CMFProjman is being ported to Plone2
CMFProjman has been asleep for quite a while, and is now being reanimated to work with Plone2. We will release it as soon as we see it's stable.
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Blog entries
Reinteract: un outil intéressant pour faire du numpy/scipy
Il existe un outil, Reinteract, qui permet d'avoir une sorte de d'éditeur/shell Python, où l'on peut aisément modifier et réinterpreter une ligne de code.
Sachant qu'il sait aussi afficher des plots, etc, il est possible de s'en servir avantageusement pour faire des sessions Matlab-like.
Je pense donc que c'est un outil à présenter à nos chers apprenants qui sont intéressés par le couple python/numpy comme substitut à Matlab ©®.
Ex:
écrit par David Douard
Testing for NaN without depending on Numpy
How can I test if a python float is "not a number" without depending on numpy? Simple, a nan value is different to any other value, including itself:
def isnan(x): return isinstance(x, float) and x!=xPrésentation PyCON FR 2008 - Assurance qualité
Une présentation sur l'assurance-qualité a été présentée le 17 mai 2008 pour les journées Python organisées par l'Association Francophone Python (AFPy).
Le but visé est de décrire quelques notions et pratiques simples pour améliorer la lisibilité et la maintenabilité de votre code python.
Quelques outils standards de python sont décrits en première partie; pour finir par une revue de projets plus ambitieux mais indispensables pour la création de code de qualité.
Photo sous licence creative commons By-Nc-Nd par : yota
Pour accéder au diaporama :
http://fr.pycon.org/presentations_2008/julien-jehannet-assurance-qualite/slides.html
New apycot release
After almost 2 years of inactivity, here is a new release of apycot the "Automated Pythonic Code Tester". We use it everyday to maintain our software quality, and we hope this tool can help you as well.
Admittedly it's not trivial to setup, but once it's running you'll be able to count on it. We're working on getting it to work "out-of-the-box"...
Here's what's in the ChangeLog :
- 2008-05-19 -- 0.11.0
- updated documentation
- new pylintrc option for the pyhton_lint checker.
- Added code to disabled checker with missing required option with the proper ERROR statut
- removed the catalog option of the xml_valid checker this feature can now be handle with the XML_CATALOG_FILE environement variable (see libxml2 doc for details)
- moved xml tool from python-xml to lxml
- new 'hourly' mode for running tests
- new 'test_activity_report' report
- pylint checker support new disable_msg and show_categories options (show_categories default to Error and Fatal categories to avoid reports polution)
- activity option "days" has been renamed to "time" and correspond to a number of day in daily mode but to a number of hour in hourly mode
- fixed debian_lint and debian_piuparts to actually do something...
- fixed docutils checker for recent docutils versions
- dropped python 2.2/2.3 compat (to run apycot itself)
- added output redirectors to the debian preprocessor to avoid parsing errors
- can use regular expressions in <pp>_match_* options
LAX - Logilab Appengine eXtension is a full-featured web application framework running on AppEngine
LAX version 0.3.0 was released today, see http://lax.logilab.org/
Get a new application running in ten minutes with the install guide and the tutorial:
Enjoy!
Update: LAX is now included in the CubicWeb semantic web framework.
Nouvelle version de LAX - Logilab Appengine eXtension
La version 0.3.0 de LAX est sortie aujourd'hui voir : http://lax.logilab.org/
Il suffit de 10 petites minutes pour avoir une application qui tourne, suivez le guide :
Mise à jour: LAX est maintenant inclus dans CubicWeb.
apycot 0.12.1 released
After one month of internship at logilab, I'm pleased to announce the 0.12.1 release of apycot.
for more information read the apycot 0.12.1 release note
You can also check the new sample configuration.
Pierre-Yves David
Windows, fichiers ouverts et tests unitaires
Un problème rencontré hier : un test unitaire plante sous Windows, après avoir créé un objet qui garde des fichiers ouverts. le tearDown du test est appelé, mais il plante car Windows refuse de supprimer des fichiers ouverts, et le framework de test garde une référence sur la fonction de test pour qu'on puisse examiner la pile d'appels. Sous Linux, pas de problème (on a le droit du supprimer du disque un fichier ouvert, et donc pas de soucis dans le teardown).
Quelques pistes pour contourner le problème:
- mettre le test dans un try...finally avec un del sur l'objet qui garde les fichiers ouverts dans le finally. Inconvénient : quand le test ne passe pas, pdb ne permet plus de voir grand chose
- au lieu de nettoyer dans le tearDown, nettoyer plus tard dans un atexit par exemple. Il faut voir comment ça se passe si plusieurs tests veulent écrire dans les mêmes fichiers (je pense qu'il faudrait un répertoire temporaire par test, si on veut pouvoir avoir plusieurs tests qui foirent et examiner leurs données, mais il faut tester pour être sûr)
- coller un try...except dans le tearDown autour de la suppression de chaque fichier, et mettre les fichiers qui posent problème dans une liste qui sera traitée à la sortie du programme (avec atexit par exemple).
Ça ressemble à du bricolage, mais on a un comportement de windows sur lequel on n'a pas de contrôle (même avec des privilèges Administrateur ou System, on ne peut pas contourner cette impossibilité de supprimer un fichier ouvert, à ma connaissance).
Une autre approche, nettement plus lourde, serait de virtualiser la création de fichiers pour travailler en mémoire (au minimum surcharger os.mkdir et le builtin open, voire dans le cas qui nous intéresse les modules qui travaillent avec des fichiers zip). Il y a peut-être des choses comme ça en circulation. Poser la question sur la liste TIP apportera peut-être des réponses (une rapide recherche dans les archives n'a rien donné).
Voir aussi ces enfilades de mars 2004 et novembre 2004 sur comp.lang.python.
Python for applied Mathematics
The presentation of Python as a tool for applied mathematics got highlighted at the 2008 annual meeting of the american Society for Industrial and Applied Mathematics (SIAM). For more information, read this blogpost and the slides.
