William E. Hart's Blog

Here's a nice discussion and comparison of Python plugin frameworks that I ran across today: Design Docs Plugins - PiTiViWiKi . This notes that a big difference between Zope and Trac plugins is that Zope defines interfaces which allows for checking interface implementation/definition, as well as facilities for plugin adapters. In this respect, the Envisage Core plugins are similar to Zope.

Updated to include pointers to the PyUtilib Component Architecture and PnP. Various Python projects I am working on could benefit from the use of a Plug-in framework.  However, there does not appear to be a standard Python plug-in framework, though there are some mature packages that support plug-ins. Here's a summary of my recent web research: yapsy - This is a simple plug-in framework that was designed specifically to support plug-ins with no external dependencies. Mary Alchin describes a simple plugin framework , with a similar goal.  His classes provide an API for the plugins, with few supporting features (e.g. searching for plugins). André Roberge has a series of posts that describe the application of plugins to refactor a simple calculator application.  The goal of this is to illustrate the requirements for plugin frameworks, with the goal of identifying best practices for plugins. There are some interesting replies to this post, which consider implementations of the

It does not take much to add a lambda function to a Python class. For example, consider the following: >>> class A: pass >>> f = lambda self,x:x >>> setattr(A,"f",f) This code adds the method f to class A . For example: >>> a=A() >>> a.f(1) 1 However, the f method created this way does not have the expected Python name: >>> A.f.__name__ ' ' Further, defining this value is not possible; the instancemethod f does not have a __name__ attribute. The trick is to name the lambda function before defining the class method: >>> class A: pass >>> f = lambda self,x:x >>> f.__name__ = "f" >>> setattr(A,"f",f) >>> f.__name__ 'f' This is simple, but it took too long to figure this out...

Consider the following use of the standard Unix time command: /usr/bin/time ls -R / If the SIGTERM signal is sent to the time process, then the ls process will continue! This is an unexpected behavior, which is not well-documented. Normally, this is not much of an issue; the process that is monitored will simply terminate quietly. However, when the time utility is used in interactive applications, process interrupts can lead to many unexpected rogue processes. The timer command is a modification of the UNIX timing utility that behaves as expected. When using timer , the SIGTERM signal is sent to the process, which terminates it as expected. The timer command is available in the UTILIB software library, but it can be compiled independently.

There are many tools available that can be used to monitor memory usage in computer programs. However, there are few tools that can be applied to monitor the memory usage of a specific process in an automated manner. Most memory monitoring tools provide a gui that a user can monitor. However, these tools are not useful in contexts where memory must be monitored repeatedly. For example, automated software tests may require checks to validate that the memory usage does not exceed expected limits. The memmon command is a new memory monitoring tool that is included in the UTILIB software library. memmon provides a convenient mechanism to report the maximum amount of memory that a process uses. The memmon command requires the absolute path to the command that will be executed. Beyond that, its default syntax is quite simple: $ ./memmon /bin/sleep 1 53768 Kb used The memmon command can also be used to terminate a process whose memory exceeds a specified threshold: $ ./memmon -k 10 /bin/

I have not blogged much this fall because I have been busy managing a variety of software releases.  I plan to include further details in upcoming blogs, but I thought I would summarize these releases here: acro 2.0 - Acro is A Common Repository for Optimizers that integrates a rich variety of optimization libraries and solvers that have been developed for large-scale engineering and scientific applications. Acro was developed to facilitate the design, development, integration and support of optimization software libraries. Thus, Acro includes both individual optimization solvers as well as optimization frameworks that provide abstract interfaces for flexible interoperability of solver components. Furthermore, many solvers included in Acro can exploit parallel computing resources to solve optimization problems more quickly. utilib 4.0 - Utilib is a library of general-purpose C++ utilities, similar in spirit to the Boost libraries. While generally treated as an Acro package, Utilib i