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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

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

Status

PyPI pyversions PyPI version shields.io master Documentation Status codecov

Scope

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

PyPI

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+https://github.com/detrin/MCUP.git#egg=mcup

Example

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)
print(params_mean)
# [0.9901532  0.02477131]
print(params_std)
# [0.01881003 0.04965347]

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

Contributing

When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change.

Project details


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mcup-0.1.1.tar.gz (6.4 kB view hashes)

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