svd_time.py

#!/usr/bin/env python3
 

 
from pathlib import Path
 
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import re
 
from prompt import ValidationSettings
import svd.validation_utils as vu
 
import ROOT as r
r.PyConfig.IgnoreCommandLineOptions = True
r.gROOT.SetBatch()
 
matplotlib.use('Agg')
plt.style.use("belle2")
 

settings = ValidationSettings(name="caf_svd_time",
                              description=__doc__,
                              download_files=[],
                              expert_config=None)
 
 
def run_validation(job_path, input_data_path=None, **kwargs):
    '''job_path will be replaced with path/to/calibration_results
    input_data_path will be replaced with path/to/data_path used for calibration
    e.g. /group/belle2/dataprod/Data/PromptSkim/'''
 
    collector_output_dir = Path(job_path) / 'SVDTimeValidation/0/collector_output/default/'
    output_dir = Path(kwargs.get('output_dir', 'SVDTimeValidation_output'))
    shift_detailed = kwargs.get('shift_detailed', False)
    plots_per_run = output_dir / 'runs'
 
    plots_per_run.mkdir(parents=True, exist_ok=True)
 
    files = list(collector_output_dir.glob('**/CollectorOutput.root'))
 
    agreements = {algo: {} for algo in vu.time_algorithms}
    precisions = {algo: {} for algo in vu.time_algorithms}
    discriminations = {algo: {} for algo in vu.time_algorithms}
    shift_agreements = {algo: {} for algo in vu.time_algorithms}
    entries_onTracks = {algo: {} for algo in vu.time_algorithms}
    entries_eventT0 = {algo: {} for algo in vu.time_algorithms}
    t0_agreements = {algo: {} for algo in vu.time_algorithms}
    t0_differences = {algo: {} for algo in vu.time_algorithms}
    absolute_shifts_values = {algo: {} for algo in vu.time_algorithms}
 
    roc_U = {algo: {} for algo in vu.time_algorithms}
    roc_V = {algo: {} for algo in vu.time_algorithms}
 
    CollectorHistograms = vu.get_merged_collector_histograms(files)
 
    max_total_run = 0
    total_item = 0
    for algo in CollectorHistograms:
        for exp in CollectorHistograms[algo]:
            nRun = len(CollectorHistograms[algo][exp])
            total_item += nRun
            if nRun > max_total_run:
                max_total_run = nRun
    total_length = max_total_run * len(vu.time_algorithms)
 
    print(f'Looping over {total_item} items')
    count = 0
    vu.progress(0, total_item)
 
    shift_histos = {}
    shift_histos_merged_over_ladder = {}
 
    for algo in CollectorHistograms:
        shift_histos[algo] = {}
        shift_histos_merged_over_ladder[algo] = {}
        for exp in CollectorHistograms[algo]:
            for run in CollectorHistograms[algo][exp]:
                # print(f"working with : algo {algo} exp {exp} run {run}")
                histos = vu.get_histos(CollectorHistograms[algo][exp][run])
 
                if histos is None:
                    print(f'Skipping file algo {algo} exp {exp} run {run}')
                    continue
 
                # if some histogram is empty (too little stat) do not crash but skip that file for that calibration
                try:
                    entries_eventT0_ = histos['eventT0'].GetEntries()
 
                    if run not in entries_eventT0[algo] or entries_eventT0_ > entries_eventT0[algo][run]:
                        agreements[algo][run] = {key: vu.get_agreement(histos['eventT0'], h_diff)
                                                 for key, h_diff in histos['diff'].items()}
 
                        t0_agreements[algo][run] = vu.get_agreement2(histos['CDCeventT0'], histos['SVDeventT0'], min_entries=10)
                        t0_differences[algo][run] = vu.get_difference(histos['SVDeventT0'], histos['CDCeventT0'], min_entries=10)
 
                        precisions[algo][run] = {key: vu.get_precision(h_diff)
                                                 for key, h_diff in histos['diff'].items()}
                        discriminations[algo][run] = {key: vu.get_roc_auc(histos['onTracks'][key], histos['offTracks'][key])
                                                      for key in histos['onTracks']}
                        shift_agreements[algo][run] = {key: vu.get_shift_agreement(hShift)
                                                       for key, hShift in histos['timeShifter'].items()}
                        entries_onTracks[algo][run] = {key: val.GetEntries() for key, val in histos['onTracks'].items()}
                        entries_eventT0[algo][run] = entries_eventT0_
                        absolute_shifts_values[algo][run] = histos["absoluteShift"]
 
