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| tuple | significance_2d.log = logging.getLogger(__name__) |
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| tuple | significance_2d.parser = argparse.ArgumentParser(description="Plot significance from combine output.") |
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| list | significance_2d.channels = ["et", "mt", "tt", "em"] |
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| tuple | significance_2d.args = parser.parse_args() |
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| tuple | significance_2d.fig = plt.figure() |
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| tuple | significance_2d.ax = fig.add_subplot(111) |
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| list | significance_2d.plots = [] |
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| | significance_2d.mass = args.mass |
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| tuple | significance_2d.x_range = ([float(a) if "." in a else int(a) for a in args.x_range.split(",")]) |
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| tuple | significance_2d.p1s = np.linspace(*x_range) |
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| tuple | significance_2d.p1_width = (max(p1s) - min(p1s)) |
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| tuple | significance_2d.y_range = ([float(a) if "." in a else int(a) for a in args.y_range.split(",")]) |
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| tuple | significance_2d.p2s = np.linspace(*y_range) |
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| tuple | significance_2d.p2_width = (max(p2s) - min(p2s)) |
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| list | significance_2d.matrix = [[0 for x in range(len(p1s))] for y in range(len(p2s))] |
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| tuple | significance_2d.in_raw = os.path.join(args.input_dir, str(p1)+"_"+str(p2), args.folder, str(mass), "higgsCombineTest.ProfileLikelihood.mH125.*.root") |
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| tuple | significance_2d.in_file = glob.glob( in_raw) |
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| tuple | significance_2d.current_chain = ROOT.TChain("chain_%i_%f"%(p1, p2)) |
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| | significance_2d.y = line.limit |
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| tuple | significance_2d.npmatrix = np.matrix(matrix) |
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| tuple | significance_2d.min_value = np.min(npmatrix[np.nonzero(npmatrix)]) |
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| tuple | significance_2d.im = plt.imshow(matrix, origin = "lower", cmap=plt.cm.Reds, interpolation="None", extent=[p1s[0]-p1_width/2, p1s[-1]+p1_width/2,p2s[0]-p2_width/2, p2s[-1]+p2_width/2], clim = (min_value,npmatrix.max())) |
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| dictionary | significance_2d.labels = { "mjj" : "$m_{jj}$", "jdeta" : "$\Delta \eta_{jj}$", "hpt" : "$p_T^{H}$", "pt2" : "$p_T^2$"} |
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