def post_creation_processing_for_1d_correlations(
hist: Hist, normalization_factor: float, rebin_factor: int,
title_label: str, axis_label: str, jet_pt: analysis_objects.JetPtBin,
track_pt: analysis_objects.TrackPtBin) -> None:
""" Basic post processing tasks for a new 1D correlation observable. """
# Rebin to decrease the fluctuations in the correlations
# We don't scale by the rebin factor here because we will scale by bin width later.
# Since we will handle it later, it doesn't make sense to try to preserve normalization here.
hist.Rebin(rebin_factor)
# Scale
hist.Scale(1.0 / normalization_factor)
# Set title, labels
jet_pt_bins_title = labels.jet_pt_range_string(jet_pt)
track_pt_bins_title = labels.track_pt_range_string(track_pt)
# This won't look so good in ROOT, but that's just because their latex rendering is absolutely atrocious...
hist.SetTitle(
rf"{title_label} with {jet_pt_bins_title}, {track_pt_bins_title}")
hist.GetXaxis().SetTitle(axis_label)
hist.GetYaxis().SetTitle(fr"$\mathrm{{dN}}/\mathrm{{d}}{axis_label}$")
def scale_CPU_time(hist: Hist) -> None:
""" Rebin the CPU time for improved presentation.
Time is only reported in increments of 10 ms. So we rebin by those 10 bins (since each bin is 1 ms)
and then scale them down to be on the same scale as the real time hist. We can perform this scaling
in place.
Note:
This scaling appears to be the same as one would usually do for a rebin, but it is slightly more
subtle, because it is as if the data was already binned. That being said, the end result is
effectively the same.
Args:
hist: CPU time histogram to be scaled.
Returns:
None.
"""
logger.debug("Performing CPU time hist scaling.")
timeIncrement = 10
hist.Rebin(timeIncrement)
hist.Scale(1.0 / timeIncrement)