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@@ -3,7 +3,7 @@ import numpy as np
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import matplotlib.pyplot as plt
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from filval.result_set import ResultSet
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-from filval.histogram import hist, hist_add, hist_norm, hist_scale, hist2d
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+from filval.histogram import hist, hist_integral, hist2d, hist2d_percent_contour
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from filval.plotting import (decl_plot, render_plots, hist_plot, hist2d_plot,
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Plot, generate_dashboard, simple_plot)
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@@ -14,58 +14,151 @@ def center_text(x, y, txt, **kwargs):
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transform=plt.gca().transAxes, **kwargs)
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+def hist_integral_ratio(num, den):
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+ num_int = hist_integral(num, times_bin_width=False)
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+ den_int = hist_integral(den, times_bin_width=False)
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+
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+ ratio = num_int / den_int
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+ error = np.sqrt(den_int) / den_int # TODO: Check this definition of error
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+ return ratio, error
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+
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@decl_plot
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-def plot_residuals(rs, layer, hit, variable, subdet, plot_cuts=True):
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- matching_cuts = [
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- dict(
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- dPhiMaxHighEt=0.05,
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- dPhiMaxHighEtThres=20.0,
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- dPhiMaxLowEtGrad=-0.002,
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- dRzMaxHighEt=9999.0,
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- dRzMaxHighEtThres=0.0,
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- dRzMaxLowEtGrad=0.0,
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- ),
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- dict(
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- dPhiMaxHighEt=0.003,
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- dPhiMaxHighEtThres=0.0,
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- dPhiMaxLowEtGrad=0.0,
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- dRzMaxHighEt=0.05,
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- dRzMaxHighEtThres=30.0,
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- dRzMaxLowEtGrad=-0.002,
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- ),
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- dict(
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- dPhiMaxHighEt=0.003,
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- dPhiMaxHighEtThres=0.0,
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- dPhiMaxLowEtGrad=0.0,
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- dRzMaxHighEt=0.05,
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- dRzMaxHighEtThres=30.0,
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- dRzMaxLowEtGrad=-0.002,
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- )
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- ]
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+def plot_residuals(rs, layer, hit, variable, subdet, plot_cuts=True, cut_sel='tight'):
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+ matching_cuts = {
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+ 'extra-narrow': [
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+ dict(
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+ dPhiMaxHighEt=0.025,
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+ dPhiMaxHighEtThres=20.0,
