Author Archives: Tim Diller

avatar

About Tim Diller

Tim is Director of the IIoT Solutions Group at Enthought. Tim holds a Ph.D. in mechanical engineering, specializing in thermal and fluid sciences. He has worked in the automotive industry with emissions measurement, modeling, and control in addition to vehicle dynamics modeling and simulation. He also held a post-doctoral research position at the University of Texas, where he developed a numerical model and simulation of thermal transport processes in the laser sintering rapid-prototyping process at the LFF.

Enthought at National Instruments’ NIWeek 2017: An Inside Look

This week I had the distinct privilege of representing Enthought at National Instruments‘ 23rd annual user conference, NIWeek 2017. National Instruments is a leader in test, measurement, and control solutions, and we share many common customers among our global scientific and engineering user base.

NIWeek kicked off on Monday with Alliance Day, where my colleague Andrew Collette and I went on stage to receive the LabVIEW Tools Network 2017 Product of the Year Award for Enthought’s Python Integration Toolkit, which provides a bridge between Python and LabVIEW, allowing you to create VI’s (virtual instruments) that make Python function and object method calls. Since its release last year, the Python Integration Toolkit has opened up access to a broad range of new capabilities for LabVIEW users,  by combining the best of Python with the best of LabVIEW. It was also inspiring to hear about the advances being made by other National Instruments partners. Congratulations to the award winners in other categories (Wineman Technology, Bloomy, and Moore Good Ideas)!

On Wednesday, Andrew gave a presentation titled “Building and Deploying Python-Powered LabVIEW Applications” to a standing-room only crowd.  He gave some background on the relative strengths of Python and LabVIEW (some of which is covered in our March 2017 webinar “Using Python and LabVIEW to Rapidly Solve Engineering Problems“) and then showcased some of the capabilities provided by the toolkit, such as plotting data acquisition results live to a web server using plotly, which is always a crowd-pleaser (you can learn more about that in the blog post “Using Plotly from LabVIEW via Python”).  Other demos included making use of the Python scikit-learn library for machine learning, (you can see Enthought’s CEO Eric Jones run that demo here, during the 2016 NIWeek keynotes.)

Continue reading

Enthought Presents the Canopy Platform at the 2017 American Institute of Chemical Engineers (AIChE) Spring Meeting

by: Tim Diller, Product Manager and Scientific Software Developer, Enthought

Last week I attended the AIChE (American Institute of Chemical Engineers) Spring Meeting in San Antonio, Texas. It was a great time of year to visit this cultural gem deep in the heart of Texas (and just down the road from our Austin offices), with plenty of good food, sights and sounds to take in on top of the conference and its sessions.

The AIChE Spring Meeting focuses on applications of chemical engineering in industry, and Enthought was invited to present a poster and deliver a “vendor perspective” talk on the Canopy Platform for Process Monitoring and Optimization as part of the “Big Data Analytics” track. This was my first time at AIChE, so some of the names were new, but in a lot of ways it felt very similar to many other engineering conferences I have participated in over the years (for instance, ASME (American Society of Mechanical Engineers), SAE (Society of Automotive Engineers), etc.).

This event underscored that regardless of industry, engineers are bringing the same kinds of practical ingenuity to bear on similar kinds of problems, and with the cost of data acquisition and storage plummeting in the last decade, many engineers are now sitting on more data than they know how to effectively handle.

What exactly is “big data”? Does it really matter for solving hard engineering problems?

Continue reading

New Year, New Enthought Products!

We’ve had a number of major product development efforts underway over the last year, and we’re pleased to share a lot of new announcements for 2017:

A New Chapter for the Enthought Python Distribution (EPD):
Python 3 and Intel MKL 2017

In 2004, Enthought released the first “Python: Enthought Edition,” a Python package distribution tailored for a scientific and analytic audience. In 2008 this became the Enthought Python Distribution (EPD), a self-contained installer with the "enpkg" command-line tool to update and manage packages. Since then, over a million users have benefited from Enthought’s tested, pre-compiled set of Python packages, allowing them to focus on their science by eliminating the hassle of setting up tools.

Enthought Python Distribution logo

Fast forward to 2017, and we now offer over 450 Python packages and a new era for the Enthought Python Distributionaccess to all of the packages in the new EPD is completely free to all users and includes packages and runtimes for both Python 2 and Python 3 with some exciting new additions. Our ever-growing list of packages includes, for example, the 2017 release of the MKL (Math Kernel Library), the fruit of an ongoing collaboration with Intel.

The New Enthought Deployment Server:
Secure, Onsite Access to EPD and Private Packages

enthought-deployment-server-centralized-management-illustration-v2

Continue reading