Fast Prototyping Informs the Way Forward
An extensive set of Python-based analysis tools, many open-source, enables Enthought scientists to collaborate with clients and very quickly pursue concepts, and explore and iterate new workflows, ones offering significant improvements in efficiency and business performance.
For software and infrastructure prototyping, development and deployment, Enthought has a platform specifically designed for advanced scientific software tools and techniques, including those that are cloud based. This enables rapid development and in-house testing prior to deployment of final applications. Components of this platform–and integration expert services–are available to clients.
An example of an Enthought internal technology is the CanopyGeo Python-based analysis environment. CanopyGeo enables Enthought domain experts to:
- Apply visualization tools to understand data relationships
- Craft and apply custom data analysis techniques to diverse data sets
- Prototype user interfaces to streamline workflows
- Prepare and test for in-house deployment for final applications
Brendon Hall | 0m48s | Co-visualizing Cross-disciplinary Data in CanopyGeo
Orders of magnitude time savings, consistency, and understanding were achieved through customized software collaboratively built by Enthought working with client experts for labeling a limited set of seismic lines. Client proprietary neural networks then interpreted the entire volume, making use of the Enthought deep learning model building tool kit. Areas in question (for example faults, salt domes) could be relabeled–and models adjusted–until experts were confident in the interpretation.
Deep Geoscience Expertise + Business Challenges + Scientific Software Craftsmen = Possibilities
Our highly technical clients have deep expertise in geoscience, oil & gas, and all aspects of their business. Enthought brings the potential of a new generation of digital technologies to their business, in particular AI/Machine Learning techniques.
Enthought experts combine strength in fundamental science, oil & gas domain knowledge, and scientific software development. Together, we collaborate with our clients to develop new possibilities; possibilities in expert efficiency increase, new workflows, and business impact.
Enthought experts have tools and techniques for building customized scientific software for all parts of the energy business. Common themes are:
Data Wrangling and Consolidation: Consistent, accessible, shared, machine readable, and located to enable efficient computations (e.g. cloud).
Visualization and Image Analysis: Efficiently accessed and integrated data to generate insights to inform and prioritize deeper analysis.
Modeling and Simulation: Computational models to replace or guide physical experiments while generating significantly more data to enable AI/Machine Learning.
Predictive Tools (AI and Machine Learning): Computational models remove drudgery from the work of experts and increase the quality and consistency of results.
CanopyGeo Environment Gallery
Scroll to see examples of how the CanopyGeo environment enables Enthought scientific software craftsmen to quickly prototype and test ideas, iterating with clients to create customized software development roadmaps.
CanopyGeo environment provides novel capabilities to co-visualize data spanning multiple geoscience disciplines, significantly reducing the limitations of domain-specific softwares to accelerate innovative analysis and research method prototyping.
This screen shows a composite set of seismic data, horizons, and well paths in a single display
Extensible and Flexible
The CanopyGeo environment enables Enthought experts to add new data types, editors, menu entries, and menus to build custom algorithms, automate cross-domain workflows, and explore multi-domain visualization tools. Entought scientists 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 new data file formats and CanopyGeo data objects can be added and accessible to its various tools. With Python scripts and the object model, the platform can be extended to handle additional data file formats.
Enthought experts experiment with new workflows through display and edit of well
The CanopyGeo environment provides Enthought experts with access to the scientific Python ecosystem for custom data processing, interactive analyses, and advanced plotting and visualization.
CanopyGeo includes an IPython interactive command line console for inspecting and processing data objects. This enables dragging a SEG-Y data object into the console, printing its header, editing its data and running advanced analysis or data processing scripts on it.
The CanopyGeo advanced editor enables writing 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, scripts are dragged to open. Experts can, find context sensitive help, use auto-completion and right-click to run the script in the IPython console.
Data can be visualized in 2D using a map view, or along a traverse (inline, crossline, or user-defined)