pyminufit 0.2.1

A great package.

pyminufit

This package is an interface to iminuit to allow for fast simple fits and mimics the functionality of PyrooFit which is an interface 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.

Links

Description	Link
Documentation	pyminufit Documentation
GitHub Repository	pyminufit GitHub Repository

Example

Simple fit and plot of a Gaussian Distribution:

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


from pyminufit.models import Gauss, Chebyshev
from pyminufit.observables import create_real_var
import numpy as np


data = np.append(
    np.random.random_sample(1000) * 10 + 745, np.random.normal(750, 1, 1000)
)

x = create_real_var(("mass", 745, 755), unit="GeV")

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

pdf = pdf_sig + pdf_bkg

pdf.fit(data)
pdf.plot(data, "02_add_pdf.png")
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.

Installation

(will be accessible via pip soon)

pip install -e .

Development

If you have any questions or need help with this package, your contributions are greatly appreciated. This project is a side project, so any assistance is welcome. When contributing, please follow design principles that prioritize simplicity of the interface while preserving the complexity of the underlying functionality. You can contribute by submitting a pull request (PR) to the GitHub repository. For larger changes consider discussing ideas in GitHub Issues. Thank you for your support!

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This package was created using the scientific python template from https://scientific-python.org/.