Simple and customizable plotting of ROOT objects (TH1, TH2, TGraph...) with matplotlib.
matplotlib is a powerful Python plotting library. This package aims to provide a simple way to use histograms and graphs created with the ROOT framework, that is ubiquitous in experimental high-energy physics, in matplotlib. The wrapper methods rely on the PyROOT bindings and are kept minimal, in order to give the user maximal flexibility to control the layout.
A simple example:
import ROOT
h1 = ROOT.TH1F("h1", "h1", 20, -5., 5.)
h1.FillRandom("gaus", 250)
## draw with ROOT
c1 = ROOT.TCanvas("c1")
h1.GetXaxis().SetRangeUser(-5., 5.)
h1.SetLineColor(ROOT.kRed)
h1.Draw("HIST")
c1.Update()
## draw with mplbplot
import matplotlib.pyplot as plt
import mplbplot.decorateAxes ## object-oriented API
fig,ax = plt.subplots(num="Fig1")
ax.rhist(h1, histtype="step", color="r")
ax.set_xlim(-5., 5.)
plt.show()More usage examples can be found in the examples directory, and, as Jupyter notebooks, here. A more diverse set of plots made with the help of this package can be found in my PhD thesis.
Three different ways for importing the helper methods can be used
(they can be freely mixed as well): the first blends nicely with
matplotlib's object-oriented API, and adds rhist, rplot, etc.
methods to matplotlib.axes.Axes when importing mplbplot.decorateAxes, e.g.
import ROOT
h = ROOT.TH1F("h1", "h1", 10, -5., 5.)
h.FillRandom("gaus", 1000)
from matplotlib import pyplot as plt
import mplbplot.decorateAxes ## object-oriented API
fig,ax = plt.subplots()
ax.rhist(h, histtype="step", color="r")The second adds the equivalent methods to the matplotlib.pyplot module, e.g.
import ROOT
h = ROOT.TH1F("h1", "h1", 10, -5., 5.)
h.FillRandom("gaus", 1000)
from matplotlib import pyplot as plt
import mplbplot.decoratePyplot ## pyplot decorators
fig,ax = plt.subplots()
plt.rhist(h, histtype="step", color="b")The mplbplot.pyplot module provides wrapper methods that check
if the first argument is a ROOT object, take the custom implementation
in that case, or fall back to the corresponding matplotlib.pyplot
method otherwise, the example can then be written as
import ROOT
h = ROOT.TH1F("h1", "h1", 10, -5., 5.)
h.FillRandom("gaus", 1000)
from matplotlib.pyplot import *
from mplbplot.pyplot import *
fig,ax = subplots()
hist(h, histtype="step", color="g")The code is structured in two layers: a set of decorators for pythonic access to the contents of ROOT histograms and graphs, and drawing methods that mimic those in matplotlib's pyplot and axes modules, but take a ROOT histogram or graph as their first argument.
The decorators are a bins method for histograms,
and a points method for graphs, both returning a list-like object,
with each element referring to a histogram bin or graph point.
Accessing the attributes of the (proxy) bin or points objects
will call the corresponding accessor of the histogram object,
e.g. bins(h)[1].content is equivalent to h.GetBinContent(1),
and points(g)[0].x to g.GetX()[0].
This turns out to be quite covenient when writing the drawing methods,
where several bin or point properties
(e.g. bin center, width, and height,
or point coordinates and uncertainties)
are often iterated over together, in zip mode.
The helper methods can be accessed in three ways, depending on the
matplotlib API choice, and the preference for making the difference
between the matplotlib and mplbplot methods explicit or not.
See above for an example of each.
Finally, the mplbplot.plothelpers module contains a collection of
components (tick label formatters etc.) and methods that may be useful
for providing a uniform layout similar to the default ROOT style.