Category Archives: SciPy

Webinar: Python for Scientists & Engineers: A Tour of Enthought’s Professional Training Course

What:  A guided walkthrough and Q&A about Enthought’s technical training course Python for Scientists & Engineers with Enthought’s VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for scientific and engineering applications

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“Writing software is not my job…I just have to do it every day.”  
-21st Century Scientist or Engineer

Many scientists, engineers, and analysts today find themselves writing a lot of software in their day-to-day work even though that’s not their primary job and they were never formally trained for it. Of course, there is a lot more to writing software for scientific and analytic computing than just knowing which keyword to use and where to put the semicolon.

Software for science, engineering, and analysis has to solve the technical problem it was created to solve, of course, but it also has to be efficient, readable, maintainable, extensible, and usable by other people — including the original author six months later!

It has to be designed to prevent bugs and — because all reasonably complex software contains bugs — it should be designed so as to make the inevitable bugs quickly apparent, easy to diagnose, and easy to fix. In addition, such software often has to interface with legacy code libraries written in other languages like C or C++, and it may benefit from a graphical user interface to substantially streamline repeatable workflows and make the tools available to colleagues and other stakeholders who may not be comfortable working directly with the code for whatever reason.

Enthought’s Python for Scientists and Engineers is designed to accelerate the development of skill and confidence in addressing these kinds of technical challenges using some of Python’s core capabilities and tools, including:

  • The standard Python language
  • Core tools for science, engineering, and analysis, including NumPy (the fast array programming package), Matplotlib (for data visualization), and Pandas (for data analysis); and
  • Tools for crafting well-organized and robust code, debugging, profiling performance, interfacing with other languages like C and C++, and adding graphical user interfaces (GUIs) to your applications.

In this webinar, we give you the key information and insight you need to evaluate whether Enthought’s Python for Scientists and Engineers course is the right solution to take your technical skills to the next level, including:

  • Who will benefit most from the course
  • A guided tour through the course topics
  • What skills you’ll take away from the course, how the instructional design supports that
  • What the experience is like, and why it is different from other training alternatives (with a sneak peek at actual course materials)
  • What previous course attendees say about the course

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michael_connell-enthought-vp-trainingPresenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT


Python for Scientists & Engineers Training: The Quick Start Approach to Turbocharging Your Work

If you are tired of running repeatable processes manually and want to (semi-) automate them to increase your throughput and decrease pilot error, or you want to spend less time debugging code and more time writing clean code in the first place, or you are simply tired of using a multitude of tools and languages for different parts of a task and want to replace them with one comprehensive language, then Enthought’s Python for Scientists and Engineers is definitely for you!

This class has been particularly appealing to people who have been using other tools like MATLAB or even Excel for their computational work and want to start applying their skills using the Python toolset.  And it’s no wonder — Python has been identified as the most popular coding language for five years in a row for good reason.

One reason for its broad popularity is its efficiency and ease-of-use. Many people consider Python more fun to work in than other languages (and we agree!). Another reason for its popularity among scientists, engineers, and analysts in particular is Python’s support for rapid application development and extensive (and growing) open source library of powerful tools for preparing, visualizing, analyzing, and modeling data as well as simulation.

Python is also an extraordinarily comprehensive toolset – it supports everything from interactive analysis to automation to software engineering to web app development within a single language and plays very well with other languages like C/C++ or FORTRAN so you can continue leveraging your existing code libraries written in those other languages.

Many organizations are moving to Python so they can consolidate all of their technical work streams under a single comprehensive toolset. In the first part of this class we’ll give you the fundamentals you need to switch from another language to Python and then we cover the core tools that will enable you to do in Python what you were doing with other tools, only faster and better!

Additional Resources

Upcoming Open Python for Scientists & Engineers Sessions:

Albuquerque, NM, Sept 11-15, 2017
Washington, DC, Sept 25-29, 2017
Los Alamos, NM, Oct 2-6, 2017
Cambridge, UK, Oct 16-20, 2017
San Diego, CA, Oct 30-Nov 3, 2017
Albuquerque, NM, Nov 13-17, 2017
Los Alamos, NM, Dec 4-8, 2017
Austin, TX, Dec 11-15, 2017

Have a group interested in training? We specialize in group and corporate training. Contact us or call 512.536.1057.