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+ dPhiMaxLowEtGrad=-0.002,
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+ dRzMaxHighEt=9999.0,
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+ dRzMaxHighEtThres=0.0,
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+ dRzMaxLowEtGrad=0.0,
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+ ),
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+ dict(
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+ dPhiMaxHighEt=0.0015,
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+ dPhiMaxHighEtThres=0.0,
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+ dPhiMaxLowEtGrad=0.0,
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+ dRzMaxHighEt=0.025,
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+ dRzMaxHighEtThres=30.0,
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+ dRzMaxLowEtGrad=-0.002,
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+ ),
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+ dict(
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+ dPhiMaxHighEt=0.0015,
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+ dPhiMaxHighEtThres=0.0,
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+ dPhiMaxLowEtGrad=0.0,
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+ dRzMaxHighEt=0.025,
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+ dRzMaxHighEtThres=30.0,
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+ dRzMaxLowEtGrad=-0.002,
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+ )
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+ ],
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+ 'narrow': [
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+ dict(
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+ dPhiMaxHighEt=0.05,
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+ dPhiMaxHighEtThres=20.0,
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+ dPhiMaxLowEtGrad=-0.002,
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+ dRzMaxHighEt=9999.0,
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+ dRzMaxHighEtThres=0.0,
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+ dRzMaxLowEtGrad=0.0,
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+ ),
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+ dict(
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+ dPhiMaxHighEt=0.003,
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+ dPhiMaxHighEtThres=0.0,
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+ dPhiMaxLowEtGrad=0.0,
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+ dRzMaxHighEt=0.05,
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+ dRzMaxHighEtThres=30.0,
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+ dRzMaxLowEtGrad=-0.002,
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+ ),
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+ dict(
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+ dPhiMaxHighEt=0.003,
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+ dPhiMaxHighEtThres=0.0,
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+ dPhiMaxLowEtGrad=0.0,
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+ dRzMaxHighEt=0.05,
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+ dRzMaxHighEtThres=30.0,
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+ dRzMaxLowEtGrad=-0.002,
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+ )
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+ ],
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+ 'wide': [
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+ dict(
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+ dPhiMaxHighEt=0.10,
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+ dPhiMaxHighEtThres=20.0,
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+ dPhiMaxLowEtGrad=-0.002,
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+ dRzMaxHighEt=9999.0,
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+ dRzMaxHighEtThres=0.0,
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+ dRzMaxLowEtGrad=0.0,
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+ ),
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+ dict(
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+ dPhiMaxHighEt=0.006,
