Welcome to the iolite 4 documentation!¶
iolite is a framework for processing time-resolved mass spectrometry data. It is mostly used for laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) data, but has been used for solution ICP-MS and TIMS data reduction. It specialises in time-resolved data reduction, with tools for baseline subtraction and normalisation to reference materials, as well as many tools for visualisation. iolite also includes advanced imaging capabilities, such as creating CellSpace images, and advanced image statistical interrogation (e.g. regions of interest, profiles etc).
iolite is a framework in that it comes with a set of best-practice features (e.g. trace element, U-Pb data reduction schemes, image construction capabilities), but is flexible and customisable so that iolite’s extensive community of users can write their own extensions to iolite. These procedures can be shared, edited, improved and evolved by the analytical community. This has been the case for much of iolite’s history. Many of iolite’s most popular features were developed by users outside the iolite Team. For example, Joe Petrus wrote the VisualAge DRS for processing U-Pb data in 2011, long before he became part of the iolite Team. He has since joined the iolite Team to become the Lead Developer. Many other third party features have been developed and shared with the community, and this continues with iolite version 4.
iolite version 4 is a completely new version of iolite. It has been re-written in C++ and python. Using C++ gives us incredibly fast processing times (up to 10 times faster than iolite v3 running in Igor Pro), and allowed us to write a modern and clean user interface. By including python scripting, our users can write extensions for iolite using python, rather than Igor Pro’s programming language (as in version 3). Python is a beautiful language to write in: it’s clean, clear and easy to learn. The other great thing about Python is that we can ship iolite v4 with a bunch of really powerful Python packages, such as NumPy (for fast and efficient handling of large datasets), SciPy (for optimization, linear algebra, interpolation, signal and image processing, and machine learning) and many others. You can also add additional Python packages so that you can access them while using iolite v4.
iolite v4 comes with everything you need to process datasets associated with the most common LA-ICP-MS applications. Detailed guides are available here.
- Installation Instructions
- Getting Your Data Into iolite
- User Interface Guide
- Guided Examples