Google Custom Search Engine, for Python
A Google custom search engine for Python has been made available by Gerard Flanagan, indexing:
Using refinements
To refine the search to any of the individual sites, you can specify a refinement using the following labels: stdlib, wiki, pypi, thehazeltree
So, to just search the python wiki, you would enter:
somesearchterm more:wiki
and similarly:
somesearchterm more:stdlib somesearchterm more:pypi somesearchterm more:thehazeltree
About http://thehazeltree.org
The Hazel Tree is a collection of popular Python texts that I have converted to reStructuredText and put together using Sphinx. It's in a publishable state, but not as polished as I'd like, and since I'll be mostly offline for the next month it will have to remain as it is for the present. However, the search engine is ready now and the clock is ticking on its subscription (one year, renewal depending on success of site), so if it's useful to anyone, it's all yours (and if you use it on your own site a link back to http://thehazeltree.org would be appreciated).
SciPy and TimeSeries
We have been using many different tools for doing statistical analysis with Python, including R, SciPy, specific C++ code, etc. It looks like the growing audience of SciPy is now in movement to have dedicated modules in SciPy (lets call them SciKits). See this thread in SciPy-user mailing-list.
Is the Openmoko freerunner a computer or a phone ?
The Openmoko Freerunner is a computer with embedded GSM, accelerometer and GPS. I got mine last week after waiting for a month for the batch to get from Taiwan to the french company I bought it from. The first thing I had to admit was that some time will pass before it gets confortable to use it as a phone. The current version of the system has many weird things in its user interface and the phone works, but the other end of the call suffers a very unpleasant echo.
I will try to install Debian, Qtopia and Om2008.8 to compare them. I also want to quickly get Python scripts to run on it and get back to Narval hacking. I had an agent running on a bulky Palm+GPS+radionetwork back in 1999 and I look forward to run on this device the same kind of funny things I was doing in AI research ten years ago.
gajim, dbus and wmii
I've been using for a long time a custom version of gajim in order to make it interact with wmii. More precisely, I have, in my wmii status bar, a dedicated log zone where I print notification messages such as new incoming emails or text received from gajim (with different colors if special words were cited, etc.).
I recently decided to throw away my custom gajim and use python and dbus to achieve the same goal in a cleaner way. A very basic version can be found in the simpled project. As of now, the only way to get the code is trhough mercurial:
hg clone http://www.logilab.org/hg/simpled
The source file is named gajimnotifier.py. In this file, you'll also find a version sending messages to Ion's status bar.
qgpibplotter is (hopefully) working
My latest personal project, pygpibtoolkit, holds a simple HPGL plotter trying to emulate the HP7470A GPIB plotter, using the very nice and cheap Prologix USB-GPIB dongle. This tool is (for now) called qgpibplotter (since it is using the Qt4 toolkit).
Tonight, I took (at last) the time to make it work nicely. Well, nicely with the only device I own which is capable of plotting on the GPIB bus, my HP3562A DSA.
Now, you just have to press the "Plot" button of your test equipment, and bingo! you can see the plot on your computer.
Converting excel files to CSV using OpenOffice.org and pyuno
The Task
I recently received from a customer a fairly large amount of data, organized in dozens of xls documents, each having dozens of sheets. I need to process this, and in order to ease the manipulation of the documents, I'd rather use standard text files in CSV (Comma Separated Values) format. Of course I didn't want to spend hours manually converting each sheet of each file to CSV, so I thought this would be a good time to get my hands in pyUno.
So I gazed over the documentation, found the Calc page on the OpenOffice.org wiki, read some sample code and got started.
The easy bit
The first few lines I wrote were (all imports are here, though some were actually added later).
import logging import sys import os.path as osp import os import time import uno def convert_spreadsheet(filename): pass def run(): for filename in sys.argv[1:]: convert_spreadsheet(filename) def configure_log(): logger = logging.getLogger('') logger.setLevel(logging.DEBUG) handler = logging.StreamHandler(sys.stdout) logger.addHandler(handler) format = "%(asctime)s %(levelname)-7s [%(name)s] %(message)s" handler.setFormatter(logging.Formatter(format)) if __name__ == '__main__': configure_log() run()That was the easy part. In order to write the convert_spreadsheet function, I needed to open the document. And to do that, I need to start OpenOffice.org.
Starting OOo
I started by copy-pasting some code I found in another project, which expected OpenOffice.org to be already started with the -accept option. I changed that code a bit, so that the function would launch soffice with the correct options if it could not contact an existing instance:
def _uno_init(_try_start=True): """init python-uno bridge infrastructure""" try: # Get the uno component context from the PyUNO runtime local_context = uno.getComponentContext() # Get the local Service Manager local_service_manager = local_context.ServiceManager # Create the UnoUrlResolver on the Python side. local_resolver = local_service_manager.createInstanceWithContext( "com.sun.star.bridge.UnoUrlResolver", local_context) # Connect to the running OpenOffice.org and get its context. # XXX make host/port configurable context = local_resolver.resolve("uno:socket,host=localhost,port=2002;urp;StarOffice.ComponentContext") # Get the ServiceManager object service_manager = context.ServiceManager # Create the Desktop instance desktop = service_manager.createInstance("com.sun.star.frame.Desktop") return service_manager, desktop except Exception, exc: if exc.__class__.__name__.endswith('NoConnectException') and _try_start: logging.info('Trying to start UNO server') status = os.system('soffice -invisible -accept="socket,host=localhost,port=2002;urp;"') time.sleep(2) logging.info('status = %d', status) return _uno_init(False) else: logging.exception("UNO server not started, you should fix that now. " "`soffice \"-accept=socket,host=localhost,port=2002;urp;\"` " "or maybe `unoconv -l` might suffice") raiseSpreadsheet conversion
Now the easy (sort of, once you start understanding the OOo API): to load a document, use desktop.loadComponentFromURL(). To get the sheets of a Calc document, use document.getSheets() (that one was easy...). To iterate over the sheets, I used a sample from the SpreadsheetCommon page on the OpenOffice.org wiki.
Exporting the CSV was a bit more tricky. The function to use is document.storeToURL(). There are two gotchas, however. The first one, is that we need to specify a filter, and to parameterize it correctly. The second one is that the CSV export filter is only able to export the active sheet, so we need to change the active sheet as we iterate over the sheets.