                        if shift_detailed:
                            for key, hShift in histos['timeShifter'].items():
                                if key in shift_histos[algo]:
                                    shift_histos[algo][key].Add(hShift)
                                else:
                                    shift_histos[algo][key] = hShift.Clone()
                                    shift_histos[algo][key].SetDirectory(0)
                                sensor_id = re.findall(r'\d+', key) + [key[-1]]
                                keyGroup = f'L{sensor_id[0]}S{sensor_id[2]}{sensor_id[3]}'
                                if keyGroup in shift_histos_merged_over_ladder[algo]:
                                    shift_histos_merged_over_ladder[algo][keyGroup].Add(hShift)
                                else:
                                    shift_histos_merged_over_ladder[algo][keyGroup] = hShift.Clone()
                                    shift_histos_merged_over_ladder[algo][keyGroup].SetDirectory(0)
                        vu.make_combined_plot('*U', histos,
                                              title=f'exp {exp} run {run} U {algo}')
                        plt.savefig(plots_per_run / f'{exp}_{run}_U_{algo}.pdf')
                        plt.close()
                        vu.make_combined_plot('*V', histos,
                                              title=f'exp {exp} run {run} V {algo}')
                        plt.savefig(plots_per_run / f'{exp}_{run}_V_{algo}.pdf')
                        plt.close()
                        roc_U[algo][run] = vu.make_roc(vu.get_combined(histos['onTracks'], '*U'),
                                                       vu.get_combined(histos['offTracks'], '*U'))
                        roc_V[algo][run] = vu.make_roc(vu.get_combined(histos['onTracks'], '*V'),
                                                       vu.get_combined(histos['offTracks'], '*V'))
                except AttributeError:
                    print(f'Skipping file algo {algo} exp {exp} run {run}')
                    continue
                # Free-up memory manually as I used `SetDirectory(0)`
                for t0 in ['eventT0', 'CDCeventT0', 'SVDeventT0']:
                    h = histos.pop(t0)
                    h.__python_owns__ = False
                    h.Delete()
 
                # absoluteShift is not a histogram, so just remove it from the dict without trying to delete
                histos.pop("absoluteShift", None)
 
                for histo_dict in histos.values():
                    for hh in histo_dict.values():
                        hh.__python_owns__ = False
                        hh.Delete()
                del histos
                for key, hh in CollectorHistograms[algo][exp][run].items():
                    if key not in ['hEventT0', 'hEventT0FromCDC', 'hEventT0FromSVD', 'hAbsoluteShiftValues']:
                        hh.__python_owns__ = False
                        hh.Delete()
                vu.progress(count + 1, total_item)
                count += 1
 
    print()
 
    if shift_detailed:
        for algo, KeyHisto in shift_histos.items():
            c2 = r.TCanvas("c2", "c2", 640, 480)
            outPDF = f"{output_dir}/shift_histograms_{algo}.pdf"
            c2.Print(outPDF + "[")
            onePad = r.TPad("onePad", "onePad", 0, 0, 1, 1)
            onePad.SetMargin(0.1, 0.2, 0.1, 0.1)
            onePad.SetNumber(1)
            onePad.Draw()
            onePad.cd()
            hShiftHisto = vu.get_shift_plot(shift_histos_merged_over_ladder[algo])
            hShiftHisto.Draw('COLZ')
            c2.Print(outPDF, "Title:" + hShiftHisto.GetName())
 
            c1 = r.TCanvas("c1", "c1", 640, 480)
            topPad = r.TPad("topPad", "topPad", 0, 0.5, 1, 1)
            btmPad = r.TPad("btmPad", "btmPad", 0, 0, 1, 0.5)
            topPad.SetMargin(0.1, 0.1, 0, 0.149)
            btmPad.SetMargin(0.1, 0.1, 0.303, 0)
            topPad.SetNumber(1)
            btmPad.SetNumber(2)
            topPad.Draw()
            btmPad.Draw()
            isOdd = True
            for key, hShift in KeyHisto.items():
                hShift.SetStats(0)
                for yn in range(hShift.GetNbinsY()):
                    norm = (hShift.ProjectionX("tmp", yn + 1, yn + 1, "")).GetMaximum()
                    if norm <= 0:
                        continue
                    for xn in range(hShift.GetNbinsX()):
                        hShift.SetBinContent(xn + 1, yn + 1, hShift.GetBinContent(xn + 1, yn + 1) / norm)
                if isOdd:
                    topPad.cd()
                    hShift.Draw("colz")
                else:
                    btmPad.cd()
                    hShift.Draw("colz")
                    c1.Print(outPDF, "Title:" + hShift.GetName())
                isOdd = not isOdd
            c1.Print(outPDF + "]")
 
