, lower, upper]) + + print(info_table) + + plot_distributions(result, suffix = f"accPwavePDF_{Q2BIN}_bin_reweighted_mc") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Code/Scripts/Python Scripts/MC Fit/table_average_to_compare.py b/Code/Scripts/Python Scripts/MC Fit/table_average_to_compare.py new file mode 100644 index 0000000..7d10e9d --- /dev/null +++ b/Code/Scripts/Python Scripts/MC Fit/table_average_to_compare.py @@ -0,0 +1,97 @@ +import re +import numpy as np + +from prettytable import PrettyTable + + +def main(): + table_1 = """+----------+-----------------------+-----------------------+----------------------+\n + | Variable | Value | Lower Error | Upper Error |\n + +----------+-----------------------+-----------------------+----------------------+\n + | AFB | -0.1327941714665493 | -0.011751983569979923 | 0.011695518975582573 |\n + | FL | 0.778784190310989 | -0.013786738313616998 | 0.01334631516152801 |\n + | S3 | 0.012016752919524067 | -0.014186281678810328 | 0.01390470699489175 |\n + | S4 | 0.0030912112690471196 | -0.02494551503535478 | 0.02500682819273369 |\n + | S5 | 0.06257482631549156 | -0.022576669869741432 | 0.02267902660423071 |\n + | S7 | 0.026461647341068015 | -0.022870493984060422 | 0.022709046371542273 |\n + | S8 | 0.022528200778115424 | -0.024500119399894143 | 0.02444883875849664 |\n + | S9 | -0.010047596608480971 | -0.015100958783891871 | 0.01497061747614531 |\n + +----------+-----------------------+-----------------------+----------------------+""" + + table_2 = """+----------+-----------------------+-----------------------+----------------------+\n + | Variable | Value | Lower Error | Upper Error |\n + +----------+-----------------------+-----------------------+----------------------+\n + | AFB | -0.044313915998597596 | -0.011266005942710903 | 0.011376116194169396 |\n + | FL | 0.8080898316779715 | -0.012903371237386076 | 0.012627755576193413 |\n + | S3 | -0.001350693384098943 | -0.01425102196417036 | 0.014014236056631763 |\n + | S4 | -0.16348388538343442 | -0.022982703026786865 | 0.02353050491741336 |\n + | S5 | -0.12998638915138938 | -0.02207623402690808 | 0.02230804022923196 |\n + | S7 | -0.019175072605277906 | -0.022219297227352726 | 0.02233298418372788 |\n + | S8 | 0.03896504393685167 | -0.02306667997644898 | 0.023056355590581525 |\n + | S9 | -0.026410777123662296 | -0.01497373735832672 | 0.015237714795859537 |\n + +----------+-----------------------+-----------------------+----------------------+""" + + table_3 = """+----------+-----------------------+-----------------------+----------------------+\n + | Variable | Value | Lower Error | Upper Error |\n + +----------+-----------------------+-----------------------+----------------------+\n + | AFB | 0.06877321968399613 | -0.00873785505692959 | 0.008866035437567673 |\n + | FL | 0.7579354189983714 | -0.01048129883366948 | 0.01017491937670441 |\n + | S3 | -0.02540601876438032 | -0.012075664333196083 | 0.012192632205060838 |\n + | S4 | -0.18300822691744986 | -0.015545142299112552 | 0.016136099542591972 |\n + | S5 | -0.28137307676130335 | -0.0160784362375397 | 0.01630021297009425 |\n + | S7 | 0.033064765761582675 | -0.01837491321971226 | 0.018457429889962827 |\n + | S8 | -0.022661825503764606 | -0.018831356033962507 | 0.018816177761568176 |\n + | S9 | -0.01151502616377776 | -0.012778180225448249 | 0.013091600028830737 |\n + +----------+-----------------------+-----------------------+----------------------+""" + + bin_labels = [r"$0.25 < q^2 < 4.00$", r"$4.00 < q^2 < 8.00$", r"$11.00 < q^2 < 12.50$", + r"$15.00 < q^2 < 18.00$", r"$1.10 < q^2 < 6.00$", + r"$1.1 < q^2 < 2.5$", r"$2.5 < q^2 < 4.0$", r"$4.0 < q^2 < 6.0$", r"$6.0 < q^2 < 8.0$"] + + tables = [table_1, table_2, table_3] + variables = ["AFB", "FL", "S3", "S4", "S5", "S7", "S8", "S9"] + select_regex = [".*AFB.*\d+", ".*FL.*\d+", ".*S3.*\d+", ".*S4.*\d+", ".*S5.*\d+", ".*S7.*\d+", ".*S8.*\d+", ".*S9.*\d+"] + value_regex = "[- ]\d+.