CEREsFit

KO | EN

gitlite — search

#hacktoberfest #react #ai #rust #deep-learning #linux #macos #nodejs #java #windows #security #ios #mcp #open-source

(★ 13)

Linear Regression for data set with correlated or uncorrelated uncertainties in both axes.

CEREsFit Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# CEREsFit

[![DOI](https://zenodo.org/badge/496215403.svg)](https://zenodo.org/badge/latestdoi/496215403)
[![pypi](https://img.shields.io/pypi/v/ceresfit?color=informational)](https://pypi.org/project/ceresfit/)
[![tests](https://github.com/galactic-forensics/CEREsFit/actions/workflows/package_testing.yml/badge.svg)](https://github.com/galactic-forensics/CEREsFit/actions/workflows/package_testing.yml)
[![codecov](https://codecov.io/gh/galactic-forensics/CEREsFit/branch/main/graph/badge.svg?token=C8KN5UE831)](https://codecov.io/gh/galactic-forensics/CEREsFit)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/galactic-forensics/CEREsFit/main.svg)](https://results.pre-commit.ci/latest/github/galactic-forensics/CEREsFit/main)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

The goal of CEREsFit (Correlated Errors Regression Estimate Fit)
is to provide a python package
that allows to calculate linear regressions on data sets with correlated uncertainties.
The calculations follow the methodology published by
[Mahon (1996)](https://doi.org/10.1080/00206819709465336).
Typos and errors that were made in that work have been corrected.
A method to allow calculating a linear regression through a fixed point,
avoiding previously made errors,
is also provided.

Details on CEREsFit can be found in [Stephan and Trappitsch (2023)](https://doi.org/10.1016/j.ijms.2023.117053).

## Installation

The package can be installed from `pypi` via:

```
pip install ceresfit
```

## Usage

Below is an example on how to use the package.

```python
>>> import numpy as np
>>> from ceresfit import LinReg

>>>  # some data
>>> xdata = np.array([1, 2, 3.1, 4.9])
>>> ydata = np.array([1.1, 1.9, 3, 5.5])

>>>  # some uncertainty and correlation
>>> xunc = 0.05 * xdata
>>> yunc = 0.073 * ydata
>>> rho = np.zeros_like(xdata) + 0.5

>>>  # do regression
>>> my_reg = LinReg(xdata, xunc, ydata, yunc, rho)

>>>  # print out the parameters and their uncertainties
>>> my_reg.slope
(0.9983613298400896, 0.06844666435449052)
>>> my_reg.intercept
(0.05545398718611372, 0.11812746374874884)
>>> my_reg.mswd
2.5105964767071143

```

Detailed example on how to use the class for fitting and plotting the results
can be found
[in these Jupyter notebooks](https://github.com/galactic-forensics/CEREsFit/tree/main/examples).

## Development & Contributing

This project is developed using [Rye](https://rye-up.com/).
After cloning,
you can simply run

```rye sync```

in the project folder and you should be good to go.

Code auto formatting is implemented using
[`pre-commit`](https://pre-commit.com/) hooks.

For local formatting and linting,
please use

```
rye fmt
rye lint
```

For running tests, use:

```
rye test
rye run test_docs
```

The first of these commands runs `pytest`,
the second checks the documentation tests using `xdoctest`.

Please feel free to raise issues on GitHub
and open pull requests if you have a feature to be added.
Tests and adequate docstrings should be provided along with your new code.

CEREsFit

CEREsFit Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

CEREsFit

CEREsFit Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README