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p:IGI+ makes importing, filtering, exploring and visualising data easy. Allowing you to form geochemical interpretations as part of an integrated analysis project.
The latest articles on Metis
The IGI property model shipped with version 2. 3 of the software sees the introduction of a new PVT property model relevant to geochemistry.
Dear users, we’re delighted to announce version 2. 3 of both p:IGI+ and Metis is now available.
The full release notes can be found here: Full-Release-Notes-2_3. pdf, here we summarise the highlights.
Unfortunately, as the sun set on the summer solstice on the beach at Westward Ho! we did not see a fabled green flash. However, there are several outcomes we can share with everyone!
First, we heard loud and clear, companies prefer fewer releases.
Import text, Excel and p:IGI-3 data, with an easy to use, semi-automated import system with full alias support.
Define your own properties to add to our extensive model, and seamlessly integrate these with the whole system.
Powerful graphing, including histograms, scatter plots, depth plots, triangle plots and line plots.
Brush data across graphs, maps and pages, to identify unusual or interesting data or easily create samples sets.
Easily create filters for your data (sample sets) based on complex criteria to find the data of interest.
Integrated Geochemical Interpretation
Interactively explore data through predefined and custom graphs with industry-standard overlays. Integrate your spatial data using maps. Use interactive selection, visual analytics, and point-and-click insights to assist in drilling down across multi-layer data sources.
Intuitive import of sample data from a central database server or Excel and text files with access to an extensive predefined geochemical property model and unlimited user properties and equations.
Efficiently summarise your data and identify where further analyses are needed. Use customisable spreadsheet views to smoothly quality control and edit data, with full undo support.
Identify relationships in data, and discover trends, outliers, and patterns in any dataset. Follow a data-driven approach using the integrated data science models, including PCA and k-means cluster analysis.
