Tag Archives: IT

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!

Avoiding “Excel Hell!” using a Python-based Toolchain

Update (Feb 6, 2014):  Enthought is now the exclusive distributor of PyXLL, a solution that helps users avoid “Excel Hell” by making it easy to develop add-ins for Excel in Python. Learn more here.

Didrik Pinte gave an informative, provocatively-titled presentation at the second, in-person New York Quantitative Python User’s Group (NY QPUG) meeting earlier this month.

There are a lot of examples in the press of Excel workflow mess-ups and spreadsheet errors contributing to some eye-popping mishaps in the finance world (e.g. JP Morgan’s spreadsheet issues may have led to the 2012 massive loss at “the London Whale”). Most of these can be traced to similar fundamental issues:

  • Data referencing/traceability

  • Numerical errors

  • Error-prone manual operations (cut & paste, …)

  • Tracing IO’s in libraries/API’s

  • Missing version control

  • Toolchain that doesn’t meet the needs of researchers, analysts, IT, etc.

Python, the coding language and its tool ecosystem, can provide a nice solution to these challenges, and many organizations are already turning to Python-based workflows in response. And with integration tools like PyXLL (to execute Python functions within Excel) and others, organizations can adopt Python-based workflows incrementally and start improving their current “Excel Hell” situation quickly.

For the details check out the video of Didrik’s NY QPUG presentation.  He demonstrates a an example solution using PyXLL and Enthought Canopy.

[vimeo 67327735 http://vimeo.com/67327735]

And grab the PDF of his slides here.


It would be great to hear your stories about “Excel Hell”. Let us know below.

–Brett Murphy