Dates: Thursday, August 25, 2016, 1:00-1:45 PM CT or Wednesday, August 31, 2016, 9:00-9:45 AM CT / 3:00-3:45 PM BT
Register now (if you can’t attend, we’ll send you a recording)
LabVIEW is a software platform made by National Instruments, used widely in industries such as semiconductors, telecommunications, aerospace, manufacturing, electronics, and automotive for test and measurement applications. Earlier this month, Enthought released the Python Integration Toolkit for LabVIEW, which is a “bridge” between the LabVIEW and Python environments.
Examples of how you can extend LabVIEW with Python, including using Python for signal and image processing, cloud computing, web dashboards, machine learning, and more
Quickly and efficiently access scientific and engineering tools for signal processing, machine learning, image and array processing, web and cloud connectivity, and much more. With only minimal coding on the Python side, this extraordinarily simple interface provides access to all of Python’s capabilities.
High-level, general purpose programming language ideally suited to the needs of engineers, scientists, and analysts
Huge, international user base representing industries such as aerospace, automotive, manufacturing, military and defense, research and development, biotechnology, geoscience, electronics, and many more
Tens of thousands of available packages, ranging from advanced 3D visualization frameworks to nonlinear equation solvers
Simple, beginner-friendly syntax and fast learning curve
Whether you are a data scientist, quantitative analyst, or an engineer, or if you are evaluating consumer purchase behavior, stock portfolios, or design simulation results, your data analysis workflow probably looks a lot like this:
Acquire > Wrangle > Analyze and Model > Share and Refine > Publish
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.
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):
We posted a recording of a 30 minute webinar that we did on the 20th that covers what Canopy is and why we developed it. There’s a few minutes of Brett Murphy(Product Manager at Enthought) discussing the “why” with some slides, and then Jason McCampbell (Development Manager for Canopy) gets into the interesting part with a 15+ minute demo of some of the key capabilities and workflows in Canopy. If you would like to watch the recorded webinar, you can find it here (the different formats will play directly in different browsers so check them and you won’t have to download the whole recording first):
Summed up in one line: Canopy provides the minimal set of tools for non-programmers to access, analyze and visualize data in an open-source Python environment.
The challenge in the past for scientists, engineers and analysts who wanted to use Python had been pulling together a working, integrated Python environment for scientific computing. Finding compatible versions of the dozens of Python packages, compiling them and integrating it all was very time consuming. That’s why we released the Enthought Python Distribution (EPD) many years back. It provided a single install of all the major packages you needed to do scientific and analytic computing with Python.
But the primary interface for a user of EPD was the command line. For a scientist or analyst used to an environment like MATLAB or one of the R IDEs, the command line is a little unapproachable and makes Python challenging to adopt. This is why we developed Canopy.
Enthought Canopy is both a Python distribution (like EPD) and an analysis environment. The analysis environment includes an integrated editor and IPython prompt to faciliate script development & testing and data analysis & plotting. The graphical package manager becomes the main interface to the Python ecosystem with its package search, install and update capabilities. And the documentation browser makes online documentation for Canopy, Python and the popular Python packages available on the desktop.
Check out the Canopy demo in the recorded webinar (link above). We hope it’s helpful.
We’ll be hosting two webinars this month, covering user interfaces with Traits and SciPy India 2009. Hope to see you there!
How do I…Build a user interface with Traits?
Friday December 4
1pm CST/ 7pm UTC Wait list (non-subscribers) Subscribe to EPD (guaranteed seat)
The Traits package is at the center of all development we do at Enthought and has changed the mental model we use for Python programming. In this week’s webinar, we’ll have an in-depth look at how we can use Traits to build user interfaces for scientific applications.
Scientific Computing with Python Webinar:
Summary of SciPy India
Friday December 18
1pm CST/ 7pm UTC Register at GoToMeeting
In a couple of weeks, Enthought President Travis Oliphant will head off to Kerala, India to speak at SciPy India 2009. Due to a training engagement, Travis missed SciPy for the first time this summer, so he’s excited for this additional opportunity to meet and collaborate with the scientific Python community. Speakers at the event include Jarrod Millman, David Cournapeau, Christopher Burns, Prabhu Ramachandran, and Asokan Pichai a great group. We’re looking forward to hearing Travis’ review of the proceedings.
It looks like it’s time for our mid-month Scientific Computing with Python webinar! This month’s topic is sure to prove very useful for many of you data analysts: Interpolation with NumPy and SciPy.
In many data-processing scenarios it is necessary to use a discrete set of available data-points to infer the value of a function at a new data-point. One approach to this problem is interpolation, which constructs a new model-function that goes through the original data-points. There are many forms of interpolation (polynomial, spline, kriging, radial basis function, etc.), and SciPy includes some of these interpolation forms. This webinar will review the interpolation modules available in SciPy and in the larger Python community and provide instruction on their use via example.