Getting getting

Begin by importing the pysoplot module.

import pysoplot

A test data set can be accessed from the pysoplot.data module.

dp = pysoplot.data.LA0708

The analytical uncertainties on this data set are given at the \(2\sigma\) level. As a general rule, functions in pysoplot expect uncertainties to be given as \(1\sigma\) absolute, so we will need to transform these before continuing.

dp = pysoplot.transform.dp_errors(dp, 'abs2s')

Now that these data point uncertainties are in the correct form, we can fit a linear regression. by setting the plot argument to True, we have told the function to also compile a plot of the data points and regression fit.

fit = pysoplot.regression.robust_fit(*dp, plot=True, diagram='tw')

This function returns a dictionary containing the regression fitting results and the plot. We can print the regression results and show the plot

pysoplot.misc.print_result(fit, 'Regression results')
fit['fig'].show()

Now that we have fitted a regression line, we can compute a concordia intercept age

tw = pysoplot.upb.concint_age(fit, method='Powell')

More coming soon…