Learn More

Download Enthought’s Machine Learning with Python’s Scikit-Learn Cheat Sheets
Enthought's Machine Learning with Python Cheat Sheets
Additional Webinars in the Training Series:

Python for Data Science: A Tour of Enthought’s Professional Technical Training Course

Python for Professionals: The Complete Guide to Enthought’s Technical Training Courses

An Exclusive Peek “Under the Hood” of Enthought Training and the Pandas Mastery Workshop

Download Enthought’s Pandas Cheat SheetsEnthought's Pandas Cheat Sheets

SciPy 2017 Conference to Showcase Leading Edge Developments in Scientific Computing with Python

Renowned scientists, engineers and researchers from around the world to gather July 10-16, 2017 in Austin, TX to share and collaborate to advance scientific computing tool


AUSTIN, TX – June 6, 2017 –
Enthought, as Institutional Sponsor, today announced the SciPy 2017 Conference will be held July 10-16, 2017 in Austin, Texas. At this 16th annual installment of the conference, scientists, engineers, data scientists and researchers will participate in tutorials, talks and developer sprints designed to foster the continued rapid growth of the scientific Python ecosystem. This year’s attendees hail from over 25 countries and represent academia, government, national research laboratories, and industries such as aerospace, biotechnology, finance, oil and gas and more.

“Since 2001, the SciPy Conference has been a highly anticipated annual event for the scientific and analytic computing community,” states Dr. Eric Jones, CEO at Enthought and SciPy Conference co-founder. “Over the last 16 years we’ve witnessed Python emerge as the de facto open source programming language for science, engineering and analytics with widespread adoption in research and industry. The powerful tools and libraries the SciPy community has developed are used by millions of people to advance scientific inquest and innovation every day.”

Special topical themes for this year’s conference are “Artificial Intelligence and Machine Learning Applications” and the “Scientific Python (SciPy) Tool Stack.” Keynote speakers include:

  • Kathryn Huff, Assistant Professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign  
  • Sean Gulick, Research Professor at the Institute for Geophysics at the University of Texas at Austin
  • Gaël Varoquaux, faculty researcher in the Neurospin brain research institute at INRIA (French Institute for Research in Computer Science and Automation)

In addition to the special conference themes, there will also be over 100 talk and poster paper speakers/presenters covering eight mini-symposia tracks including: Astronomy; Biology, Biophysics, and Biostatistics; Computational Science and Numerical Techniques; Data Science; Earth, Ocean, and Geo Sciences; Materials Science and Engineering; Neuroscience; and Open Data and Reproducibility.

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Webinar – Python for Professionals: The Complete Guide to Enthought’s Technical Training Courses

View the Python for Professionals Webinar

What: Presentation and Q&A with Dr. Michael Connell, VP, Enthought Training Solutions
Who Should Watch: Anyone who wants to develop proficiency in Python for scientific, engineering, analytic, quantitative, or data science applications, including team leaders considering Python training for a group, learning and development coordinators supporting technical teams, or individuals who want to develop their Python skills for professional applications

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Python is an uniquely flexible language – it can be used for everything from software engineering (writing applications) to web app development, system administration to “scientific computing” — which includes scientific analysis, engineering, modeling, data analysis, data science, and the like.

Unlike some “generalist” providers who teach generic Python to the lowest common denominator across all these roles, Enthought specializes in Python training for professionals in scientific and analytic fields. In fact, that’s our DNA, as we are first and foremost scientists, engineers, and data scientists ourselves, who just happen to use Python to drive our daily data wrangling, modeling, machine learning, numerical analysis, simulation, and more.

If you’re a professional using Python, you’ve probably had the thought, “how can I be better, smarter, and faster in using Python to get my work done?” That’s where Enthought comes in – we know that you don’t just want to learn generic Python syntax, but instead you want to learn the key tools that fit the work you do, you want hard-won expert insights and tips without having to discover them yourself through trial and error, and you want to be able to immediately apply what you learn to your work.

Bottom line: you want results and you want the best value for your invested time and money. These are some of the guiding principles in our approach to training.