Parametrizing the export filter
The parameters are passed in a tuple of PropertyValue uno structures, as the second argument to the storeToURL method. I wrote a helper function which accepts any named arguments and convert them to such a tuple:
def make_property_array(**kwargs): """convert the keyword arguments to a tuple of PropertyValue uno structures""" array = [] for name, value in kwargs.iteritems(): prop = uno.createUnoStruct("com.sun.star.beans.PropertyValue") prop.Name = name prop.Value = value array.append(prop) return tuple(array)Now, what do we put in that array? The answer is in the FilterOptions page of the wiki : The FilterName property is "Text - txt - csv (StarCalc)". We also need to configure the filter by using the FilterOptions property. This is a string of comma separated values
- ASCII code of field separator
- ASCII code of text delimiter
- character set, use 0 for "system character set", 76 seems to be UTF-8
- number of first line (1-based)
- Cell format codes for the different columns (optional)
I used the value "59,34,76,1", meaning I wanted semicolons for separators, and double quotes for text delimiters.
Here's the code:
def convert_spreadsheet(filename): """load a spreadsheet document, and convert all sheets to individual CSV files""" logging.info('processing %s', filename) url = "file://%s" % osp.abspath(filename) export_mask = make_export_mask(url) # initialize Uno, get a Desktop object service_manager, desktop = _uno_init() try: # load the Document document = desktop.loadComponentFromURL(url, "_blank", 0, ()) controller = document.getCurrentController() sheets = document.getSheets() logging.info('found %d sheets', sheets.getCount()) # iterate on all the spreadsheets in the document enumeration = sheets.createEnumeration() while enumeration.hasMoreElements(): sheet = enumeration.nextElement() name = sheet.getName() logging.info('current sheet name is %s', name) controller.setActiveSheet(sheet) outfilename = export_mask % name.replace(' ', '_') document.storeToURL(outfilename, make_property_array(FilterName="Text - txt - csv (StarCalc)", FilterOptions="59,34,76,1" )) finally: document.close(True) def make_export_mask(url): """convert the url of the input document to a mask for the written CSV file, with a substitution for the sheet name >>> make_export_mask('file:///home/foobar/somedoc.xls') 'file:///home/foobar/somedoc$%s.csv' """ components = url.split('.') components[-2] += '$%s' components[-1] = 'csv' return '.'.join(components)A new way of distributing Python code ?
On distutils-sig, the question of distutils/setuptools replacing is frequently raised and a lot of effort is made to find what would be the best way to build and distribute python code.
I don't understand the reason why we have a massive coupling between build and distribution (setuptools and pypi to be more precise) and I'm not convinced about this "global" approach. I hope the python community will examine the possibility to change that and split the problem in two distinct projects.
One of the most successful ideas of Python is its power in extending other languages. And in fact, that's the major problem to solve for the build area. I'm pretty sure it will take a long time before obtaining a valuable (and widely adopted) solution and this is so complicated that the choice of the building chain should be kept under the responsibility of the upstream maintainers for now (distutils, setuptools, makefile, SCons, ...).
Concerning the distribution, here are the mandatory features I expect:
- installing source code managing dependencies with foreign contribution
- have binary builds without interaction with the primary host system
- be multi-platform agnostic (Linux, BSD, Windows, Mac, ...)
- clean upgrade/uninstall
- kind of sandboxes for testing and development mode
- no administrator privilege required
I found the http://0install.net project homepage and was really impressed by the tons of functionalities already available and the other numerous advantages, like:
- multiple version installation
- reuse external distribution effort (integrate deb, rpm, ...)
- digital signatures
- basic mirroring solution
- notification about software updates
- command line oriented but various GUI exist
- try to follow standards (XDG specifications on freedesktop.org))
I'm questioning seriously why this project could not be considered as a clean and build-independent python packages index system. Moreover, 0install has already some build capabilities (see 0compile) but the ultimate reason is that it will largely facilitate migrations when a new python build standard will emerge.
Conclusion
0install looks like a mature project driven by smart people and already included in modern distributions. I'll definitively give it a try soon.
Using branches in mercurial
The more we use mercurial to manage our code repositories, the more we enjoy its extended functionalities. Lately we've been playing and using branches which end up being very useful. We also use hgview instead of the built-in "hg view" command. And its latest release supports the branches functionality, you can filter out the branch you want to look at. Update your installation (apt-get upgrade ?) to enjoy this new functionality... or download it.
Belier - le ssh par hops
On vient de découvrir belier qui permet de se connecter facilement à des machines auquelles on doit accéder par des machines ssh intermédiaires. Ca peut s'avérer utile. En plus, c'est en python. En plus, il a fait des paquets debian... et en plus il mentionne pylint. Du coup il mérite mention ici.
new pylint / astng / common releases
I'm pleased to announce releases of pylint 0.18, logilab-astng 0.19 and logilab-common 0.39. All these packages should now be cleanly available through easy install.
Also, happy pylint users will get:
- fixed python 2.6 support (pylint/astng tested from 2.4 to 2.6)
- get source code (and so astng) for zip/egg imports
- some understanding of the property decorator and of unbound methods
- some false positives fixed and others minor improvments
See projects home page and ChangeLog for more information:
http://www.logilab.org/project/pylint http://www.logilab.org/project/logilab-astng http://www.logilab.org/project/logilab-common
Please report any problem / question to the python-projects@lists.logilab.org mailing-list.
Enjoy!
iclassmethod decorator to define both a class and an instance method in one go
You'll find in the logilab.common.decorators module the iclassmethod decorator which may be pretty handy in some cases as it allows methods to be both called as class methods or as instance methods. In the first case the first argument will be the class and the second case it will be the instance.
Example extracted (and adapted for simplicity) from CubicWeb:
from logilab.common.decorators import iclassmethod class Form(object): _fields_ = [] def __init__(self): self.fields = list(self._fields_) @iclassmethod def field_by_name(cls_or_self, name): """return field with the given name and role""" if isinstance(cls_or_self, type): fields = cls_or_self._fields_ else: fields = cls_or_self.fields for field in fields: if field.name == name: return field raise Exception('FieldNotFound: %s' % name)Example session:
>>> from logilab.common import attrdict >>> f = Form() >>> f.fields.append(attrdict({'name': 'something', 'value': 1}) >>> f.field_by_name('something') {'name': 'something', 'value': 1} >>> Form.field_by_name('something') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<stdin>", line 15, in field_by_name Exception: FieldNotFound: somethingSo we get a field_by_name method which will act differently (actually use different input data) when called as instance method or as class method.
Also notice the attrdict trick that can also be achieved with the Python 2.6 named tuple.
