Archive for the 'Enthought Canopy' category

Enthought Canopy 1.4 Released: Includes New Canopy-Configured Command Prompt

Apr 29 2014 Published by under Enthought Canopy

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Enthought Canopy Update AvailableEnthought Canopy 1.4 is now available! Users can easily update to this latest version by clicking on the green “Update available” link at the bottom right of the Canopy intro screen window or by going to Help > Canopy Application Updates within the application.

Key additions in this release are a Canopy-configured command prompt, inclusion of new packages in the full installer utilized by IT groups and users running from disconnected networks, and continued stability upgrades. We’ve also updated or added over 50 supported packages in Canopy’s Package Manager on a continual basis since the v.1.3 release. See the full release notes and the full list of currently available Canopy packages.

New Canopy-Configured Command Prompt

Enthought Canopy Command PromptAn important usability feature added in Enthought Canopy 1.4 is a Canopy-configured command prompt available from the Canopy Editor window on all platforms via Tools > Command Prompt. When selected, this opens a Command Prompt (Windows) or Terminal (Linux, Mac OS) window pre-configured with the correct environment settings to use Canopy’s Python installation from the command line. This avoids having to modify your login environment variables. In particular, on Windows when using standard (ie, non-administrative) user accounts it can be difficult to override some system settings.

Full Installers Updated with New Packages

The full installers have been updated with many new packages as well. The following packages are now bundled in the full installers: atom, boto, fiona, flake8, kiwisolver, mccabe, NLTK, pandsql, patsy, pyephem, pyodbc, pyshp, pysal, sqlparse, and traits_enaml.

All of these packages are also available via Canopy’s Package Manager for on-demand installation; the full installers provide bundled access to these packages for IT groups performing large installations and for users running from disconnected networks. See the Enthought Canopy Package Index for additional information on these and other bundled packages.

Over 50 Supported Canopy Packages Updated or Added

Since the Canopy v1.3 release we have updated or added over 50 supported Canopy packages. Highlighted new additions include: cartopy, Python Tools for Visual Studio (PTVS), blosc/libblosc, and GDAL with hdf5 support. Key packages with updates include: ipython 2.0, pandas, pysal, pyzmq/zmq, enaml, and requests.

Windows Installation Note: we uncovered one issue during final testing that impacts Windows users who will be updating both the 32- and 64-bit Canopy versions on the same machine. If you have both versions installed, please see this Knowledge Base article.

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PyXLL: Deploy Python to Excel Easily

Feb 06 2014 Published by under Canopy, Enthought Canopy, Finance, News, Python, PyXLL

PyXLL Solution Home | Buy PyXLL | Press Release

Today Enthought announced that it is now the worldwide distributor for PyXLL, and we’re excited to offer this key product for deploying Python models, algorithms and code to Excel. Technical teams can use the full power of Enthought Canopy, or another Python distro, and end-users can access the results in their familiar Excel environment. And it’s straightforward to set up and use.

Installing PyXLL from Enthought Canopy

PyXLL is available as a package subscription (with significant discounts for multiple users). Once you’ve purchased a subscription you can easily install it via Canopy’s Package Manager as shown in the screenshots below (note that at this time PyXLL is only available for Windows users). The rest of the configuration instructions are in the Quick Start portion of the documentation. PyXLL itself is a plug-in to Excel. When you start Excel, PyXLL loads into Excel and reads in Python modules that you have created for PyXLL. This makes PyXLL especially useful for organizations that want to manage their code centrally and deploy to multiple Excel users.

Enthought Canopy Package Manager   Install PyXLL from Enthought Canopy's Package Manager

Creating Excel Functions with PyXLL

To create a PyXLL Python Excel function, you use the @xl_func decorator to tell PyXLL the following function should be registered with Excel, what its argument types are, and optionally what its return type is. PyXLL also reads the function’s docstring and provides that in the Excel function description. As an example, I created a module my_pyxll_module.py and registered it with PyXLL via the PyXLL config file. In that module I put a simple function pyfib(): a naive Fibonacci implementation.

When I start Excel, I can access the Excel function wizard and find my pyfib() function and use it. The function documentation in Excel comes from my docstring. PyXLL parses the “n: integer input” portion as the variable documentation.

If I go back and make a change to the function, I can reload PyXLL without restarting Excel and update the cells. If I add another function to my module, it too will get loaded and be available to use in my worksheet.

So if you are developing Python models or functions for a large number of distributed Excel users, you can manage the code centrally. PyXLL will load new versions and new functions from the central repository whenever a user starts Excel. Deployment is very straightforward, and central management of all the code reduces the risk of Excel macros and functions proliferating uncontrolled.

I can also create menu functions using the decorator @xl_menu. PyXLL ships with several examples that you can start with. The one below adds a menu item to the Excel Add-in menu, and pops up a message box when selected.

       

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