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+ dPhiMaxHighEtThres=0.0,
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+ dPhiMaxLowEtGrad=0.0,
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+ dRzMaxHighEt=0.10,
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+ dRzMaxHighEtThres=30.0,
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+ dRzMaxLowEtGrad=-0.002,
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+ ),
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+ dict(
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+ dPhiMaxHighEt=0.006,
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+ dPhiMaxHighEtThres=0.0,
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+ dPhiMaxLowEtGrad=0.0,
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+ dRzMaxHighEt=0.10,
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+ dRzMaxHighEtThres=30.0,
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+ dRzMaxLowEtGrad=-0.002,
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+ )
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+ ],
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+ 'extra-wide': [
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+ dict(
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+ dPhiMaxHighEt=0.15,
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+ dPhiMaxHighEtThres=20.0,
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+ dPhiMaxLowEtGrad=-0.002,
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+ dRzMaxHighEt=9999.0,
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+ dRzMaxHighEtThres=0.0,
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+ dRzMaxLowEtGrad=0.0,
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+ ),
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+ dict(
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+ dPhiMaxHighEt=0.009,
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+ dPhiMaxHighEtThres=0.0,
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+ dPhiMaxLowEtGrad=0.0,
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+ dRzMaxHighEt=0.15,
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+ dRzMaxHighEtThres=30.0,
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+ dRzMaxLowEtGrad=-0.002,
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+ ),
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+ dict(
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+ dPhiMaxHighEt=0.009,
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+ dPhiMaxHighEtThres=0.0,
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+ dPhiMaxLowEtGrad=0.0,
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+ dRzMaxHighEt=0.15,
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+ dRzMaxHighEtThres=30.0,
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+ dRzMaxLowEtGrad=-0.002,
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+ )
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+ ]
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+ }
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h = hist2d(getattr(rs, f'{variable}_{subdet}_L{layer}_H{hit}_v_Et'))
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def calc_window(et):
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idx = min(hit-1, 2)
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- high_et = matching_cuts[idx][f'{variable}MaxHighEt']
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- high_et_thres = matching_cuts[idx][f'{variable}MaxHighEtThres']
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- low_et_grad = matching_cuts[idx][f'{variable}MaxLowEtGrad']
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+ cuts = matching_cuts[cut_sel]
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+ high_et = cuts[idx][f'{variable}MaxHighEt']
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+ high_et_thres = cuts[idx][f'{variable}MaxHighEtThres']
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+ low_et_grad = cuts[idx][f'{variable}MaxLowEtGrad']
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return high_et + min(0, et-high_et_thres)*low_et_grad
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hist2d_plot(h, colorbar=True)
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+ xs, ys = hist2d_percent_contour(h, .90, 'x')
