ceresfit 0.3.1

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

CEREsFit

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). 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.

Installation

The package can be installed from pypi via:

pip install ceresfit

Usage

Below is an example on how to use the package.

>>> 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.

Development & Contributing

This project is developed using Rye. 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 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.