Reading SPE files
If you would like to read SPE files from charge-coupled device (CCD) cameras, I have contributed a recipe to the SciPy cookbook, see Reading SPE files.
EuroSciPy'09 (part 1/2): The Need For Speed
The EuroSciPy2009 conference was held in Leipzig at the end of July and was sponsored by Logilab and other companies. It started with three talks about speed.
Starving CPUs
In his keynote, Fransesc Alted talked about starving CPUs. Thirty years back, memory and CPU frequencies where about the same. Memory speed kept up for about ten years with the evolution of CPU speed before falling behind. Nowadays, memory is about a hundred times slower than the cache which is itself about twenty times slower than the CPU. The direct consequence is that CPUs are starving and spend many clock cycles waiting for data to process.
In order to improve the performance of programs, it is now required to know about the multiple layers of computer memory, from disk storage to CPU. The common architecture will soon count six levels: mechanical disk, solid state disk, ram, cache level 3, cache level 2, cache level 1.
Using optimized array operations, taking striding into account, processing data blocks of the right size and using compression to diminish the amount of data that is transfered from one layer to the next are four techniques that go a long way on the road to high performance. Compression algorithms like Blosc increase throughput for they strike the right balance between being fast and providing good compression ratios. Blosc compression will soon be available in PyTables.
Fransesc also mentions the numexpr extension to numpy, and its combination with PyTables named tables.Expr, that nicely and easily accelerates the computation of some expressions involving numpy arrays. In his list of references, Fransesc cites Ulrich Drepper article What every programmer should know about memory.
Using PyPy's JIT for science
Maciej Fijalkowski started his talk with a general presentation of the PyPy framework. One uses PyPy to describe an interpreter in RPython, then generate the actual interpreter code and its JIT.
Since PyPy is has become more of a framework to write interpreters than a reimplementation of Python in Python, I suggested to change its misleading name to something like gcgc the Generic Compiler for Generating Compilers. Maciej answered that there are discussions on the mailing list to split the project in two and make the implementation of the Python interpreter distinct from the GcGc framework.
Maciej then focused his talk on his recent effort to rewrite in RPython the part of numpy that exposes the underlying C library to Python. He says the benefits of using PyPy's JIT to speedup that wrapping layer are already visible. He has details on the PyPy blog. Gaël Varoquaux added that David Cournapeau has started working on making the C/Python split in numpy cleaner, which would further ease the job of rewriting it in RPython.
CrossTwine Linker
Damien Diederen talked about his work on CrossTwine Linker and compared it with the many projects that are actively attacking the problem of speed that dynamic and interpreted languages have been dragging along for years. Parrot tries to be the über virtual machine. Psyco offers very nice acceleration, but currently only on 32bits system. PyPy might be what he calls the Right Approach, but still needs a lot of work. Jython and IronPython modify the language a bit but benefit from the qualities of the JVM or the CLR. Unladen Swallow is probably the one that's most similar to CrossTwine.
CrossTwine considers CPython as a library and uses a set of C++ classes to generate efficient interpreters that make calls to CPython's internals. CrossTwine is a tool that helps improving performance by hand-replacing some code paths with very efficient code that does the same operations but bypasses the interpreter and its overhead. An interpreter built with CrossTwine can be viewed as a JIT'ed branch of the official Python interpreter that should be feature-compatible (and bug-compatible) with CPython. Damien calls he approach "punching holes in C substrate to get more speed" and says it could probably be combined with Psyco for even better results.
CrossTwine works on 64bit systems, but it is not (yet?) free software. It focuses on some use cases to greatly improve speed and is not to be considered a general purpose interpreter able to make any Python code faster.
More readings
Cython is a language that makes writing C extensions for the Python language as easy as Python itself. It replaces the older Pyrex.
The SciPy2008 conference had at least two papers talking about speeding Python: Converting Python Functions to Dynamically Compiled C and unPython: Converting Python Numerical Programs into C.
David Beazley gave a very interesting talk in 2009 at a Chicago Python Users group meeting about the effects of the GIL on multicore machines.
I will continue my report on the conference with the second part titled "Applications And Open Questions".
The Configuration Management Problem
Today I felt like summing up my opinion on a topic that was discussed this year on the Python mailing lists, at PyCon-FR, at EuroPython and EuroSciPy... packaging software! Let us discuss the two main use cases.
The first use case is to maintain computer systems in production. A trait of production systems, is that they can not afford failures and are often deployed on a large scale. It leaves little room for manually fixing problems. Either the installation process works or the system fails. Reaching that level of quality takes a lot of work.
The second use case is to facilitate the life of software developers and computer users by making it easy for them to give a try to new pieces of software without much work.
The first use case has to be addressed as a configuration management problem. There is no way around it. The best way I know of managing the configuration of a computer system is called Debian. Its package format and its tool chain provide a very extensive and efficient set of features for system development and maintenance. Of course it is not perfect and there are missing bits and open issues that could be tackled, like the dependencies between hardware and software. For example, nothing will prevent you from installing on your Debian system a version of a driver that conflicts with the version of the chip found in your hardware. That problem could be solved, but I do not think the Debian project is there yet and I do not count it as a reason to reject Debian since I have not seen any other competitor at the level as Debian.
The second use case is kind of a trap, for it concerns most computer users and most of those users are either convinced the first use case has nothing in common with their problem or convinced that the solution is easy and requires little work.
The situation is made more complicated by the fact that most of those users never had the chance to use a system with proper package management tools. They simply do not know the difference and do not feel like they are missing when using their system-that-comes-with-a-windowing-system-included.
Since many software developers have never had to maintain computer systems in production (often considered a lower sysadmin job) and never developed packages for computer systems that are maintained in production, they tend to think that the operating system and their software are perfectly decoupled. They have no problem trying to create a new layer on top of existing operating systems and transforming an operating system issue (managing software installation) into a programming langage issue (see CPAN, Python eggs and so many others).
Creating a sub-system specific to a language and hosting it on an operating system works well as long as the language boundary is not crossed and there is no competition between the sub-system and the system itself. In the Python world, distutils, setuptools, eggs and the like more or less work with pure Python code. They create a square wheel that was made round years ago by dpkg+apt-get and others, but they help a lot of their users do something they would not know how to do another way.
