A Quick Histogram drawer for `ROOT::TTree` for smoother HEP analysis!

pip install qhist==0.1.2



package version pipeline status coverage report License: GPL v3 Documentation Status python version

QHist -- A Quick Histogram drawer for ROOT::TTree for smoother HEP analysis!

Examples (click arrow to view output):

>>> h1 = QHist()
>>> h1.trees = t1
>>> h1.params = 'M'
>>> h1.draw()

>>> QHist(trees=t1, params=['mu_PT/1e3','pi_PT/1e3'], xlabel='PT [GeV]', xmax=60).draw()

>>> H = QHist(trees=[t1, t2, t3], filters=['mu_PT>20e3'])
>>> H(params='APT'  , xmin=0  , xlabel='PT-asymmetry').draw()
>>> H(params='M/1e3', xmax=120, xlabel='Mass [GeV]').draw()

Installation & dependencies

It's available from pip install qhist. The package requires an existing installation of PyROOT.


This packacge was written and used during my PhD in 2013-2017 at EPFL (Lausanne) and LHCb collaboration (CERN), for the work in Z->tau tau cross-section measurement and H->mu tau searches at LHCb (8TeV). I hope it can be of a good use for future analysis...

Introduction: TPC & comparison stack

To draw a histogram in ROOT, generally a triplet of information (tree, param, cut) is needed. I call this TPC. The general command is:

tree->Draw("param", "cut")

In QHist, this is reorganized as:

h = QHist()
h.trees  = tree     # mandatory <ROOT.TTree>
h.params = "param"  # mandatory <string>
h.cuts   = "cut"    # optional  <string>

The instance of QHist can be considered as a struct of information to be attached onto before the call draw(). Once draw() is called, the output will be saved automatically (see section below for more detail). The QHist instance will be frozen and should not be used again.

By default, there's no need to explicitly give the axis information (min, max, number of bins), this is handle automatically.

Comparison stack means attaching more elements (list) to the desired field. For example, attaching more params from the same tree can be used to compare between branches:

h = QHist()
h.trees  = tree 
h.params = 'muon_PT', 'pion_PT', 'kaon_PT'

or multiple trees of same param (branch):

h = QHist()
h.trees  = t1, t2, t3
h.params = 'tau_M'

or comparing different cuts operating on the same tree's branch:

h = QHist()
h.trees  = t1
h.params = 'mu_PT'
h.cuts   = 'mu_Q > 0', 'mu_Q < 0'

Note that in all cases of stack above, the axis information also handle automatically using sensible default (i.e., axis min is the minimum of all entries, etc.)

It's forbidden to assign multiple variables on more than one of the TPC field at once. In case one wants to stack a branch of one tree, against another branch of another tree, a more advance field tpc should be used instead:

h = QHist()
h.tpc = [ (t1, p1, c1), (t2, p2, c2) ]

Note that in this case, a complete triplet TPC should be given in order to avoid ambiguity of missing component. If this mode of assignment is used, the simpler fields (trees, params, cuts) are disabled, and vice-versa.

In many case, this style of complex field can be avoided by using TTree.SetAlias appropriately, for example:

t1.SetAlias('some_figure_of_merit1', 'FOM')
t2.SetAlias('some_figure_of_merit2', 'FOM')
h = QHist()
h.trees = t1, t2
h.params = 'FOM'


Compact syntax

The compact syntax passes the arguments into the constructor instead of assignment on the instance. For example, these two instances are completely identical:

## expended version
h1 = QHist()
h1.trees = t1
h1.params = 'mu_PT'

## compact version
h2 = QHist(trees=t1, params='mu_PT')

The usage of compact syntax becomes more evident in templating, see the section below.

(safe) static assignment

All attributes are static, in a sense that once it's assigned, it can no longer be changed. This ensures that you get the histogram you wanted, with no interference:

>>> h = QHist(cuts='c1')
>>> h.cuts = 'c2'
Traceback (most recent call last):
AttributeError: Attribute "cuts" already set to ('c1',)

Also no-ducktyping, so that you're confident that you're using the correct field:

>>> QHist(cut='c1')
Traceback (most recent call last):
AttributeError: 'QHist' object has no attribute 'cut'

It's also type-safe: each field expect the correct argument type.