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+ plt.plot(xs, ys, color='green', label='90\% contour')
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+ xs, ys = hist2d_percent_contour(h, .995, 'x')
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+ plt.plot(xs, ys, color='darkgreen', label='99.5\% contour')
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+
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if plot_cuts:
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ets = h[3][:, 0]
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cuts = [calc_window(et) for et in ets]
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plt.plot(cuts, ets, color='red', label='Cut Value')
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- plt.legend()
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+ plt.legend(loc='upper right')
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plt.xlabel({'dPhi': r'$\delta \phi$ (rads)',
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'dRz': r'$\delta R/z$ (cm)'}[variable])
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plt.ylabel('$E_T$ (GeV)')
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-
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@decl_plot
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def plot_seed_eff(rs):
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r"""## ECAL-Driven Seeding Efficiency
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@@ -107,6 +200,7 @@ def plot_tracking_eff(rs):
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ax_pt = plt.subplot(221)
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ax_eta = plt.subplot(222)
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ax_phi = plt.subplot(223)
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+ ax_eta_pt = plt.subplot(224)
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errors = True
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plt.sca(ax_pt)
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@@ -127,6 +221,12 @@ def plot_tracking_eff(rs):
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plt.xlabel(r"Sim-Track $\phi$")
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plt.ylim((0, 1.1))
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+ plt.sca(ax_eta_pt)
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+ hist2d_plot(hist2d(rs.tracking_eff_v_eta_pt))
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+ plt.xlabel(r"Sim-Track $\eta$")
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+ plt.ylabel(r"Sim-Track $p_T$")
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+ plt.colorbar()
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+
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@decl_plot
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def plot_seed_purity(rs, ext=""):
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@@ -184,22 +284,19 @@ def plot_track_purity(rs, ext=""):
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hist_plot(get_hist("tracking_pur_v_pt"), include_errors=errors)
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center_text(0.5, 0.3, r'$|\eta|<2.4$')
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plt.xlabel(r"GSF-Track $p_T$")
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- if not ext:
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- plt.ylim((0, 1.1))
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+ plt.ylim((0, 1.1))
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plt.sca(ax_eta)
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hist_plot(get_hist("tracking_pur_v_eta"), include_errors=errors)
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center_text(0.5, 0.3, r'$p_T>20$')
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plt.xlabel(r"GSF-Track $\eta$")
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- if not ext:
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- plt.ylim((0, 1.1))
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+ plt.ylim((0, 1.1))
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plt.sca(ax_phi)
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hist_plot(get_hist("tracking_pur_v_phi"), include_errors=errors)