\d+" + samples = {'1': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None}, + '2': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None}, + '3': {'AFB': None, 'FL': None, 'S3': None, 'S4': None, 'S5': None, 'S7': None, 'S8': None, 'S9': None}, + } + + for index in range(len(tables)): + for variable, regex in zip(variables, select_regex): + splitted = tables[index].splitlines() + for line in splitted: + subresult = re.match(regex, line) + if subresult != None: + substring = subresult.group(0) + result = re.findall(value_regex, substring) + value = np.float64(result[0]) + lower_err = np.float64(result[1]) + upper_err = np.float64(result[2]) + + samples[str(index + 1)][variable] = (value, lower_err, upper_err) + + fit_variables_split = [[] for _ in range(8)] + fit_errors_split = [[] for _ in range(8)] + for value in samples.values(): + count = 0 + for fitvalue in value.values(): + fit_variables_split[count].append(fitvalue[0]) + fit_errors_split[count].append(np.sqrt(fitvalue[1]**2 + fitvalue[2]**2)) + count += 1 + + info_table = PrettyTable(["Variable", "Value", "Lower Error", "Upper Error"]) + info_table.title = f"q2_split_{bin_labels[4]}_average" + fit_labels = ["AFB", "FL", "S3", "S4", "S5", "S7", "S8", "S9"] + count = 0 + for name, fit_split_var, fit_err_split in zip(fit_labels, fit_variables_split, fit_errors_split): + fit_avg = np.mean(fit_split_var) + error_split = 1/3 * np.sqrt(fit_err_split[0]**2 + fit_err_split[1]**2 + fit_err_split[2]**2) + info_table.add_row([name, fit_avg, -error_split, error_split]) + count += 1 + + print(info_table) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Code/Scripts/Python Scripts/properties.env b/Code/Scripts/Python Scripts/properties.env new file mode 100644 index 0000000..335108a --- /dev/null +++ b/Code/Scripts/Python Scripts/properties.env @@ -0,0 +1,7 @@ +LOWER_COSTHETAK_CUT=-1.00 +UPPER_COSTHETAK_CUT=0.851 +SYS_PATH=../../ +SYS_PATH_LEVEL_1=../ +GEN_FILE=../../KplusPi0Resolved_Run2_MC_EvtGen_FCNC.root +MC_FILE=../../KplusPi0Resolved_Run2_MC_FCNC.root +MC_PHSP_FILE="../../KplusPi0Resolved_Run2_PHSP_FCNC.root" diff --git a/Code/Scripts/Python Scripts/requirements.txt b/Code/Scripts/Python Scripts/requirements.txt new file mode 100644 index 0000000..ea83dfc --- /dev/null +++ b/Code/Scripts/Python Scripts/requirements.txt @@ -0,0 +1,208 @@ +absl-py==0.15.0 +aenum==3.1.11 +algopy==0.5.7 +analysis @ git+https://github.com/mayou36/analysis-tools@5db2f64e48cac26954d182b62eea7665d26847d2 +appdirs==1.4.4 +argon2-cffi @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi_1640817743617/work +argon2-cffi-bindings @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi-bindings_1649500321618/work +asdf==2.11.1 +asdf-standard==1.0.2 +asdf-transform-schemas==0.2.2 +asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1618968359944/work +astunparse==1.6.3 +attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1640799537051/work +awkward==1.8.0 +awkward0==0.15.5 +-e git+https://gitlab.cern.ch/LHCb-RD/ewp-bd2ksteeangular-legacy.git@c5e306ac36ce7de31ff8c0e2f1833c1d3b2e1dcb#egg=b2kstll&subdirectory=b2kstll +backcall @ file:///home/conda/feedstock_root/build_artifacts/backcall_1592338393461/work +backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1618230623929/work +beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1649463573192/work +bleach @ file:///home/conda/feedstock_root/build_artifacts/bleach_1649361991009/work +cachetools==4.2.4 +certifi==2021.10.8 +cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1636046055389/work +charset-normalizer==2.0.12 +ckmutil==0.3.2 +clang==5.0 +cloudpickle==2.0.0 +colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1602866480661/work +colored==1.4.3 +colorlog==6.6.0 +ConfigArgParse==1.5.3 +connection-pool==0.0.3 +contextlib2==21.6.0 +cycler==0.11.0 +datrie==0.8.2 +debugpy @ file:///home/conda/feedstock_root/build_artifacts/debugpy_1649586347330/work +decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work +defusedxml @ file:///home/conda/feedstock_root/build_artifacts/defusedxml_1615232257335/work +dm-tree==0.1.7 +docutils==0.18.1 +dotmap==1.3.30 +dpath==2.0.6 +entrypoints @ file:///home/conda/feedstock_root/build_artifacts/entrypoints_1643888246732/work +executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1646044401614/work +fasteners==0.17.3 +fastjsonschema @ file:///home/conda/feedstock_root/build_artifacts/python-fastjsonschema_1641751198313/work/dist +flatbuffers==1.12 +flavio==2.3.3 +flit_core @ file:///home/conda/feedstock_root/build_artifacts/flit-core_1645629044586/work/source/flit_core +fonttools==4.33.3 +formulate==0.1.1 +future==0.18.2 +gast==0.4.0 +gitdb==4.0.9 +GitPython==3.1.27 +google-auth==1.35.0 +google-auth-oauthlib==0.4.6 +google-pasta==0.2.0 +grpcio==1.46.1 +h5py==3.1.0 +hep-ml==0.7.1 +hepstats==0.5.0 +hepunits==2.2.0 +idna==3.3 +iminuit @ file:///home/conda/feedstock_root/build_artifacts/iminuit_1649770305045/work +importlib-metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1648728288044/work +importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1650169872758/work +iniconfig==1.1.1 +ipykernel @ file:///home/conda/feedstock_root/build_artifacts/ipykernel_1649684276995/work/dist/ipykernel-6.13.0-py3-none-any.whl +ipyopt==0.11.0 +ipyparallel @ file:///home/conda/feedstock_root/build_artifacts/ipyparallel_1652183878568/work +ipython @ file:///home/conda/feedstock_root/build_artifacts/ipython_1651238283435/work +ipython-genutils==0.2.0 +jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1649067096717/work +Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1651774399431/work +jmespath==1.0.0 +joblib==1.1.0 +jsonschema @ file:///home/conda/feedstock_root/build_artifacts/jsonschema-meta_1651798819471/work +jupyter-client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1652061014773/work +jupyter-core @ file:///home/conda/feedstock_root/build_artifacts/jupyter_core_1652365269273/work +jupyterlab-pygments @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_pygments_1649936611996/work +keras==2.6.0 +Keras-Preprocessing==1.1.2 +kiwisolver==1.4.2 +llvmlite==0.38.0 +Markdown==3.3.7 +MarkupSafe @ file:///home/conda/feedstock_root/build_artifacts/markupsafe_1648737556467/work +matplotlib==3.5.2 +matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1631080358261/work +metakernel @ file:///home/conda/feedstock_root/build_artifacts/metakernel_1648594625035/work +mistune @ file:///home/conda/feedstock_root/build_artifacts/mistune_1635844679781/work +mplhep==0.3.23 +mplhep-data==0.0.3 +mpmath==1.2.1 +nbclient @ file:///home/conda/feedstock_root/build_artifacts/nbclient_1652155694429/work +nbconvert @ file:///home/conda/feedstock_root/build_artifacts/nbconvert-meta_1649676641343/work +nbformat @ file:///home/conda/feedstock_root/build_artifacts/nbformat_1651607001005/work +nest-asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1648959695634/work +nlopt==2.7.0 +notebook @ file:///home/conda/feedstock_root/build_artifacts/notebook_1650363291341/work +numba @ file:///home/conda/feedstock_root/build_artifacts/numba_1652226553013/work +numdifftools==0.9.40 +numexpr==2.8.1 +numpy==1.19.5 +oauthlib==3.2.0 +opt-einsum==3.3.0 +ordered-set==4.1.0 +packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1637239678211/work +pandas==1.4.2 +pandocfilters @ file:///home/conda/feedstock_root/build_artifacts/pandocfilters_1631603243851/work +parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1638334955874/work +particle==0.20.1 +patsy==0.5.2 +pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1602535608087/work +pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work +Pillow==9.1.0 +plac==1.3.5 +pluggy==1.0.0 +portalocker @ file:///home/conda/feedstock_root/build_artifacts/portalocker_1645465641398/work +prettytable==3.3.0 +progressbar2==4.0.0 +prometheus-client @ file:///home/conda/feedstock_root/build_artifacts/prometheus_client_1649447152425/work +prompt-toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1649130487073/work +protobuf==3.20.1 +psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1648872973590/work +ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl +PuLP==2.6.0 +pure-eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1642875951954/work +py==1.11.0 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work +Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1650904496387/work +pylha==0.4 +pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1652235407899/work +PyRoot==0.3.0 +pyrsistent @ file:///home/conda/feedstock_root/build_artifacts/pyrsistent_1649013355209/work +pytest==7.1.2 +python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work +python-dotenv==0.20.0 +python-utils==3.2.2 +pytz==2022.1 +PyYAML==6.0 +pyzmq @ file:///home/conda/feedstock_root/build_artifacts/pyzmq_1649055645150/work +ratelimiter==1.2.0.post0 +requests==2.27.1 +requests-oauthlib==1.3.1 +retry==0.9.2 +root-numpy @ file:///home/conda/feedstock_root/build_artifacts/root_numpy_1642275926538/work +root-pandas==0.7.0 +rsa==4.8 +rundec==0.5.2 +scikit-learn==1.1.0 +scipy==1.8.0 +seaborn==0.11.2 +semantic-version==2.9.0 +Send2Trash @ file:///home/conda/feedstock_root/build_artifacts/send2trash_1628511208346/work +six==1.15.0 +smart-open==6.0.0 +smmap==5.0.0 +snakemake==7.6.2 +soupsieve @ file:///home/conda/feedstock_root/build_artifacts/soupsieve_1638550740809/work +stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1644872665635/work +statsmodels==0.13.2 +stopit==1.1.2 +sympy==1.10.1 +tables==3.7.0 +tabulate==0.8.9 +tensorboard==2.6.0 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.1 +tensorflow==2.6.3 +tensorflow-addons==0.16.1 +tensorflow-estimator==2.6.0 +tensorflow-gpu==2.6.3 +tensorflow-graphics-gpu==1.0.0 +tensorflow-probability==0.13.0 +termcolor==1.1.0 +terminado @ file:///home/conda/feedstock_root/build_artifacts/terminado_1649103779998/work +texttable==1.6.4 +tf-quant-finance==0.0.1.dev30 +Theano==1.0.5 +threadpoolctl==3.1.0 +tinycss2 @ file:///home/conda/feedstock_root/build_artifacts/tinycss2_1637612658783/work +tomli==2.0.1 +toposort==1.7 +tornado @ file:///home/conda/feedstock_root/build_artifacts/tornado_1648827245914/work +tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1649051611147/work +traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1652201717608/work +typeguard==2.13.3 +typing-extensions==3.10.0.2 +uhi==0.3.1 +uncertainties==3.1.6 +uproot==4.2.3 +uproot3-methods==0.10.1 +urllib3==1.26.9 +voluptuous==0.13.1 +wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1600965781394/work +webencodings==0.5.1 +Werkzeug==2.1.2 +wilson==2.2.1 +wrapt==1.12.1 +xgboost==1.6.1 +xrootd==5.4.2 +yamlloader==1.1.0 +yte==1.4.0 +zfit==0.8.3 +zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1649012893348/work diff --git a/Code/Scripts/Python Scripts/root_file_inspect.py b/Code/Scripts/Python Scripts/root_file_inspect.py new file mode 100644 index 0000000..f93a6e3 --- /dev/null +++ b/Code/Scripts/Python Scripts/root_file_inspect.py @@ -0,0 +1,29 @@ +import os +import dotenv +import sys +import argparse + +dotenv.load_dotenv('properties.env') + +sys.path.insert(0, os.getenv('SYS_PATH_LEVEL_1')) + +from rootio import RootIO + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--file', dest = 'file', default = '') + parser.add_argument('--regex', dest = 'regex', default = '') + args = parser.parse_args() + FILE = args.file + regex = args.regex + + rootio = RootIO(FILE) + if regex: + rootio.get_information(regex = regex) + return + rootio.get_information() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/Code/Scripts/Python Scripts/rootio.py b/Code/Scripts/Python Scripts/rootio.py new file mode 100755 index 0000000..c14a847 --- /dev/null +++ b/Code/Scripts/Python Scripts/rootio.py @@ -0,0 +1,37 @@ +import os +import re +import uproot + + +class RootIO: + def __init__(self, path_to_file: str): + if not os.path.exists(path = path_to_file): + raise ValueError("Path does not exists!") + self.path_to_file = path_to_file + + + def get_information(self, regex: str = None) -> None: + with uproot.open(self.path_to_file) as file: + print(f"File classnames:\n{file.classnames()}") + for classname, classtype in file.classnames().items(): + print(f"Classname:\n{classname}") + print(f"Classtype:\n{classtype}") + print(f"Content of the class:") + for name, branch in file[classname].items(): + print(f"{name}: {branch}") + if regex != None: + print(f"Regex search results:") + attributes = file[classname].keys() + for attribute in attributes: + result = self.