Welcome to the iolite 4 documentation!¶
About¶
iolite is a cross-platform application for processing time-resolved data. It is mostly used for laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) data, but has also been used for solution ICP-MS, TIMS, LA-ICP-OES and LIBS 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. See the Python section for more information.
iolite v4 comes with everything you need to process datasets associated with the most common LA-ICP-MS applications. Detailed guides are available here.
If you have any questions or comments about iolite, you can contact us via email.
Contents:
- Installation Instructions
- Getting Your Data Into iolite
- User Interface Guide
- Tools
- Guided Examples
- Notes
- Python integration
- Manipulating selections from python
- Downsampling and exporting time series data
- Accessing session data from 3rd party software
- Installing additional python packages
- Transforming laser data into channels and results
- Comparing splines - part 1
- Accessing session data from 3rd party software
- Examining Covariance and Correlation in iolite
- Weighted U/Ca via a simple UI plugin
- Using IsoplotR in iolite
- Replacing channel data
- Coverting ppm channels to weight % oxide
- The Non-Linearity of the Age Equation and when you might notice it
- About masks in iolite
- Pb concentration calculations and TotalPb_ppm
- Synchronizing individual laser log samples
- Inserting Timestamps into Old Agilent Files
- Using image logs in iolite
- About updating resources
- Using a polygonal ROI as a clipping mask
- Using iolite 3/Igor Pro's color tables in iolite 4
- Using iolite's new Selection Checker tool
- Importing Nu Plasma .run files into iolite v4
- Selection refinement
- Filling in the blanks (in imaging)
- Python
- iolite Python for LA-ICP-MS Course
- Precognition