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

Apr 29 2014 Published by under Enthought Canopy

Enthought Canopy Product Page | Download Enthought Canopy

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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Implied Volatility with Python’s Pandas Library AND Python in Excel

Mar 31 2014 Published by under General

Authors: Brett Murphy and Aaron Waters

The March 6 New York Quantitative Python User’s Group (NY QPUG) Meetup included presentations by NAG (Numerical Algorithms Group), known for its high quality numerical computing software and high performance computing (HPC) services, and Enthought, a provider of scientific computing solutions powered by Python.

Brian Spector, a technical consultant at NAG, presented “Implied Volatility using Python’s Pandas Library.” He covered a technique and script for calculating implied volatility for option prices in the Black–Scholes formula using Pandas and nag4py. With this technique, you can determine for what volatility the Black–Scholes equation price equals the market price. This volatility is then denoted as the implied volatility observed in the market. Brian fitted varying degrees of polynomials to the volatility curves, then examined the volatility surface and its sensitivity with respect to the interest rate. See the full presentation in the video below:

Brian Spector of NAG demonstrates a technique and script for calculating Implied Volatility using Python’s Pandas Library at the March 2014 NYQPUG Meetup.

Implied Volatility Plot

An interactive Implied Volatility plot with Numpy, Pandas, Chaco, Matplotlib and nag4py

Then Aaron Watters, scientific software developer at Enthought, presented an overview of replacing VBA with Python in Excel using the PyXLL package. Instead of uncontrolled spreadsheet versions spreading across an organization, PyXLL allows you to load centrally-managed Python code and execute it in Excel, giving you the full breadth and power of the Python analytic computing ecosystem within the familiar user interface of Excel. Aaron showed a demo of a tool in Excel where he could browse his disk usage graphically.

Enthought: Chaco GUI in Excel

Chaco GUI running in Excel with data recalculating live in the spreadsheet

For those looking to get their latest Python models and algorithms out to Excel users, PyXLL greatly streamlines the process. See Aaron’s full demo of the functionality below:

Aaron Watters of Enthought presented an overview of replacing VBA with Python for Excel with PyXLL at the March 2014 NYQPUG Meetup.

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