    dd = {}
    runs = sorted(agreements[vu.time_algorithms[0]])
    dd['run'] = sum([[i]*len(vu.names_sides) for i in runs], [])
    dd['name'] = vu.names_sides*len(runs)
    dd['side'] = [i[-1] for i in dd['name']]
 
    for algo in vu.time_algorithms:
        dd[f'agreement_{algo}'] = [agreements[algo][run][side] for run, side in zip(dd['run'], dd['name'])]
        dd[f'precision_{algo}'] = [precisions[algo][run][side] for run, side in zip(dd['run'], dd['name'])]
        dd[f'discrimination_{algo}'] = [discriminations[algo][run][side] for run, side in zip(dd['run'], dd['name'])]
        dd[f'shift_agreement_{algo}'] = [shift_agreements[algo][run][side] for run, side in zip(dd['run'], dd['name'])]
        dd[f'entries_onTracks_{algo}'] = [entries_onTracks[algo][run][side] for run, side in zip(dd['run'], dd['name'])]
        dd[f'entries_eventT0_{algo}'] = [entries_eventT0[algo][run] for run, side in zip(dd['run'], dd['name'])]
        dd[f'T0_agreement_{algo}'] = [t0_agreements[algo][run] for run, side in zip(dd['run'], dd['name'])]
        dd[f'T0_difference_{algo}'] = [t0_differences[algo][run] for run, side in zip(dd['run'], dd['name'])]
 
    # Make ROC plots
    for run in runs:
        plt.figure()
        plt.plot(*roc_U['CoG6'][run], 'k-', label='CoG6 U')
        plt.plot(*roc_V['CoG6'][run], 'k:', label='CoG6 V')
        plt.plot(*roc_U['CoG3'][run], 'b-', label='CoG3 U')
        plt.plot(*roc_V['CoG3'][run], 'b:', label='CoG3 V')
        plt.plot(*roc_U['ELS3'][run], 'r-', label='ELS3 U')
        plt.plot(*roc_V['ELS3'][run], 'r:', label='ELS3 V')
        plt.legend(loc='lower left')
        plt.xlabel('sgn efficiency')
        plt.ylabel('bkg rejection')
        plt.title(f'ROC run {run}')
        plt.xlim((0, 1))
        plt.ylim((0, 1))
        plt.tight_layout()
        plt.savefig(plots_per_run / f'ROC_{run}.pdf')
        plt.close()
 
    df = pd.DataFrame(dd)
    df.to_pickle(output_dir / 'df.pkl')
    # df = pd.read_pickle('df.pkl')
 
    absolute_shifts_dict = {}
    absolute_shifts_dict['run'] = sum([[i]*len(vu.names_layer_sides) for i in runs], [])  # 4 layers * 2 sides
    absolute_shifts_dict['name'] = vu.names_layer_sides*len(runs)
    for algo in vu.time_algorithms:
        absolute_shifts_dict[f'absolute_shift_values_{algo}'] = [
            absolute_shifts_values[algo][run][layer_side] for run, layer_side in zip(
                absolute_shifts_dict['run'], absolute_shifts_dict['name'])]
 
    absolute_shifts_df = pd.DataFrame(absolute_shifts_dict)
 
    print('Making combined plots')
 
    for algo in vu.time_algorithms:
        plt.figure(figsize=(6.4*max(2, total_length/30), 4.8*2))
        ax = sns.violinplot(x='run', y=f'agreement_{algo}', hue='side', data=df, split=True)
        ax.set_ylim([-2, 2])
        ax.xaxis.set_minor_locator(ticker.NullLocator())
        plt.axhline(0, color='black', linestyle='--')
        plt.axhline(0.5, color='black', linestyle=':')
        plt.axhline(-0.5, color='black', linestyle=':')
        plt.setp(ax.get_xticklabels(), rotation=90)
        plt.tight_layout()
        plt.savefig(output_dir / f'agreement_{algo}.pdf')
        plt.close()
 
        plt.figure(figsize=(6.4*max(2, total_length/30), 4.8*2))
        ax = sns.violinplot(x='run', y=f'precision_{algo}', hue='side', data=df, split=True)
        ax.set_ylim([0, 50])
        ax.xaxis.set_minor_locator(ticker.NullLocator())
        plt.axhline(10, color='black', linestyle=':')
        plt.axhline(20, color='black', linestyle=':')
        plt.setp(ax.get_xticklabels(), rotation=90)
        plt.tight_layout()
        plt.savefig(output_dir / f'precision_{algo}.pdf')
        plt.close()
 
