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MCUP will propagate uncertainty of your data points to the parameters of the regression using a Monte Carlo approach.

Project description


MCUP (Monte Carlo Uncertainity Propagation) is a Python library that estimates the uncertainty of least squares fit parameters with Monte Carlo.


PyPI pyversions PyPI version master Documentation Status codecov


The aim of this package is to estimate the error of regression parameters based on error intervals of the input data.

PEE – Parameter Error Estimator, a bootstraping method which, takes input data for lsq (x, y, x_err, y_err), generates datapoints within given errors and calculate mean and std of parameters from lsq fit.

Installing MCUP


To install mlxtend, just execute

python3 -m pip install mcup  

Dev Version

The MCUP version on PyPI may always be one step behind. You can install the latest development version from the GitHub repository by executing

python3 -m pip install git+


import numpy as np
from mcup import Measurement

x_data = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
y_data = np.array([0.1, 0.9, 2.2, 2.8, 3.9, 5.1])

y_sigma = np.array([0.0, 0.1, 0.1, 0.1, 0.1, 0.1])

def fun(x, c):
    return c[0] * x + c[1]

c_initial_guess = [0.0, 0.0]

measurement = Measurement(x=x_data, y=y_data, y_err=y_sigma)
measurement.set_function(fun, c_initial_guess)

params_mean, params_std = measurement.evaluate_params(iter_num=1000)
# [0.9901532  0.02477131]
# [0.01881003 0.04965347]

params_mean, params_std = measurement.evaluate_params(atol=1e-4, rtol=1e-4)
# [0.98854127 0.02771339]
# [0.0172098  0.04729087]


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