PyrooFit

PyrooFit is a fit framework for python and pandas DataFrames on top of the ROOT.RooFit package.

The package allows for simple fits of standard PDFs and easy setup of custom PDFs in one or more fit dimensions.

Example

Simple fit and plot of a Gaussian Distribution:

from pyroofit.models import Gauss
import numpy as np

data = np.random.normal(0, 1, 1000)

pdf = Gauss(('x', -3, 3), mean=(-1, 0, 1))
pdf.fit(data)
pdf.plot('example_gauss.pdf',)

pdf.get()

A more complex example on combination of Gauss pdf for signal and Polynomial for background:

from pyroofit.models import Gauss, Chebychev
import numpy as np
import pandas as pd
import ROOT



df = {'mass': np.append(np.random.random_sample(1000)*10 + 745, np.random.normal(750, 1, 1000))}
df = pd.DataFrame(df)

x = ROOT.RooRealVar('mass', 'M', 750, 745, 755, 'GeV')  # or x = ('mass', 745, 755)

pdf_sig = Gauss(x, mean=(745, 755), sigma=(0.1, 1, 2), title="Signal")
pdf_bkg = Chebychev(x, n=1, title="Background")

pdf = pdf_sig + pdf_bkg

pdf.fit(df)
pdf.plot('example_sig_bkg.pdf', legend=True)
pdf.get()

Observables can be initialised by a list or tuple with the column name / variable name as first argument, followed by the range and/or with the initial value and range:

x = ('x', -3, 3)
x = ('mass', -3, 0.02, 3)

Parameters are initialised with a tuple: sigma=(0,1) or again including a starting parameter: sigma=(0.01, 0, 1) The order here is not important.

All parameters and observables can also be initialised by a ROOT.RooRealVar.

Installation

Dependencies: ROOT (with PyRoot enabled)

• Download this repository

• (recommended) Use or install anaconda python environment

• Activate ROOT installation with python support

• run python setup.py install in this folder

• run python setup.py docs to create the documentation

If you do not have your own python installation you can use:

python setup.py install --user
PATH=$PATH~/.local/bin

If there are still missing packages you might need to install them via pip install package --user.

Planned Features

• Improve documentation
• Save and load PDF as yaml
• Plotting in matpltotlib