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center_text(0.5, 0.3, r'$p_T>20$ and $|\eta|<2.4$')
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plt.xlabel(r"GSF-Track $\phi$")
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- if not ext:
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- plt.ylim((0, 1.1))
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+ plt.ylim((0, 1.1))
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@decl_plot
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@@ -212,50 +309,44 @@ def plot_hit_vs_layer(rs, region):
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plt.ylabel('Hit \#')
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-if __name__ == '__main__':
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- rs = ResultSet('seeds', '../hists/new_seeding.root')
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+def single_cut_plots(cut_sel):
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+ rs = ResultSet(f'{cut_sel}-window', f'../hists/{cut_sel}-window.root')
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- # Next, declare all of the (sub)plots that will be assembled into full
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- # figures later
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- seed_eff = plot_seed_eff, (rs, )
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+ seed_eff = plot_seed_eff, (rs,)
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tracking_eff = plot_tracking_eff, (rs,)
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- seed_pur = plot_seed_purity, rs
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- seed_pur_num = plot_seed_purity, (rs,), {'ext': '_num'}
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- seed_pur_den = plot_seed_purity, (rs,), {'ext': '_den'}
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+ seed_pur = plot_seed_purity, (rs,)
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track_pur = plot_track_purity, (rs,)
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- track_pur_num = plot_track_purity, (rs,), {'ext': '_num'}
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- track_pur_den = plot_track_purity, (rs,), {'ext': '_den'}
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+ track_pur_seed_match = plot_track_purity, (rs,), dict(ext='2')
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- BPIX_residuals_L1_H1_dPhi = plot_residuals, (rs, 1, 1, 'dPhi', 'BPIX')
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- BPIX_residuals_L2_H2_dPhi = plot_residuals, (rs, 2, 2, 'dPhi', 'BPIX')
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- BPIX_residuals_L3_H3_dPhi = plot_residuals, (rs, 3, 3, 'dPhi', 'BPIX')
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+ BPIX_residuals_L1_H1_dPhi = plot_residuals, (rs, 1, 1, 'dPhi', 'BPIX'), dict(cut_sel=cut_sel)
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+ BPIX_residuals_L2_H2_dPhi = plot_residuals, (rs, 2, 2, 'dPhi', 'BPIX'), dict(cut_sel=cut_sel)
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+ BPIX_residuals_L3_H3_dPhi = plot_residuals, (rs, 3, 3, 'dPhi', 'BPIX'), dict(cut_sel=cut_sel)
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BPIX_residuals_L1_H1_dRz = plot_residuals, (rs, 1, 1, 'dRz', 'BPIX'), {'plot_cuts': False}
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- BPIX_residuals_L2_H2_dRz = plot_residuals, (rs, 2, 2, 'dRz', 'BPIX')
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- BPIX_residuals_L3_H3_dRz = plot_residuals, (rs, 3, 3, 'dRz', 'BPIX')
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+ BPIX_residuals_L2_H2_dRz = plot_residuals, (rs, 2, 2, 'dRz', 'BPIX'), dict(cut_sel=cut_sel)
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+ BPIX_residuals_L3_H3_dRz = plot_residuals, (rs, 3, 3, 'dRz', 'BPIX'), dict(cut_sel=cut_sel)
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- FPIX_residuals_L1_H1_dPhi = plot_residuals, (rs, 1, 1, 'dPhi', 'FPIX')
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- FPIX_residuals_L2_H2_dPhi = plot_residuals, (rs, 2, 2, 'dPhi', 'FPIX')
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- FPIX_residuals_L3_H3_dPhi = plot_residuals, (rs, 3, 3, 'dPhi', 'FPIX')
