The main error propagation algorithm in iolite was first published by Paton et al. (2010). Its purpose is to estimate the additional uncertainty to be added to the internal uncertainty calculated by iolite and is most often used in U-Pb studies.

NOTE: The following is a guest post by Graham Hagen-Peter (even though I -Bence- am listed as the author, it is Graham’s work; I am just posting it here).

The iolite GitHub repository contains many examples of additional modules for iolite written in python. It’s not always obvious how to download and use these files, so here’s a quick rundown of the process.

Laser logs and mass spec sample metadata speed up the process of creating selections in iolite considerably. However, sometimes we want to further refine those selections for a variety of reasons.

To import mass spec data into iolite, a few key pieces of information are required. The most basic of these are the time the data were collected and what masses were measured.

Missing the color tables of iolite 3/Igor Pro in iolite 4? Read on to find out how you can transfer those color tables from Igor Pro to iolite 4!

An important part of iolite is the time ordering of data. This allows iolite to accurately model time-dependent phenomena such as sensitivity drift, mass fractionation and baseline variation.

Laser logs can save a lot of time when setting up selections in iolite. Rather than painstakingly reviewing each bit of data and assigning it to a selection/group, the time stamps in a laser log and the analysis annotations can be matched up with mass spec data to automate this process.

One question that came up recently was how to get the results in weight percent oxide rather than ppm. In this note, we’ll go through one way you can do that using iolite’s python functionality.

We recently had a question about replacing values below 0 with 0 so I thought I’d write a quick note to demonstrate one way you can do that using iolite’s python functionality.