__find_pattern_in_text(regex = regex, text = attribute) + if result != "": + print(f"{result}") + + + def __find_pattern_in_text(self, regex: str, text: str) -> str: + search_result = re.search(pattern = regex, string = text) + result = "" + if search_result != None: + result = search_result.group(0) + + return result \ No newline at end of file diff --git a/Code/Scripts/hep_analytics/__init__.py b/Code/Scripts/hep_analytics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Code/Scripts/hep_analytics/pathing/__init__.py b/Code/Scripts/hep_analytics/pathing/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Code/Scripts/hep_analytics/pathing/aggregate.py b/Code/Scripts/hep_analytics/pathing/aggregate.py new file mode 100644 index 0000000..a9733da --- /dev/null +++ b/Code/Scripts/hep_analytics/pathing/aggregate.py @@ -0,0 +1,13 @@ +class PathAggregator: + def __init__(self, basefolder: str, directory: str, file: str, year: str = None) -> None: + self.basefolder = basefolder + self.directory = directory + self.file = file + self.year = year + + + def get_path_to_file(self) -> str: + if self.year != None: + return f"./{self.basefolder}/{self.directory}/{self.year}/{self.file}" + + return f"./{self.basefolder}/{self.directory}/{self.file}" \ No newline at end of file diff --git a/Code/Scripts/hep_analytics/processing/__init__.py b/Code/Scripts/hep_analytics/processing/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Code/Scripts/hep_analytics/processing/extract.py b/Code/Scripts/hep_analytics/processing/extract.py new file mode 100644 index 0000000..2f1ba32 --- /dev/null +++ b/Code/Scripts/hep_analytics/processing/extract.py @@ -0,0 +1,20 @@ +import numpy as np +import uproot +import typing +from dataclasses import dataclass + + +@dataclass +class FileManager: + file: str + tree: str + branches: list[str] + + def extract_data(self) -> list[np.ndarray]: + with uproot.open(self.file) as file: + file_tree = file[self.tree] + data = [] + for branch in self.branches: + data.append(file_tree[branch].array(library = "np")) + + return data diff --git a/Code/Scripts/hep_analytics/processing/transform.py b/Code/Scripts/hep_analytics/processing/transform.py new file mode 100644 index 0000000..4144d40 --- /dev/null +++ b/Code/Scripts/hep_analytics/processing/transform.py @@ -0,0 +1,20 @@ +import numpy as np + +from hep_ml.reweight import BinsReweighter + + +def select_feature(feature: np.ndarray, limits: tuple[float, float]) -> tuple[np.ndarray, list]: + selection_indices = [] + for index, value in enumerate(feature): + if value > limits[0] and value < limits[1]: + selection_indices.append(index) + + return feature[selection_indices], selection_indices + + +def reweight_feature(original_feature: list, target_feature: list, n_bins: int, n_neighs: int = 2): + original_weights = np.ones(len(original_feature)) + bin_reweighter = BinsReweighter(n_bins = n_bins, n_neighs = n_neighs) + bin_reweighter.fit(original = original_feature, target = target_feature, original_weight = original_weights) + + return bin_reweighter.predict_weights(original = original_feature, original_weight = original_weights) \ No newline at end of file diff --git a/Code/Scripts/hep_analytics/processing/visualisation.py b/Code/Scripts/hep_analytics/processing/visualisation.py new file mode 100644 index 0000000..1fedf3f --- /dev/null +++ b/Code/Scripts/hep_analytics/processing/visualisation.py @@ -0,0 +1,22 @@ +import matplotlib.pyplot as plt +import mplhep + + +mplhep.style.use("LHCb2") + +def reweight_comparing_plot(original_feature: list, target_feature: list, weights: list, n_bins: int, suptitle: str, titles: list[str], save: str) -> None: + if len(original_feature) != len(weights): + raise ValueError(f"original_feature and weights need to have the same dimension!") + + fig, ax = plt.subplots(nrows = 1, ncols = 3, figsize = (30, 12.5)) + + ax[0].hist(original_feature, bins = n_bins) + ax[0].set_ylabel("counts/a.u.") + ax[0].set_title(titles[0]) + ax[1].hist(original_feature, bins = n_bins, weights = weights) + ax[1].set_title(titles[1]) + ax[2].hist(target_feature, bins = n_bins) + ax[2].set_title(titles[2]) + + fig.suptitle(suptitle) + fig.savefig(f"{save}") \ No newline at end of file