A wall is quickly hit though, as the approach becomes overly complex as soon as they try to depend on things that do not belong to their Python sub-system. What if your application needs a database? What if your application needs to link to libraries? What if your application needs to reuse data from or provide data to other applications? What if your application needs to work on different architectures?
The software developers that never had to maintain computer systems in production wish these tasks were easy. Unfortunately they are not easy and cannot be. As I said, there is no way around configuration management for the one who wants a stable system. Configuration management requires both project management work and software development work. One can have a system where packaging software is less work, but that comes at the price of stability and reduced functionnality and ease of maintenance.
Since none of the two use cases will disappear any time soon, the only solution to the problem is to share as much data as possible between the different tools and let each one decide how to install software on his computer system.
Some links to continue your readings on the same topic:
- The every Linux distribution should have the same package manager fallacy by D. Cournapeau
- Python Packaging: a few observations and a cabal for a solution by D. Cournapeau
- Words on distribute, distutils and PEP 376, 386, 345 by Tarek Ziadé
- Looking for a Windows Package Manager on Logilab's blog
Looking for a Windows Package Manager
As said in a previous article, I am convinced that part of the motivation for making package sub-systems like the Python one, which includes distutils, setuptools, etc, is that Windows users and Mac users never had the chance to use a tool that properly manages the configuration of their computer system. They just do not know what it would be like if they had at least a good package management system and do not miss it in their daily work.
I looked for Windows package managers that claim to provide features similar to Debian's dpkg+apt-get and here is what I found in alphabetical order.
AppSnap
AppSnap is written in Python and uses wxPython, PyCurl and PyYAML. It is packaged using Py2Exe, compressed with UPX and installed using NSIS.
It has not seen activity in the svn or on its blog since the end of 2008.
Appupdater
Appupdater provides functionality similar to apt-get or yum. It automates the process of installing and maintaining up to date versions of programs. It claims to be fully customizable and is licensed under the GPL.
It seems under active development at SourceForge.
WinAptic
WinAptic is another Synaptic clone written this time in Pascal that has not evolved since the end of 2007.
Win-Get
Win-get is an automated install system and software repository for Microsoft Windows. It is similar to apt-get: it connects to a link repository, finds an application and downloads it before performing the installation routine (silent or standard) and deleting the install file.
It is written in pascal and is set up as a SourceForge project, but not much has been done lately.
WinLibre
WinLibre is a Windows free software distribution that provides a repository of packages and a tool to automate and simplify their installation.
WinLibre was selected for Google Summer of Code 2009.
ZeroInstall
ZeroInstall started as a "non-admin" package manager for Linux distributions and is now extending its reach to work on windows.
Conclusion
I have not used any of these tools, the above is just the result of some time spent searching the web.
A more limited approach is to notify the user of the newer versions:
- App-Get will show you a list of your installed Applications. When an update is available for one of them, it will highlighted and you will be able to update the specific applications in seconds.
- GetIt is not an application-getter/installer. When you want to install a program, you can look it up in GetIt to choose which program to install from a master list of all programs made available by the various apt-get clones.
The appupdater project also compares itself to the programs automating the installation of software on Windows.
Some columists expect the creation of application stores replicating the iPhone one.
I once read about a project to get the Windows kernel into the Debian distribution, but can not find any trace of it... Remember that Debian is not limited to the Linux kernel, so why not think about a very improbable apt-get install windows-vista ?
New pylint/astng release, but... pylint needs you !
After several months with no time to fix/enhance pylint beside answering email and filing tickets, I've finally tackled some tasks yesterday night to publish bug fixes releases ([1] and [2]).
The problem is that we don't have enough free time at Logilab to lower the number of tickets in pylint tracker page . If you take a look at the ticket tab, you'll see a lot of pendings bug and must-have features (well, and some other less necessary...). You can already easily contribute thanks to the great mercurial dvcs, and some of you do, either by providing patches or by reporting bugs (more tickets, iiirk ! ;) Thank you all btw !!
Now I was wondering what could be done to make pylint going further, and the first ideas which came to my mind was :
- do ~3 days sprint
- do some 'tickets killing' days, as done in some popular oss projects
But for this to be useful, we need your support, so here are some questions for you:
- would you come to a sprint at Logilab (in Paris, France), so you can meet us, learn a lot about pylint, and work on tickets you wish to have in pylint?
- if France is too far away for most people, would you have another location to propose?
- would you be on jabber for a tickets killing day, providing it's ok with your agenda? if so, what's your knowledge of pylint/astng internals?
you may answer by adding a comment to this blog (please register first by using the link at the top right of this page) or by mail to sylvain.thenault@logilab.fr. If we've enough positive answers, we'll take the time to organize such a thing.
You can now register on our sites
With the new version of CubicWeb deployed on our "public" sites, we would like to welcome a new (much awaited) functionality : you can now register directly on our websites. Getting an account with give you access to a bunch of functionalities :
- registering to a project's activity with get you automated email reports of what is happening on that project
- you can directly add tickets on projects instead of talking about it on the mailing lists
- you can bookmark content
- tag stuff
- and much more...
This is also a way of testing out the CubicWeb framework (in this case the forge cube) which you can take home and host yourself (debian recommended). Just click on the "register" link on the top right, or here.
Photo by wa7son under creative commons.
Using tempfile.mkstemp correctly
The mkstemp function in the tempfile module returns a tuple of 2 values:
- an OS-level handle to an open file (as would be returned by os.open())
- the absolute pathname of that file.
I often see code using mkstemp only to get the filename to the temporary file, following a pattern such as:
from tempfile import mkstemp import os def need_temp_storage(): _, temp_path = mkstemp() os.system('some_commande --output %s' % temp_path) file = open(temp_path, 'r') data = file.read() file.close() os.remove(temp_path) return dataThis seems to be working fine, but there is a bug hiding in there. The bug will show up on Linux if you call this functions many time in a long running process, and on the first call on Windows. We have leaked a file descriptor.
