Canopy Geoscience (Canopy Geo) is a Python-based analysis environment that is straightforward to extend with custom data analysis workflows and visualization tools, providing a comprehensive solution for rapid development of new analysis methods, creation of user interfaces to streamline custom workflows, and in-house deployment of final applications.
• Powerful cross-domain data analysis and visualization
• Extensible for new analysis and visualization solutions
• Flexible, customizable Python-based platform
• Allows for rapid innovation and implementation
Canopy Geo provides novel capabilities to co-visualize data spanning multiple geoscience disciplines, significantly reducing the limitations of domain-specific solutions to accelerate the delivery of innovative analysis and research methods to the geoscience community.
In Canopy Geo you can add new data types, editors, menu entries, and menus to build your own extensions for custom algorithms, cross-domain workflow automation, and multi-domain visualization tools. Developers can contribute reader-writers, data objects, editors, 3D elements for the 3D viewer, menu actions, dockable side panels, and more through specialized add-ons.
The flexible I/O and data architecture and standard readers mean you can also add new data file formats and Canopy Geo data objects and make them accessible to Canopy Geo’s various tools. With Python scripts and the Canopy Geo object model, you can extend the platform to handle additional data file formats.
Access the full power of the scientific Python ecosystem for custom data processing, interactive analyses, and advanced plotting and visualization.
Canopy Geo includes an IPython interactive command line console for inspecting and processing data objects. You can drag a SEG-Y data object into the console, print its header, edit its data and run advanced analysis or data processing scripts on it.
With Canopy Geo’s advanced editor, you can write scripts to automate analysis pipelines and new workflows. For example, a script can access a data object loaded in the data panel, analyze its content, create new data objects based on this analysis, and open a visualization tool to display the result. With the editor, you can drag a script in to open it, find context sensitive help, use auto-completion and right-click to run the script in the IPython console.