In this webinar, we’ll give you the information you need to decide whether Enthought’s Python training is the right solution for your or your team’s unique situation, helping answer questions such as:

  • What kinds of Python training does Enthought offer? Who is it designed for? 
  • Who will benefit most from Enthought’s training (current skill levels, roles, job functions)?
  • What are the key things that make Enthought’s training different from other providers and resources?
  • What are the differences between Enthought’s training courses and who is each one best for?
  • What specific skills will I have after taking an Enthought training course?
  • Will I enjoy the curriculum, the way the information is presented, and the instructor?
  • Why do people choose to train with Enthought? Who has Enthought worked with and what is their feedback?

We’ll also provide a guided tour and insights about our our five primary course offerings to help you understand the fit for you or your team:

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michael_connell-enthought-vp-training

Presenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT


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Webinar: Work Better, Smarter, and Faster in Python with Enthought Training on Demand

Join Us For a Webinar

Enthought Training on Demand Webinar

We’ll demonstrate how Enthought Training on Demand can help both new Python users and experienced Python developers be better, smarter, and faster at the scientific and analytic computing tasks that directly impact their daily productivity and drive results.

View a recording of the Work Better, Smarter, and Faster in Python with Enthought Training on Demand webinar here.

What You’ll Learn

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Exploring NumPy/SciPy with the “House Location” Problem

Author: Aaron Waters

I created a Notebook that describes how to examine, illustrate, and solve a geometric mathematical problem called “House Location” using Python mathematical and numeric libraries. The discussion uses symbolic computation, visualization, and numerical computations to solve the problem while exercising the NumPy, SymPy, Matplotlib, IPython and SciPy packages.

I hope that this discussion will be accessible to people with a minimal background in programming and a high-school level background in algebra and analytic geometry. There is a brief mention of complex numbers, but the use of complex numbers is not important here except as “values to be ignored”. I also hope that this discussion illustrates how to combine different mathematically oriented Python libraries and explains how to smooth out some of the rough edges between the library interfaces.

http://nbviewer.ipython.org/urls/raw.github.com/awatters/CanopyDemoArchive/master/misc/house_locations.ipynb

Advanced Cython Recorded Webinar: Typed Memoryviews

Author: Kurt SmithWebinar_screenshot

Typed memoryviews are a new Cython feature for accessing memory buffers, such as NumPy arrays, without any Python overhead. This makes them very useful for manipulating blocks of memory in Cython directly without calling into the Python-C API.  Typed memoryviews have a clean declaration syntax and have a NumPy-like look and feel, supporting slicing, striding and indexing.

I go into more detail and provide some specific examples on how to use typed memoryviews in this webinar: “Advanced Cython: Using the new Typed Memoryviews”.

If you would like to watch the recorded webinar, you can find a link below (the different formats will play directly in different browsers so check to see which one works for you, and you won’t have to download the whole recording ahead of time):

For all you EPD Users: Canopy v1.1

EPD (Enthought Python Distribution) provided a simple install of Python for scientific computing on the major platforms: Windows, Linux and Mac-OS. Those looking for a clean, straightforward Python stack to unpack into a particular directory found EPD to be pretty ideal.

With the introduction of Enthought Canopy, we began addressing users who are more engineer or scientist than programmer and were much less familiar with command-line interfaces. The Canopy desktop (in the vein of MATLAB or Spyder) aims at these technical users who want to use Python, but more as an application or IDE. To implement the desktop in Python and to allow both it and a user-defined Python environment to co-exist and be separately updated, we used virtual environments. As a consequence Canopy can feel a bit foreign to EPD users. With 1.1 we have added a new command line interface (CLI) that will hopefully make EPD users feel more at home in Canopy while retaining many of the Canopy advantages such as in-place update and virtual environment support.

Now, EPD users who just want to use Canopy as a plain Python environment with their own tools or IDE can easily create one or more Python environments. For example, from the command line on Windows:

        Canopy_cli.exe setup C:\Python27

or on Linux:

        canopy_cli setup ~/canopy

The target directory can be any you choose. If you want to make this Python environment the default on your system, you can specify the –default switch, and Canopy will add the appropriate bin directory (Scripts directory on Windows) to your PATH environment variable. On Mac OS and Linux systems, Canopy does this by appending a line to your ~/.bash_profile file which activates the correct virtual environment. On Windows, this Python environment is also added to the system registry so third-party tools can correctly find it.