The first element of the tuple returned by mkstemp is typically an integer used to refer to a file by the OS. In Python, not closing a file is usually no big deal because the garbage collector will ultimately close the file for you, but here we are not dealing with file objects, but with OS-level handles. The interpreter sees an integer and has no way of knowing that the integer is connected to a file. On Linux, calling the above function repeatedly will eventually exhaust the available file descriptors. The program will stop with:
IOError: [Errno 24] Too many open files: '/tmp/tmpJ6g4Ke'
On Windows, it is not possible to remove a file which is still opened by another process, and you will get:
Windows Error [Error 32]
Fixing the above function requires closing the file descriptor using os.close_():
from tempfile import mkstemp import os def need_temp_storage(): fd, temp_path = mkstemp() os.system('some_commande --output %s' % temp_path) file = open(temp_path, 'r') data = file.read() file.close() os.close(fd) os.remove(temp_path) return dataIf you need your process to write directly in the temporary file, you don't need to call os.write_(fd, data). The function os.fdopen_(fd) will return a Python file object using the same file descriptor. Closing that file object will close the OS-level file descriptor.
Pylint a besoin de vous
Après plusieurs mois au point mort ou presque, Sylvain a pu hier soir publier des versions corrigeant un certain nombre de bogues dans pylint et astng ([1] et [2]).
Il n'en demeure pas moins qu'à Logilab, nous manquons de temps pour faire baisser la pile de tickets ouverts dans le tracker de pylint. Si vous jetez un œuil dans l'onglet Tickets, vous y trouverez un grand nombre de bogues en souffrance et de fonctionalités indispensables (certaines peut-être un peu moins que d'autres...) Il est déjà possible de contribuer en utilisant mercurial pour fournir des patches, ou en signalant des bogues (aaaaaaaaaarg ! encore des tickets !) et certains s'y sont mis, qu'ils en soient remerciés.
Maintenant, nous nous demandions ce que nous pourrions faire pour faire avance Pylint, et nos premières idées sont :
- organiser un petit sprint de 3 jours environ
- organiser des jours de "tuage de ticket", comme ça se pratique sur différents projets OSS
Mais pour que ça soit utile, nous avons besoin de votre aide. Voici donc quelques questions :
- est-ce que vous participeriez à un sprint à Logilab (à Paris, France), ce qui nous permettrait de nous rencontrer, de vous apprendre plein de choses sur le fonctionnement de Pylint et de travailler ensemble sur des tickets qui vous aideraient dans votre travail ?
- si la France c'est trop loin, où est-ce que ça vous arrangerait ?
- seriez-vous prêt à vous joindre à nous sur le serveur jabber de Logilab ou sur IRC, pour participer à une chasse au ticket (à une date à déterminer). Si oui, quel est votre degré de connaissance du fonctionnement interne de Pylint et astng ?
Vous pouvez répondre en commentant sur ce blog (pensez à vous enregistrer en utilisant le lien en haut à droite sur cette page) ou en écrivant à sylvain.thenault@logilab.fr. Si nous avons suffisamment de réponses positives nous organiserons quelque chose.
hgview 1.1.0 released
I am pleased to announce the latest release of hgview 1.1.0.
What is it?
For the ones from the back of the classroom near the radiator, let me remind you that hgview is a very helpful tool for daily work using the excellent DVCS Mercurial (which we heavily use at Logilab). It allows to easily and visually navigate your hg repository revision graphlog. It is written in Python and pyqt.
What's new
- user can now configure colors used in the diff area (and they now defaults to white on black)
- indicate current working directory position by a square node
- add many other configuration options (listed when typing hg help hgview)
- removed 'hg hgview-options' command in favor of 'hg help hgview'
- add ability to choose which parent to diff with for merge nodes
- dramatically improved UI behaviour (shortcuts)
- improved help and make it accessible from the GUI
- make it possible not to display the diffstat column of the file list (which can dramatically improve performances on big repositories)
- standalone application: improved command line options
- indicate working directory position in the graph
- add auto-reload feature (when the repo is modified due to a pull, a commit, etc., hgview detects it, reloads the repo and updates the graph)
- fix many bugs, especially the file log navigator should now display the whole graph
Download and installation
The source code is available as a tarball, or using our public hg repository of course.
To use it from the sources, you just have to add a line in your .hgrc file, in the [extensions] section:
hgext.hgview=/path/to/hgview/hgext/hgview.py
Debian and Ubuntu users can also easily install hgview (and Logilab other free software tools) using our deb package repositories.
hgview 1.2.0 released
Here is at last the release of the version 1.2.0 of hgview.
In a nutshell, this release includes:
- a basic support for mq extension,
- a basic support for hg-bfiles extension,
- working directory is now displayed as a node of the graph (if there are local modifications of course),
- it's now possible to display only the subtree from a given revision (a bit like hg log -f)
- it's also possible to activate an annotate view (make navigation slower however),
- several improvements in the graph filling and rendering mecanisms,
- I also added toolbar icons for the search and goto "quickbars" so they are not "hidden" any more to the one reluctant to user manuals,
- it's now possible to go directly to the common ancestor of 2 revisions,
- when on a merge node, it's now possible to choose the parent the diff is computed against,
- make search also search in commit messages (it used to search only in diff contents),
- and several bugfixes of course.
- Notes:
- there are packages for debian lenny, squeeze and sid, and for ubuntu hardy, interpid, jaunty and karmic. However, for lenny and hardy, provided packages won't work on pure distribs since hgview 1.2 depends on mercurial 1.1. Thus for these 2 distributions, packages will only work if you have installed backported mercurial packages.
SCons presentation in 5 minutes
Building software with SCons requires to have Python and SCons installed.
As SCons is only made of Python modules, the sources may be shipped with your project if your clients can not install dependencies. All the following exemples can be downloaded at the end of that blog.
A building tool for every file extension
First a Fortran 77 program will be built made of two files:
$ cd fortran-project $ scons -Q gfortran -o cfib.o -c cfib.f gfortran -o fib.o -c fib.f gfortran -o compute-fib cfib.o fib.o $ ./compute-fib First 10 Fibonacci numbers: 0. 1. 1. 2. 3. 5. 8. 13. 21. 34.
The '-Q' option tell to Scons to be less verbose. For cleaning the project, add the '-c' option:
$ scons -Qc Removed cfib.o Removed fib.o Removed compute-fib
From this first example, it can been seen that SCons find the 'gfortran' tool from the file extension. Then have a look at the user's manual if you want to set a particular tool.