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+ FPIX_residuals_L1_H1_dPhi = plot_residuals, (rs, 1, 1, 'dPhi', 'FPIX'), dict(cut_sel=cut_sel)
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+ FPIX_residuals_L2_H2_dPhi = plot_residuals, (rs, 2, 2, 'dPhi', 'FPIX'), dict(cut_sel=cut_sel)
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+ FPIX_residuals_L3_H3_dPhi = plot_residuals, (rs, 3, 3, 'dPhi', 'FPIX'), dict(cut_sel=cut_sel)
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FPIX_residuals_L1_H1_dRz = plot_residuals, (rs, 1, 1, 'dRz', 'FPIX'), {'plot_cuts': False}
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- FPIX_residuals_L2_H2_dRz = plot_residuals, (rs, 2, 2, 'dRz', 'FPIX')
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- FPIX_residuals_L3_H3_dRz = plot_residuals, (rs, 3, 3, 'dRz', 'FPIX')
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+ FPIX_residuals_L2_H2_dRz = plot_residuals, (rs, 2, 2, 'dRz', 'FPIX'), dict(cut_sel=cut_sel)
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+ FPIX_residuals_L3_H3_dRz = plot_residuals, (rs, 3, 3, 'dRz', 'FPIX'), dict(cut_sel=cut_sel)
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- FPIX_residuals_L1_H2_dPhi = plot_residuals, (rs, 1, 2, 'dPhi', 'FPIX')
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- FPIX_residuals_L1_H3_dPhi = plot_residuals, (rs, 1, 3, 'dPhi', 'FPIX')
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- FPIX_residuals_L2_H3_dPhi = plot_residuals, (rs, 2, 3, 'dPhi', 'FPIX')
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+ FPIX_residuals_L1_H2_dPhi = plot_residuals, (rs, 1, 2, 'dPhi', 'FPIX'), dict(cut_sel=cut_sel)
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+ FPIX_residuals_L1_H3_dPhi = plot_residuals, (rs, 1, 3, 'dPhi', 'FPIX'), dict(cut_sel=cut_sel)
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+ FPIX_residuals_L2_H3_dPhi = plot_residuals, (rs, 2, 3, 'dPhi', 'FPIX'), dict(cut_sel=cut_sel)
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- FPIX_residuals_L1_H2_dRz = plot_residuals, (rs, 1, 2, 'dPhi', 'FPIX')
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- FPIX_residuals_L1_H3_dRz = plot_residuals, (rs, 1, 3, 'dPhi', 'FPIX')
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- FPIX_residuals_L2_H3_dRz = plot_residuals, (rs, 2, 3, 'dPhi', 'FPIX')
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+ FPIX_residuals_L1_H2_dRz = plot_residuals, (rs, 1, 2, 'dPhi', 'FPIX'), dict(cut_sel=cut_sel)
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+ FPIX_residuals_L1_H3_dRz = plot_residuals, (rs, 1, 3, 'dPhi', 'FPIX'), dict(cut_sel=cut_sel)
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+ FPIX_residuals_L2_H3_dRz = plot_residuals, (rs, 2, 3, 'dPhi', 'FPIX'), dict(cut_sel=cut_sel)
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hit_vs_layer_barrel = plot_hit_vs_layer, (rs, 'barrel')
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hit_vs_layer_forward = plot_hit_vs_layer, (rs, 'forward')
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- # Now assemble the plots into figures.
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plots = [
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Plot(BPIX_residuals_L1_H1_dPhi, 'Phi Residuals Layer 1 Hit 1 - BPIX'),
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Plot(BPIX_residuals_L2_H2_dPhi, 'Phi Residuals Layer 2 Hit 2 - BPIX'),
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@@ -282,15 +373,228 @@ if __name__ == '__main__':
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Plot(tracking_eff, 'GSF Tracking Efficiency'),
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Plot(hit_vs_layer_barrel, 'Hit vs Layer - Barrel'),
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Plot(hit_vs_layer_forward, 'Hit vs Layer - Forward'),
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+ Plot(seed_pur, 'ECAL-Driven Seeding Purity'),
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Plot(track_pur, 'GSF Track Purity'),
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- Plot(track_pur_num, 'GSF Track Purity Numerator'),
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- Plot(track_pur_den, 'GSF Track Purity Denominator'),
