MCUP (Monte Carlo Uncertainity Propagation) is a Python library that estimates the uncertainty of least squares fit parameters with Monte Carlo.
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.
To install mlxtend, just execute
python3 -m pip install mcup 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=mcupimport 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]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.