Describing the construction with Python objects
A second C program will directly run the execution from the SCons file by adding a test command:
$ cd c-project $ scons -Q run-test gcc -o test.o -c test.c gcc -o fact.o -c fact.c ar rc libfact.a fact.o ranlib libfact.a gcc -o test-fact test.o libfact.a run_test(["run-test"], ["test-fact"]) OK
However running scons alone builds only the main program:
$ scons -Q gcc -o main.o -c main.c gcc -o compute-fact main.o libfact.a $ ./compute-fact Computing factorial for: 5 Result: 120
This second example shows that the construction dependency is described by passing Python objects. An interesting point is the possibility to add your own Python functions in the build process.
Hierarchical build with environment
A third C++ program will create a shared library used for two different programs: the main application and a test suite. The main application can be built by:
$ cd cxx-project $ scons -Q g++ -o main.o -c -Imbdyn-src main.cxx g++ -o mbdyn-src/nodes.os -c -fPIC -Imbdyn-src mbdyn-src/nodes.cxx g++ -o mbdyn-src/solver.os -c -fPIC -Imbdyn-src mbdyn-src/solver.cxx g++ -o mbdyn-src/libmbdyn.so -shared mbdyn-src/nodes.os mbdyn-src/solver.os g++ -o mbdyn main.o -Lmbdyn-src -lmbdyn
It shows that SCons handles for us the compilation flags for creating a shared library according to the tool (-fPIC). Moreover extra environment variables have been given (CPPPATH, LIBPATH, LIBS), which are all translated for the chosen tool. All those variables can be found in the user's manual or in the man page. The building and running of the test suite is made by giving an extra variable:
$ TEST_CMD="LD_LIBRARY_PATH=mbdyn-src ./%s" scons -Q run-tests g++ -o tests/run_all_tests.o -c -Imbdyn-src tests/run_all_tests.cxx g++ -o tests/test_solver.o -c -Imbdyn-src tests/test_solver.cxx g++ -o tests/all-tests tests/run_all_tests.o tests/test_solver.o -Lmbdyn-src -lmbdyn run_test(["tests/run-tests"], ["tests/all-tests"]) OK
Conclusion
That is rather convenient to build softwares by manipulating Python objects, moreover custom actions can be added in the process. SCons has also a configuration mechanism working like autotools macros that can be discovered in the user's manual.
Apycot for Mercurial
What is apycot
apycot is a highly extensible test automatization tool used for Continuous Integration. It can:
- download the project from a version controlled repository (like SVN or Hg);
- install it from scratch with all dependencies;
- run various checkers;
- store the results in a CubicWeb database;
- post-process the results;
- display the results in various format (html, xml, pdf, mail, RSS...);
- repeat the whole procedure with various configurations;
- get triggered by new changesets or run periodically.
For an example, take a look at the "test reports" tab of the logilab-common project.
Setting up an apycot for Mercurial
During the mercurial sprint, we set up a proof-of-concept environment running six different checkers:
- Check syntax of all python files.
- Check syntax of all documentation files.
- Run pylint on the mercurial source code with the mercurial pylintrc.
- Run the check-code.py script included in mercurial checking style and python errors
- Run the Mercurial's test suite.
- Run Mercurial's benchmark on a reference repository.
The first three checkers, shipped with apycot, were set up quickly. The last three are mercurial specific and required few additional tweaks to be integrated to apycot.
The bot was setup to run with all public mercurial repositories. Five checkers immediately proved useful as they pointed out some errors or warnings (on some rarely used contrib files it even found a syntax error).
Prospectives
A public instance is being set up. It will provide features that the community is looking forward to:
- testing all python versions;
- running pure python or the C variant;
- code coverage of the test suite;
- performance history.
Conclusion
apycot proved to be highly flexible and could quickly be adapted to Mercurial's test suite even for people new to apycot. The advantages of continuously running different long running tests is obvious. So apycot seems to be a very valuable tool for improving the software development process.
Why you shoud get rid of os.system, os.popen, etc. in your code
I regularly come across code such as:
output = os.popen('diff -u %s %s' % (appl_file, ref_file), 'r')Code like this might well work machine but it is buggy and will fail (preferably during the demo or once shipped).
Where is the bug?
It is in the use of %s, which can inject in your command any string you want and also strings you don't want. The problem is that you probably did not check appl_file and ref_file for weird things (spaces, quotes, semi colons...). Putting quotes around the %s in the string will not solve the issue.
So what should you do? The answer is "use the subprocess module": subprocess.Popen takes a list of arguments as first parameter, which are passed as-is to the new process creation system call of your platform, and not interpreted by the shell:
pipe = subprocess.Popen(['diff', '-u', appl_file, ref_file], stdout=subprocess.PIPE) output = pipe.stdout
By now, you should have guessed that the shell=True parameter of subprocess.Popen should not be used unless you really really need it (and even them, I encourage you to question that need).
Launching Python scripts via Condor
As part of an ongoing customer project, I've been learning about the Condor queue management system (actually it is more than just a batch queue management system, tacking the High-throughput computing problem, but in my current project, we're not using the full possibilities of Condor, and the choice was dictated by other considerations outside the scope of this note). The documentation is excellent, and the features of the product are really amazing (pity the project runs on Windows, and we cannot use 90% of these...).
To launch a job on a computer participating in the Condor farm, you just have to write a job file which looks like this:
Universe=vanilla Executable=$path_to_executabe Arguments=$arguments_to_the_executable InitialDir=$working_directory Log=$local_logfile_name Output=$local_file_for_job_stdout Error=$local_file_for_job_stderr Queue
and then run condor_submit my_job_file and use condor_q to monitor the status your job (queued, running...)
My program is generating Condor job files and submitting them, and I've spent hours yesterday trying to understand why they were all failing : the stderr file contained a message from Python complaining that it could not import site and exiting.
A point which was not clear in the documentation I read (but I probably overlooked it) is that the executable mentionned in the job file is supposed to be a local file on the submission host which is copied to the computer running the job. In the jobs generated by my code, I was using sys.executable for the Executable field, and a path to the python script I wanted to run in the Arguments field. This resulted in the Python interpreter being copied on the execution host and not being able to run because it was not able to find the standard files it needs at startup.
Once I figured this out, the fix was easy: I made my program write a batch script which launched the Python script and changed the job to run that script.
UPDATE : I'm told there is a Transfer_executable=False line I could have put in the script to achieve the same thing.
