Metadata-Version: 2.3
Name: pyminufit
Version: 0.2.2
Summary: A Python package for fitting data with iMinuit
Project-URL: Homepage, https://github.com/SimonUU/pyminufit
Project-URL: Bug Tracker, https://github.com/SimonUU/pyminufit/issues
Project-URL: Discussions, https://github.com/SimonUU/pyminufit/discussions
Project-URL: Changelog, https://github.com/SimonUU/pyminufit/releases
Author-email: Simon <simon@gmail.com>
License: BSD 3-Clause License
        
        Copyright (c) 2024, Simon.
        All rights reserved.
        
        Redistribution and use in source and binary forms, with or without
        modification, are permitted provided that the following conditions are met:
        
        * Redistributions of source code must retain the above copyright notice, this
          list of conditions and the following disclaimer.
        
        * Redistributions in binary form must reproduce the above copyright notice,
          this list of conditions and the following disclaimer in the documentation
          and/or other materials provided with the distribution.
        
        * Neither the name of the vector package developers nor the names of its
          contributors may be used to endorse or promote products derived from
          this software without specific prior written permission.
        
        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
        AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
        IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
        DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
        FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
        DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
        SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
        CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
        OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
        OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
License-File: LICENSE
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Typing :: Typed
Requires-Python: >=3.8
Requires-Dist: iminuit
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scipy
Provides-Extra: dev
Requires-Dist: black; extra == 'dev'
Requires-Dist: pytest-cov>=3; extra == 'dev'
Requires-Dist: pytest>=6; extra == 'dev'
Provides-Extra: docs
Requires-Dist: furo>=2023.08.17; extra == 'docs'
Requires-Dist: myst-parser>=0.13; extra == 'docs'
Requires-Dist: sphinx-autodoc-typehints; extra == 'docs'
Requires-Dist: sphinx-copybutton; extra == 'docs'
Requires-Dist: sphinx>=7.0; extra == 'docs'
Provides-Extra: test
Requires-Dist: pytest-cov>=3; extra == 'test'
Requires-Dist: pytest>=6; extra == 'test'
Description-Content-Type: text/markdown

# pyminufit

[![Actions Status][actions-badge]][actions-link]
[![Documentation Status][rtd-badge]][rtd-link]

[![PyPI version][pypi-version]][pypi-link]
[![Conda-Forge][conda-badge]][conda-link]
[![PyPI platforms][pypi-platforms]][pypi-link]

[![GitHub Discussion][github-discussions-badge]][github-discussions-link]

<!-- SPHINX-START -->

<!-- prettier-ignore-start -->
[actions-badge]:            https://github.com/SimonUU/pyminufit/workflows/CI/badge.svg
[actions-link]:             https://github.com/SimonUU/pyminufit/actions
[conda-badge]:              https://img.shields.io/conda/vn/conda-forge/pyminufit
[conda-link]:               https://github.com/conda-forge/pyminufit-feedstock
[github-discussions-badge]: https://img.shields.io/static/v1?label=Discussions&message=Ask&color=blue&logo=github
[github-discussions-link]:  https://github.com/SimonUU/pyminufit/discussions
[pypi-link]:                https://pypi.org/project/pyminufit/
[pypi-platforms]:           https://img.shields.io/pypi/pyversions/pyminufit
[pypi-version]:             https://img.shields.io/pypi/v/pyminufit
[rtd-badge]:                https://readthedocs.org/projects/pyminufit/badge/?version=latest
[rtd-link]:                 https://pyminufit.readthedocs.io/en/latest/?badge=latest

<!-- prettier-ignore-end -->

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][rtd-link]         |
| GitHub Repository | [pyminufit GitHub Repository][actions-link] |

This package is based on top of [iminuit](https://scikit-hep.org/iminuit/). For
more information, you can refer to the
[iminuit documentation](https://iminuit.readthedocs.io/en/latest/).

## Example

Simple fit and plot of a Gaussian Distribution:

```python
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:

```python
import pyminufit as mnf
import numpy as np

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

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

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

pdf = pdf_sig + pdf_bkg

pdf.fit(data)
pdf.plot(data, "02_add_pdf.png", dpi=150)
pdf.get()
```

![Fitting multiple pdf to data.](./examples/02_add_pdf.png)

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

You can install `pyminufit` directly from PyPI using pip:

```bash
pip install pyminufit
```

Alternatively, if you want to install the development version directly from
GitHub, you can do so with:

```bash
pip install git+https://github.com/simonuu/pyminufit.git
```

For a development installation, download the git reposityro and run:

```bash
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!

## Planned Features

- [ ] ProdPdf: Product pdf for multiple observables
- [ ] Convolutions
- [ ] Shared parameters
- [ ] Backend options for the pdf calculation

---

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