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Geant4 Zero to Hero

The Definitive Guide for End Users

Python for Geant4 Data Analysis

The Python package uproot can be used to read Geant4 output file saved in ROOT format. It can be easily installed using python -m pip install uproot.

If you are familiar with ROOT and would like to migrate to Python for analyzing Geant4 output in ROOT format (for example, an output file from GEARS), you can find here a brief list of Python equivalence of ROOT commands:

Open file:

ROOT:

$ root gears.root # open gears output in ROOT format

Python:

$ python
>>> import uproot as up
>>> file = up.open("gears.root")

Check file contents:

ROOT:
```
root[] .ls
```
Python:
```
>>> file.classnames()
```
or
```
>>> file.keys()
```

List variables in TTree ntuples:

ROOT:
```
root[] t->Show()
```

Python:

>>> t = file['t'] # get TTree object 't' from file
>>> t.show() # show variables saved in the tree, t
>>> t.keys() # another way to show variables saved in the tree

Draw the distribution of a variable as a histogram:

ROOT:
```
root[] t->Draw("x")
```

Python:

>>> x=t['x'].array(library='pd')
>>> x.plot.hist(bins=100)
>>> import matplotlib.pyplot as plot
>>> plot.show()

Draw the distribution of a selected subset of the variable as a histogram:

ROOT:

root[] t->Draw("x", "vlm==1") // draw x coordinate of step points in volume 1

Python:

>>> import awkward as ak
>>> import matplotlib.pyplot as plot
>>> x = t.arrays(['x'], "vlm==1") # get all x in vlm 1
>>> plot.hist(ak.flatten(x, axis=None)), bins=100)
>>> plot.show()

Ref. https://awkward-array.org/doc/main/user-guide/how-to-restructure-flatten.html

Draw a 2D histogram:

ROOT:

root[] t->Draw("y:x") // draw x, y distribution of step points

Python:

>>> import numpy as np
>>> import awkward as ak
>>> import matplotlib.pyplot as plot
>>> x = np.asarray(ak.flatten(t['x'].array()))
>>> y = np.asarray(ak.flatten(t['y'].array()))
>>> plot.hist2d(x,y,bins=100)

Ref.: