Interface for plotting HEP data with matplotlib
Built with:
| Software | Version |
|---|---|
| matplotlib | 2.2.2 |
| numpy | 1.14.5 |
| ROOT | 6.14/04 |
| uproot | 3.2.5 |
To use in a CMSSW environment, the hepPlotter code will need to be nested inside a directory under $CMSSW_BASE/src/.
For this setup, it is assumed that other analysis code lives in the directory $CMSSW_BASE/src/Analysis/:
## setup CMSSW
cmsrel CMSSW_9_4_4
cd CMSSW_9_4_4/src/
cmsenv
git cms-init --upstream-only
mkdir Analysis
git clone https://github.com/demarley/hepPlotter.git Analysis/
Outside of a CMSSW environment,
you will need to modify your python path appropriately (as shown in the examples).
Thus, you can clone this repository (git clone https://github.com/demarley/hepPlotter.git)
into any directory you wish.
See the examples directory for jupyter notebooks that detail how to use the framework.
| Notebook | ABOUT |
|---|---|
| simple_1D_arrays.ipynb | Plots showing arrays turned into 1-dimensional histograms (created with numpy) |
| simple_1D_root.ipynb | Plots of ROOT histograms and efficiencies (accessed with ROOT) |
| simple_1D_uproot.ipynb | Plots showing 1-dimensional histograms (accessed with uproot) |
| simple_2D_arrays.ipynb | Plots showing 2-dimensional histograms (created/accessed with numpy) |
| simple_2D_root.ipynb | Plots showing 2-dimensional histograms (accessed with ROOT) |
| simple_2D_uproot.ipynb | Plots showing 2-dimensional histograms (accessed with uproot) |
| simple_datamc.ipynb | Plots showing Data/MC comparisons (accessed with ROOT) |
The framework now supports using c++ ROOT or uproot as backends.
This means that, for a given ROOT file with histograms, you can choose a backend
of either ROOT or uproot to open & manipulate your histograms.
This is particularly useful in virtual environments (and other python-focused settings)
where c++ ROOT isn't available.
To set your backend, simply set the option backend = 'uproot' or backend = 'ROOT'
when making your histograms (see the notebooks in examples/ for more information.
If no backend is declared, hepPlotter will first try to set the backend to c++ ROOT.
If that is unavailable, hepPlotter will try to use uproot as the backend.
You can see how the backend is defined
here.
Most commonly, data/mc plots are just for 1-dimensional distributions.
It is possible to plot 2-dimensional data/mc plots, but this is not natively supported in hepPlotter.
Instead, as a user you can pass a 2-dimensional histogram that represents the data/mc ratio to hepPlotter.
Here is an example snippet of code:
# 2-dimensional Data/MC plot
# Setup the plot with the usual declarations for 2-dimensional histograms
hist = Histogram2D()
hist.backend = 'ROOT'
hist.x_label = "x-label"
hist.y_label = "y-label"
hist.saveAs = "hist2d_datamc_example"
hist.CMSlabel = 'outer'
hist.CMSlabelStatus = "Simulation Internal"
hist.logplot['data'] = False
# Set properties unique to 2D histogram
hist.colormap = 'bwr' # blue-white-red
hist.colorbar['title'] = "Data/MC" # text label for colorbar
hist.initialize() # sets the parameters of the plot
# Add data to the plot - just an example (not stored in example.root)
total_mc_hist = file.Get("totalMC2D") # access 2D MC data (may need to add multiple histograms)
data_hist = file.Get("data2D") # access 2D data
data_hist.Divide(total_mc_hist) # get the ratio
hist.Add(datamc,name="datamc")
p = hist.execute() # make the plot (p represents the matplotlib 'figure' object)
hist.savefig() # save the figure (with name "hist.saveAs+hist.format") and close it
There is no native support to calculate systematic uncertainties for the user in hepPlotter. Instead, users are encouraged to modify the bin error to represent the total systematic uncertainty.
Future updates will allow the user to plot a statistical uncertainty, systematic uncertainty, and stat+syst uncertainty (in addition to individual curves for specific sources of uncertainty).
The code will be an expanded version of
this
function using code from
here.
Contact the author, submit an issue, or submit a PR.