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+ Plot(track_pur_seed_match , 'GSF Track Purity (Seed Truth Match)'),
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+ simple_plot(rs.gsf_tracks_nmatch_sim_tracks, log='y'),
|
|
|
]
|
|
|
|
|
|
- # Finally, render and save the plots and generate the html+bootstrap
|
|
|
- # dashboard to view them
|
|
|
render_plots(plots, to_disk=False)
|
|
|
generate_dashboard(plots, 'Seeding Efficiency',
|
|
|
- output='eff_plots.html',
|
|
|
+ output=f'{rs.sample_name}.html',
|
|
|
source=__file__,
|
|
|
config=rs.config)
|
|
|
+
|
|
|
+
|
|
|
+@decl_plot
|
|
|
+def plot_seed_roc_curve(rss):
|
|
|
+ def get_num_den(rs, basename):
|
|
|
+ num = hist(getattr(rs, f'{basename}_num'))
|
|
|
+ den = hist(getattr(rs, f'{basename}_den'))
|
|
|
+ return hist_integral_ratio(num, den)
|
|
|
+ for rs in rss:
|
|
|
+ eff, eff_err = get_num_den(rs, 'seed_eff_v_phi')
|
|
|
+ pur, pur_err = get_num_den(rs, 'seed_pur_v_phi')
|
|
|
+ plt.errorbar([pur], [eff], xerr=[pur_err], yerr=[eff_err], label=rs.sample_name)
|
|
|
+ center_text(0.5, 0.3, r'$p_T>20$ and $|\eta|<2.4$')
|
|
|
+ plt.axis('equal')
|
|
|
+ plt.xlim((0.8, 1.0))
|
|
|
+ plt.ylim((0.8, 1.0))
|
|
|
+ plt.xlabel('ECAL-Driven Seeding Purity')
|
|
|
+ plt.ylabel('ECAL-Driven Seeding Efficiency')
|
|
|
+ plt.grid()
|
|
|
+ plt.legend()
|
|
|
+
|
|
|
+
|
|
|
+@decl_plot
|
|
|
+def plot_seed_eff_all(rss):
|
|
|
+ ax_pt = plt.subplot(221)
|
|
|
+ ax_eta = plt.subplot(222)
|
|
|
+ ax_phi = plt.subplot(223)
|
|
|
+ errors = True
|
|
|
+ for rs in rss:
|
|
|
+ plt.sca(ax_pt)
|
|
|
+ hist_plot(hist(rs.seed_eff_v_pt), include_errors=errors, label=rs.sample_name)
|
|
|
+ plt.sca(ax_eta)
|
|
|
+ hist_plot(hist(rs.seed_eff_v_eta), include_errors=errors, label=rs.sample_name)
|
|
|
+ plt.sca(ax_phi)
|
|
|
+ hist_plot(hist(rs.seed_eff_v_phi), include_errors=errors, label=rs.sample_name)
|
|
|
+
|
|
|
+ plt.sca(ax_pt)
|
|
|
+ center_text(0.5, 0.3, r'$|\eta|<2.4$')
|
|
|
+ plt.xlabel(r"Sim-Track $p_T$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+
|
|
|
+ plt.sca(ax_eta)
|
|
|
+ center_text(0.5, 0.3, r'$p_T>20$')
|
|
|
+ plt.xlabel(r"Sim-Track $\eta$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+ plt.legend(loc='lower right')
|
|
|
+
|
|
|
+ plt.sca(ax_phi)
|
|
|
+ center_text(0.5, 0.3, r'$p_T>20$ and $|\eta|<2.4$')
|
|
|
+ plt.xlabel(r"Sim-Track $\phi$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+
|
|
|
+
|
|
|
+@decl_plot
|
|
|
+def plot_seed_pur_all(rss):
|
|
|
+ ax_pt = plt.subplot(221)
|
|
|
+ ax_eta = plt.subplot(222)
|
|
|
+ ax_phi = plt.subplot(223)
|
|
|
+ errors = True
|
|
|
+ for rs in rss:
|
|
|
+ plt.sca(ax_pt)
|
|
|
+ hist_plot(hist(rs.seed_pur_v_pt), include_errors=errors, label=rs.sample_name)
|
|
|
+ plt.sca(ax_eta)
|
|
|
+ hist_plot(hist(rs.seed_pur_v_eta), include_errors=errors, label=rs.sample_name)
|
|
|
+ plt.sca(ax_phi)
|
|
|
+ hist_plot(hist(rs.seed_pur_v_phi), include_errors=errors, label=rs.sample_name)
|
|
|
+
|
|
|
+ plt.sca(ax_pt)
|
|
|
+ center_text(0.5, 0.3, r'$|\eta|<2.4$')
|
|
|
+ plt.xlabel(r"Seed $p_T$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+
|
|
|
+ plt.sca(ax_eta)
|
|
|
+ center_text(0.5, 0.3, r'$p_T>20$')
|
|
|
+ plt.xlabel(r"Seed $\eta$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+ plt.legend(loc='lower right')
|
|
|
+
|
|
|
+ plt.sca(ax_phi)
|
|
|
+ center_text(0.5, 0.3, r'$p_T>20$ and $|\eta|<2.4$')
|
|
|
+ plt.xlabel(r"Seed $\phi$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+
|
|
|
+
|
|
|
+@decl_plot
|
|
|
+def plot_tracking_roc_curve(rss, ext=''):
|
|
|
+ def get_num_den(rs, basename):
|
|
|
+ num = hist(getattr(rs, f'{basename}{ext}_num'))
|
|
|
+ den = hist(getattr(rs, f'{basename}{ext}_den'))
|
|
|
+ return hist_integral_ratio(num, den)
|
|
|
+ for rs in rss:
|
|
|
+ eff, eff_err = get_num_den(rs, 'tracking_eff_v_phi')
|
|
|
+ pur, pur_err = get_num_den(rs, 'tracking_pur_v_phi')
|
|
|
+ plt.errorbar([pur], [eff], xerr=[pur_err], yerr=[eff_err], label=rs.sample_name)
|
|
|
+ center_text(0.5, 0.3, r'$p_T>20$ and $|\eta|<2.4$')
|
|
|
+ plt.axis('equal')
|
|
|
+ plt.xlim((0.8, 1.0))
|
|
|
+ plt.ylim((0.8, 1.0))
|
|
|
+ plt.xlabel('GSF-Track Purity')
|
|
|
+ plt.ylabel('GSF-Track Efficiency')
|
|
|
+ plt.grid()
|
|
|
+ plt.legend()
|
|
|
+
|
|
|
+
|
|
|
+@decl_plot
|
|
