Enthought download

Test automation is now enabled. Freescale needed a toolset for scripting their tests, queueing up jobs, and storing the information in a central database. The data requires flexible reporting and visualization to allow the user to diagnose problems, tied to the script that generated them.

Shown is a screen where users select a device to test. Ben Lasscock , Technical Lead for Energy Solutions, discusses a deep learning tool created to automate sequence stratigraphy the analysis of seismic images for deposition patterns of sediments.

Seismic interpretation requires the repetitive application of pattern and texture recognition of seismic images, informed by the geologic understanding of a skilled interpreter.

The problem of image segmentation and training data creation is common throughout science. Client experts provided guidance to Enthought throughout the development. The result was a cloud-based labelling toolkit which operates like a digital lab book, with every test recordable and reproducible.

A data processing pipeline was created for reading raw image data directly from the microscope output, inputting the data into trained models to segment and identify individual grains and porosity. Shown here is customized software created to provide an intuitive interface to visualize and navigate the multidimensional image stacks.

An expert labels individual grains, which are then used to train deep learning models for automated classification. These are a number of problems common to multiple industries being solved using Python, in which Enthought provides training.

Once the installation process is complete, the user is prompted with the option to launch the Canopy application after the setup exits alternative the application can be started by visiting the Windows Start menu and selecting Canopy.

The last step of a Canopy application install is to set up the first Python environment. The first time that the Canopy GUI is launched the application will prompt and guide you through the process of creating the first Python environment. The remainder of this section describes the standard GUI setup process.

However note that there are also two other ways to set up your Python environment:. When Canopy is launched for the first time, it will automatically configure your Python environment in the default location unless specified otherwise by a command-line option or a preference setting. This step allows each user on a multi-user machine to have his or her own local set of Python environments. Quick Start Topics. Mac OSX Installation. Navigation index next previous Canopy 2.

However note that there are also two other ways to set up your Python environment: For administrators and users who wish to set up and use Canopy without the GUI, i. Brendon Hall, part of Energy Solutions, used custom built software for the oilfield to study this fascinating core.

The results are now published. There are underlying lessons to be learned from considering the difference between a taxi company and Uber. Both have access to the same set of technologies: Mobile phones, mobile apps, credit card processing, GPS and turn-by-turn navigation.

The key difference is not in how the technologies are leveraged, but in the way each company is set up to take advantage of the possibilities that these technologies can provide. SciPy was co-founded by Enthought CEO Eric Jones in , when a group of about 50 like minded scientists gathered at Cal Tech, passionate about the potential of the Python scientific software stack to revolutionize the problem solving capabilities of scientists and engineers.

The virtual event attracted over 1, participants, almost double the Austin gathering of The full program will consist of 2 days of tutorials July , followed by 3 days of talks July , and ends with 2 days of developer sprints July Learn more here. Driving Applied Digital Innovation. Learn more and register to access the recording. They built the right AI tools and developed the right skills in our scientists.