>>> QHist(trees=42, params=None)
Traceback (most recent call last):
TypeError: Required type (<class 'ROOT.TTree'>,)...

The list of fields are the following:

Category Fields
Core trees, params, cuts, tpc
Computation filters, normalize, options, postproc
Axis xmin, xmax, xbin, xlog, xlabel,ymin, ymax, ybin, ylog, ylabel,zmin, zmax, zbin, zlog, zlabel,
Styles st_code, st_color, st_line, st_marker, st_fill
Output name, title, prefix, suffix, batch, auto_name
Others legends, comment

Different histogram types

QHist support several of histogram types following the same syntax as TTree::Draw:

Type ROOT.TTree.Draw QHist
TH1 tree.Draw('param') h.params = 'param'
TH2 tree.Draw('Y:X') h.params = 'Y:X'
TH3 tree.Draw('X:Y:Z') h.params = 'X:Y:Z'
TProfile tree.Draw('Y:X', '', 'prof') h.params = 'Y:X', h.options = 'prof'

However, comparison stacking is only supported in TH1, TProfile, and partially for TH2.

Cosmetic controls


The axis control used to be buried deep in each TAxis instance, and not so easy to change without good understanding of drawing lifecycle. For QHist, the following fields are exposed. They are normally tried to be determined automatically, but the user can always set it explicitly:

h = QHist()
h.xmin = 20         # (float) axis minimum
h.xmax = 100        # (float) axis maximum
h.xbin = 10         # (int)   number of bins on axis
h.xlog = True       # (bool)  should the axis be on log scale or not.
h.xlabel = 'mass'   # (str)   Label on the axis.

There are same fields for Y-axis (ymin, ymax, ...) and Z-axis (zmin, zmax, ...). This unifies the usage whether the histogram is TH1 or TH2.


By default, the legends are tried to be determined from the given stack. Otherwise it can be set explicitly from list of string, with the same number as stacking entries.

h = QHist(trees=[t1,t2])
h.legends = 'Z -> tautau', 'Z -> mumu'

Styling: (Line, Marker)-(Width, Size, Color, Style)

QHist exposes the following boolean-attribute to control more cosmetics

  • st_color: If True, turn on the color, apply to LineColor, MarkerColor, FillColor. The color rotation follows ROOT.TColor.GetColorPalette.

  • st_line: If True, turn on the LineStyle. The rotation style is hard-coded for now.

  • st_marker: If True, turn on the MarkerStyle. The rotation style is hard-coded for now.

  • st_fill: If True, turn on the FillStyle. The rotation style is hard-coded for now.

In addition, I have an experimental st_code = int to change the stacking style.

  • st_code = 0: default stacking, i.e., super-imposed over each other.
  • st_code = 1: like above, but first entry is dot-marker, others are filled.
  • st_code = 2: Like above, but the first entry is not stacked, and it used dot-marker. Other entries are stacked and color-filled. I used this mainly to plot signal versus sum of backgrounds.


String argument attached to QHist.options = 'opt' will be passed to the third argument of TTree.Draw('param', 'cut', 'opt'). This will create the desirable effect of that option. Note that I have not test this extensively, just prof and colz. If a list of string is given, the each option will be attributed to each member of the stack.


The field cuts is designed to be used for comparison stack. In the case where same cuts is intended to be applied identically to all entries, the field filters should be used:

h = QHist()
h.trees   = t1
h.params  = 'APT'
h.cuts    = 'mu_Q > 0', 'mu_Q < 0'  # comparison
h.filters = 'DPHI > 2.7'            # filtering

The general boolean operator behaves as expected:

h = QHist()
h.filters = '(mu_PT > 20) & (pi_PT > 20) & (pi_BPVIP > 0.03)'

Because the string can be long and complex with all the nested bracket, QHist can help you more: passing the list,tuple of string acts as a boolean-AND, whilst set acts as boolean-OR:

h = QHist()
h.filters = [
  'mu1_PT > 20',
  'mu2_PT > 20',
  {'mu1_trigger', 'mu2_trigger'},  # any muon fires trigger.
  'mu1_mu2_MASS > 40',


The field normalize, used to control the normalization (integral) of the histograms, can be quite complex: its behavior depends on the type of argument:

  • bool: True (default) normalizes all histograms to 1, while False does nothing.
  • float: normalizes the histogram such that its integral equals to the given value.
  • callable(float)->float: passing a 1-arg function receives current histogram integral as an input, and the function should return new integral as an output.