        plt.figure(figsize=(6.4*max(2, total_length/30), 4.8*2))
        ax = sns.violinplot(x='run', y=f'discrimination_{algo}', hue='side', data=df, split=True)
        ax.set_ylim([0.5, 1])
        ax.xaxis.set_minor_locator(ticker.NullLocator())
        plt.axhline(0.8, color='black', linestyle=':')
        plt.axhline(0.9, color='black', linestyle=':')
        plt.setp(ax.get_xticklabels(), rotation=90)
        plt.tight_layout()
        plt.savefig(output_dir / f'discrimination_{algo}.pdf')
        plt.close()
 
        plt.figure(figsize=(6.4*max(2, total_length/30), 4.8*2))
        ax = sns.violinplot(x='run', y=f'shift_agreement_{algo}', hue='side', data=df, split=True, cut=0)
        ax.xaxis.set_minor_locator(ticker.NullLocator())
        ax.set_ylim([0.0, 3.5])
        plt.axhline(0, color='black', linestyle='--')
        plt.axhline(0.5, color='black', linestyle=':')
        plt.axhline(1.0, color='black', linestyle=':')
        plt.axhline(2.0, color='black', linestyle=':')
        plt.setp(ax.get_xticklabels(), rotation=90)
        plt.tight_layout()
        plt.savefig(output_dir / f'shift_agreement_{algo}.pdf')
        plt.close()
 
        plt.figure(figsize=(6.4*max(2, total_length/30), 4.8*2))
        ax = sns.violinplot(x='run', y=f'entries_onTracks_{algo}', hue='side', data=df, split=True, cut=0)
        ax.xaxis.set_minor_locator(ticker.NullLocator())
        plt.setp(ax.get_xticklabels(), rotation=90)
        plt.tight_layout()
        plt.savefig(output_dir / f'entries_onTracks_{algo}.pdf')
        plt.close()
 
        plt.figure(figsize=(6.4*max(2, total_length/30), 4.8*2))
        ax = sns.violinplot(x='run', y=f'entries_eventT0_{algo}', hue='side', data=df, split=True)
        ax.xaxis.set_minor_locator(ticker.NullLocator())
        plt.setp(ax.get_xticklabels(), rotation=90)
        plt.tight_layout()
        plt.savefig(output_dir / f'entries_eventT0_{algo}.pdf')
        plt.close()
 
        for metric in ['agreement', 'difference']:
            plt.figure(figsize=(6.4*max(2, total_length/30), 4.8*2))
            ax = sns.violinplot(x='run', y=f'T0_{metric}_{algo}', data=df, split=True)
            ax.set_ylim([-2, 2])
            ax.xaxis.set_minor_locator(ticker.NullLocator())
            plt.axhline(0, color='black', linestyle='--')
            plt.axhline(0.5, color='black', linestyle=':')
            plt.axhline(-0.5, color='black', linestyle=':')
            plt.setp(ax.get_xticklabels(), rotation=90)
            plt.tight_layout()
            plt.savefig(output_dir / f'T0_{metric}_{algo}.pdf')
            plt.close()
 
        plt.figure(figsize=(6.4*max(2, total_length/30), 4.8*2))
        ax = sns.scatterplot(
            x='run', y=f'absolute_shift_values_{algo}',
            hue='name', data=absolute_shifts_df,
            marker='o', s=40,
        )
        ax.set_ylim([-5, 5])
        run_values = sorted(absolute_shifts_df['run'].unique())
        ax.set_xticks(run_values)
        ax.set_xticklabels([str(r) for r in run_values])
        ax.xaxis.set_minor_locator(ticker.NullLocator())
        plt.axhline(0, color='black', linestyle='--')
        plt.axhline(2, color='black', linestyle=':')
        plt.axhline(-2, color='black', linestyle=':')
        plt.setp(ax.get_xticklabels(), rotation=90)
        plt.ylabel(f'absolute shift ({algo}) (ns)')
        ax.legend(title='layer/side', ncol=2)
        plt.tight_layout()
        plt.savefig(output_dir / f'absolute_shift_{algo}.pdf')
        plt.close()
 
 
if __name__ == '__main__':
 
    import argparse
    parser = argparse.ArgumentParser(description=__doc__,
                                     formatter_class=argparse.RawTextHelpFormatter)
 
    # b2val-prompt-run wants to pass to the script also input_data_path
    # and requested_iov. As they are not required by this validation I just accept
    # them together with calibration_results_dir and then ignore them
    parser.add_argument('calibration_results_dir',
                        help='The directory that contains the collector outputs',
                        nargs='+')
 
    parser.add_argument('-o', '--output_dir',
                        help='The directory where all the output will be saved',
                        default='SVDTimeValidation_output')
    parser.add_argument('-l',
                        help='Make additional pdf with details cluster size vs shift',
                        action='store_true')
    args = parser.parse_args()
 
    run_validation(args.calibration_results_dir[0], output_dir=args.output_dir, shift_detailed=args.l)