Since we use virtual environments, the installation layout for Canopy is different. With Canopy we install what is referred to as “Canopy Core”: the core Python environment and a minimum set of packages needed to bootstrap Canopy itself. With it we can lock down the Canopy environment, facilitate the automatic update mechanism, and provide reliable startup and fail-safe recovery. For the user, there is a different environment. This means when a Python update comes out, it is no longer necessary to install a whole new environment plus all of your packages and get everything working again. Instead, simply update Canopy and go back to working — all of your packages are still installed but Python has been upgraded.

To complete an install, Canopy creates two virtual environments named ‘System’ and ‘User’. System is where the Canopy GUI runs; no user code runs in this environment. Updates to this virtual environment are done via the Canopy update mechanisms. The User environment is where the kernel and all user code runs. This virtual environment is managed by Package Manager from the desktop or by enpkg from the command line; any packages can be updated and installed without fear of disrupting the GUI. Similarly, updates to the Canopy GUI will not affect packages installed in the User environment and break your code.

So why stick with virtual environments for an “EPD-like” install? One of the big challenges with the old, “flat” EPD installation method was updating an install, or trying out different package configurations. With virtual environments, you can create a new environment which inherits packages from another virtual environment, and try out a few package changes. When you are satisfied, it’s straightforward to throw away the experimentation area and make the changes to the original, stable virtual environment.

For more details, check out Creating an EPD-like Python environment in our online docs. And you can download Canopy v1.1 now.

Canopy v1.1 – Linux, Command Line Interface and More

Final-version-canopy-logo (1)

With version 1.1, Enthought Canopy now:

1) addresses, much more completely, the command line use cases that EPD users and IT managers expect from their Python distributions,

2) makes Linux support generally available,

3) streamlines installation for users without internet access with full, single-click installers,

4) supports multiple virtual environments for advanced users via “venv” backported to Python 2.7, and

5) provides updates like numpy 1.7.1, matplotlib 1.3.0 and more.

It’s been just over 4 months since Canopy v1.0 shipped with the new desktop analysis environment and our updated Python distribution for scientific computing. Canopy’s analysis environment seems to be well-received by users looking for a simpler GUI environment, but the Canopy graphical installation process left something to be desired by our EPD users.

Along with the Canopy desktop for users that don’t want to work directly from a command line, Canopy version 1.1 now provides command-line utilities that streamline the installation of a complete Python scientific stack for current EPD users who want to work from the shell or command line. In addition, IT groups or tools specialists that need to manage a central install of Python for a workgroup or department now have the tools they need to install and maintain Canopy. Version 1.1’s command-line installation and setup (and the 1-click, full installers detailed below) are much better for supporting Canopy installations on clusters as well.

Canopy for Linux is now fully released. We have full, tested support for RedHat5, CentOS5, and Ubuntu 12.04. Linux distros and versions beyond those work as well (anecdotally and based on some in-house use), but those are our tested versions.

With Canopy v1.0 we implemented a 2-step installation process. The installer includes the Canopy desktop, the Python packages needed by Canopy itself, and other core scientific Python stack packages for a minimal install (the libraries in Canopy Express). For those with a subscription, the second step requires downloading any additional packages using the Package Manager. This 2-step process is problematic for users that don’t have easy internet access or need to install centrally for a group. To help, we now provide full installers with all the Python packages we support included. This provides a streamlined 1-step install process for those who need it or want it.

To ensure users can install any package updates they wish without messing up package dependencies for Canopy itself, we use virtual environments under the hood. With v1.1 we now provide command-line access to our backport of “venv”. The new CLI provides utilities to create, upgrade, activate and deactivate your own virtual environments. Now its much easier to try out new Python environments or set up multiple configurations for a workgroup.

Canopy v1.1 ships many updates to packages and many new ones: OpenCV, LLVM, Bottleneck, gevent, msgpack, py, pytest, six, NLTK, Numba, Mock, patsy and more. You can see the full details on the Canopy Package Index page.

We hope you find version 1.1 useful!