|
+def plot_tracking_eff_all(rss, ext=''):
|
|
|
+ ax_pt = plt.subplot(221)
|
|
|
+ ax_eta = plt.subplot(222)
|
|
|
+ ax_phi = plt.subplot(223)
|
|
|
+ errors = True
|
|
|
+ for rs in rss:
|
|
|
+ plt.sca(ax_pt)
|
|
|
+ hist_plot(hist(getattr(rs, f'tracking_eff_v_pt{ext}')), include_errors=errors, label=rs.sample_name)
|
|
|
+ plt.sca(ax_eta)
|
|
|
+ hist_plot(hist(getattr(rs, f'tracking_eff_v_eta{ext}')), include_errors=errors, label=rs.sample_name)
|
|
|
+ plt.sca(ax_phi)
|
|
|
+ hist_plot(hist(getattr(rs, f'tracking_eff_v_phi{ext}')), include_errors=errors, label=rs.sample_name)
|
|
|
+
|
|
|
+ plt.sca(ax_pt)
|
|
|
+ center_text(0.5, 0.3, r'$|\eta|<2.4$')
|
|
|
+ plt.xlabel(r"Sim-Track $p_T$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+
|
|
|
+ plt.sca(ax_eta)
|
|
|
+ center_text(0.5, 0.3, r'$p_T>20$')
|
|
|
+ plt.xlabel(r"Sim-Track $\eta$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+ plt.legend(loc='lower right')
|
|
|
+
|
|
|
+ plt.sca(ax_phi)
|
|
|
+ center_text(0.5, 0.3, r'$p_T>20$ and $|\eta|<2.4$')
|
|
|
+ plt.xlabel(r"Sim-Track $\phi$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+
|
|
|
+
|
|
|
+@decl_plot
|
|
|
+def plot_tracking_pur_all(rss, ext=''):
|
|
|
+ ax_pt = plt.subplot(221)
|
|
|
+ ax_eta = plt.subplot(222)
|
|
|
+ ax_phi = plt.subplot(223)
|
|
|
+ errors = True
|
|
|
+ for rs in rss:
|
|
|
+ plt.sca(ax_pt)
|
|
|
+ hist_plot(hist(getattr(rs, f'tracking_pur_v_pt{ext}')), include_errors=errors, label=rs.sample_name)
|
|
|
+ plt.sca(ax_eta)
|
|
|
+ hist_plot(hist(getattr(rs, f'tracking_pur_v_eta{ext}')), include_errors=errors, label=rs.sample_name)
|
|
|
+ plt.sca(ax_phi)
|
|
|
+ hist_plot(hist(getattr(rs, f'tracking_pur_v_phi{ext}')), include_errors=errors, label=rs.sample_name)
|
|
|
+
|
|
|
+ plt.sca(ax_pt)
|
|
|
+ center_text(0.5, 0.3, r'$|\eta|<2.4$')
|
|
|
+ plt.xlabel(r"GSF-Track $p_T$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+
|
|
|
+ plt.sca(ax_eta)
|
|
|
+ center_text(0.5, 0.3, r'$p_T>20$')
|
|
|
+ plt.xlabel(r"GSF-Track $\eta$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+ plt.legend(loc='lower right')
|
|
|
+
|
|
|
+ plt.sca(ax_phi)
|
|
|
+ center_text(0.5, 0.3, r'$p_T>20$ and $|\eta|<2.4$')
|
|
|
+ plt.xlabel(r"GSF-Track $\phi$")
|
|
|
+ plt.ylim((0, 1.1))
|
|
|
+
|
|
|
+
|
|
|
+@decl_plot
|
|
|
+def plot_ecal_rel_res(rss):
|
|
|
+ for rs in rss:
|
|
|
+ hist_plot(hist(rs.ecal_energy_resolution), label=rs.sample_name)
|
|
|
+ plt.xlabel(r"ECAL $E_T$ relative error")
|
|
|
+ plt.legend()
|
|
|
+
|
|
|
+def all_cut_plots(cuts):
|
|
|
+ rss = [ResultSet(f'{cut_sel}-window', f'../hists/{cut_sel}-window.root') for cut_sel in cuts]
|
|
|
+
|
|
|
+ tracking_roc_curve = plot_tracking_roc_curve, (rss,)
|
|
|
+ tracking_eff_all = plot_tracking_eff_all, (rss,)
|
|
|
+ tracking_pur_all = plot_tracking_pur_all, (rss,)
|
|
|
+ tracking_roc_curve2 = plot_tracking_roc_curve, (rss, '2')
|
|
|
+ tracking_eff_all2 = plot_tracking_eff_all, (rss, '2')
|
|
|
+ tracking_pur_all2 = plot_tracking_pur_all, (rss, '2')
|
|
|
+
|
|
|
+ seed_roc_curve = plot_seed_roc_curve, (rss,)
|
|
|
+ seed_eff_all = plot_seed_eff_all, (rss,)
|
|
|
+ seed_pur_all = plot_seed_pur_all, (rss,)
|
|
|
+
|
|
|
+ ecal_rel_res = plot_ecal_rel_res, (rss,)
|
|
|
+
|
|
|
+ plots = [
|
|
|
+ Plot(tracking_roc_curve, 'Tracking ROC Curve'),
|
|
|
+ Plot(tracking_eff_all, 'Tracking Efficiency'),
|
|
|
+ Plot(tracking_pur_all, 'Tracking Purity'),
|
|
|
+ Plot(tracking_roc_curve2, 'Tracking ROC Curve (Seed Matched)'),
|
|
|
+ Plot(tracking_eff_all2, 'Tracking Efficiency (Seed Matched)'),
|
|
|
+ Plot(tracking_pur_all2, 'Tracking Purity (Seed Matched)'),
|
|
|
+ Plot(seed_roc_curve, 'Seeding ROC Curve'),
|
|
|
+ Plot(seed_eff_all, 'ECAL-Driven Seeding Efficiency'),
|
|
|
+ Plot(seed_pur_all, 'ECAL-Driven Seeding Purity'),
|
|
|
+ Plot(ecal_rel_res, 'ECAL ET Relative Resolution'),
|
|
|
+ ]
|
|
|
+
|
|
|
+ render_plots(plots, to_disk=False)
|
|
|
+ generate_dashboard(plots, 'Comparisons',
|
|
|
+ output='comparisons.html',
|
|
|
+ source=__file__,
|
|
|
+ config=rss[0].config)
|
|
|
+
|
|
|
+
|
|
|
+if __name__ == '__main__':
|
|
|
+ cuts = ['extra-narrow', 'narrow', 'wide', 'extra-wide']
|
|
|
+ all_cut_plots(cuts)
|
|
|
+ for cut in cuts:
|
|
|
+ single_cut_plots(cut)
|