Passing multiple arguments to normalize will align each argument with the entries in the comparison stack, so the same length is expected, for example:

h = QHist()
h.trees     = t1, t2, t3    # 3 entries
h.normalize = [40, 25, 15]  # 3 integrals for each histograms


This is the most important feature of QHist and the reason I wrote this in the first place.

QHist provides a facility for histogram templating when you want to plot several set of histograms with input consistency (i.e., Dont-repeat-yourself). Each instance of QHist can be called like a constructor to create a new instance, passing all of the fields downstream. The static nature of the fields ensure that each descendant respects the value set in the ancestor.

## Base template
H0 = QHist(trees=[t1,t2,t3], filters='weight', prefix='signal', st_code=2)

## Base mass plot
Hmass = H0(params='mass', xmin=20, xmax=120, xlabel='mass [GeV/c^{2}]')
Hmass(name='M20', xbin=20, ylabel='Candidates / (5 GeV/c^{2})').draw()
Hmass(name='M50', xbin=50, ylabel='Candidates / (2 GeV/c^{2})').draw()

## Others
H = H0(cuts='mass_window', ylabel='Candidates')
H(params='P/1e3', xmin=90 , xmax=3e3).draw()
H(params='ETA'  , xmin=2.0, xmax=8.0).draw()

Just imagine doing this in plain ROOT, keeping consistency across all plots, same cuts, axis, style, annotations. That is ~300 lines of code down to 10...


QHist has sensible behavior concerning the output once draw is complete:

  • First, it check if the field name is set, or flag auto_name = True. If not, it will save nothing. This is probably a quick interactive session.

  • If name is set or auto_name = True, it will save 2 outputs: '.pdf' and '.root'. The subdirectory with the name of current script will be created, and the output will go there. In the subsequent call, next output will be dump into the same subdir, as well as new entry appended into the 'root' file if existed.

For example, given QHist(name='hist') in ./myscript.py file, the output will be ./myscript/hist.pdf and ./myscript/myscript.root.

The flag auto_name will try its best to deduce an appropriate name judging from the comparison stack. Of course, if things are complicate you should give it an explicit name yourself.

Other notable naming fields are:

  • title: This appears as the title of the plot. By default it follows name, but setting this directly has higher priority.

  • prefix, suffix: Useful in the templating, where the string in either field appears as prefix/suffix of the name/title output.

User-defined post-processing & anchors

QHist provides the way of drawing histograms quickly, so the freedom may seem limited if the user intended to draw something more complicate, or modify the resultant plots, fine-tune the color, annotating with more labels, etc.

The field postproc provide a solution where the user-defined action can be done before the output stage. It accept a function with one argument, representing the QHist instance itself. The actions inside this function will be run before output is saved. For example:

def postproc(h):

  ## Tune legend position
  leg = h.anchors.legend
  leg.header   = 'LHCb#sqrt{s} = 8 TeV'
  leg.x1NDC    = 0.66
  leg.x2NDC    = 0.93
  leg.y1NDC    = 0.56
  leg.y2NDC    = 0.93
  leg.textSize = 0.06

  ## Optional mask
  if 'Mnowin' in h.name:
    dt = h.prefix
    h  = h.anchors.stack
    mmin, mmax = (20,120) if dt=='emu' else (20,60) if dt in ('mumu', 'ee') else (30,120)
    ymax = h.anchors.mainpad.uymax
    box1 = ROOT.TBox(h.xaxis.xmin, 0., mmin, ymax)
    box2 = ROOT.TBox(mmax, 0., h.xaxis.xmax, ymax)
    for box in (box1, box2):
      box.ownership = False
      box.fillStyle = 1001
      box.fillColorAlpha = 1, 0.3

  ## Force no-exponent
  h.anchors.stack.xaxis.noExponent = True

## attach
h = QHist()
h.postproc = postproc

In particular, h.anchors provides collected "pointers" to important TObject in the drawing. These fields can be accessed:

Name Object
canvas TCanvas of the entire canvas.
mainpad TPad of the main pad showing the histogram.
stack THStack instance that holds member histograms.
legend TLegend object to the legend of the pad.