PostgreSQL on windows : plpythonu and "specified module could not be found" error
I recently had to (remotely) debug an issue on windows involving PostgreSQL and PL/Python. Basically, two very similar computers, with Python2.5 installed via python(x,y), PostgreSQL 8.3.8 installed via the binary installer. On the first machine create language plpythonu; worked like a charm, and on the other one, it failed with C:\\Program Files\\Postgresql\\8.3\\plpython.dll: specified module could not be found. This is caused by the dynamic linker not finding some DLL. Using Depends.exe showed that plpython.dll looks for python25.dll (the one it was built against in the 8.3.8 installer), but that the DLL was there.
I'll save the various things we tried and jump directly to the solution. After much head scratching, it turned out that the first computer had TortoiseHg installed. This caused C:\\Program Files\\TortoiseHg to be included in the System PATH environment variable, and that directory contains python25.dll. On the other hand C:\\Python25 was in the user's PATH environment variable on both computers. As the database Windows service runs using a dedicated local account (typically with login postgres), it would not have C:\\Python25 in its PATH, but if TortoiseHg was there, it would find the DLL in some other directory. So the solution was to add C:\\Python25 to the system PATH.
Virtualenv - Play safely with a Python
Plan
virtualenv, pip and Distribute are tree tools that help developers and packagers. In this short presentation we will see some virtualenv capabilities.
Please, keep in mind that all above stuff has been made using : Debian Lenny, python 2.5 and virtualenv 1.4.5.
Abstract
virtualenv builds python sandboxes where it is possible to do whatever you want as a simple user without putting in jeopardy your global environment.
virtualenv allows you to safety:
- install any python packages
- add debug lines everywhere (not only in your scripts)
- switch between python versions
- try your code as you are a final user
- and so on ...
Install and usage
Install
Prefered way
Just download the virtualenv python script at http://bitbucket.org/ianb/virtualenv/raw/tip/virtualenv.py and call it using python (e.g. python virtualenv.py).
For conveinience, we will refers to this script using virtualenv.
Other ways
For Debian (ubuntu as well) addicts, just do :
$ sudo aptitude install python-virtualenv
Fedora users would do:
$ sudo yum install python-virtualenv
And others can install from PyPI (as superuser):
$ pip install virtualenv
or
$ easy_install pip && pip install virtualenv
You could also get the source here.
Quick Guide
To work in a python sandbox, do as follow:
$ virtualenv my_py_env $ source my_py_env/bin/activate (my_py_env)$ python
"That's all Folks !"
Once you have finished just do:
(my_py_env)$ deactivate
or quit the tty.
What does virtualenv actually do ?
At creation time
Let's start again ... more slowly. Consider the following environment:
$ pwd /home/you/some/where $ ls
Now create a sandbox called my-sandbox:
$ virtualenv my-sandbox New python executable in "my-sandbox/bin/python" Installing setuptools............done.
The output said that you have a new python executable and specific install tools. Your current directory now looks like:
$ ls -Cl my-sandbox/ README $ tree -L 3 my-sandbox my-sandbox/ |-- bin | |-- activate | |-- activate_this.py | |-- easy_install | |-- easy_install-2.5 | |-- pip | `-- python |-- include | `-- python2.5 -> /usr/include/python2.5 `-- lib `-- python2.5 |-- ... |-- orig-prefix.txt |-- os.py -> /usr/lib/python2.5/os.py |-- re.py -> /usr/lib/python2.5/re.py |-- ... |-- site-packages | |-- easy-install.pth | |-- pip-0.6.3-py2.5.egg | |-- setuptools-0.6c11-py2.5.egg | `-- setuptools.pth |-- ...In addition to the new python executable and the install tools you have an whole new python environment containing libraries, a site-packages/ (where your packages will be installed), a bin directory, ...
- Note:
- virtualenv does not create every file needed to get a whole new python environment. It uses links to global environment files instead in order to save disk space end speed up the sandbox creation. Therefore, there must already have an active python environment installed on your system.
At activation time
At this point you have to activate the sandbox in order to use your custom python. Once activated, python still has access to the global environment but will look at your sandbox first for python's modules:
$ source my-sandbox/bin/activate (my-sandbox)$ which python /home/you/some/where/my-sandbox/bin/python $ echo $PATH /home/you/some/where/my-sandbox/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games (pyver)$ python -c 'import sys;print sys.prefix;' /home/you/some/where/my-sandbox (pyver)$ python -c 'import sys;print "\n".join(sys.path)' /home/you/some/where/my-sandbox/lib/python2.5/site-packages/setuptools-0.6c8-py2.5.egg [...] /home/you/some/where/my-sandbox /home/you/personal/PYTHONPATH /home/you/some/where/my-sandbox/lib/python2.5/ [...] /usr/lib/python2.5 [...] /home/you/some/where/my-sandbox/lib/python2.5/site-packages [...] /usr/local/lib/python2.5/site-packages /usr/lib/python2.5/site-packages [...]
First of all, a (my-sandbox) message is automatically added to your prompt in order to make it clear that you're using a python sandbox environment.
Secondly, my-sandbox/bin/ is added to your PATH. So, running python calls the specific python executable placed in my-sandbox/bin.
- Note
- It is possible to improve the sandbox isolation by ignoring the global paths and your PYTHONPATH (see Improve isolation section).
Installing package
It is possible to install any packages in the sandbox without any superuser privilege. For instance, we will install the pylint development revision in the sandbox.
Suppose that you have the pylint stable version already installed in your global environment:
(my-sandbox)$ deactivate $ python -c 'from pylint.__pkginfo__ import version;print version' 0.18.0
Once your sandbox activated, install the development revision of pylint as an update:
$ source /home/you/some/where/my-sandbox/bin/activate (my-sandbox)$ pip install -U hg+http://www.logilab.org/hg/pylint#egg=pylint-0.19
The new package and its dependencies are only installed in the sandbox:
(my-sandbox)$ python -c 'import pylint.__pkginfo__ as p;print p.version, p.__file__' 0.19.0 /home/you/some/where/my-sandbox/lib/python2.6/site-packages/pylint/__pkginfo__.pyc (my-sandbox)$ deactivate $ python -c 'import pylint.__pkginfo__ as p;print p.version, p.__file__' 0.18.0 /usr/lib/pymodules/python2.6/pylint/__pkginfo__.pyc
You can safely do any change in the new pylint code or in others sandboxed packages because your global environment is still unchanged.
Useful options
Improve isolation
As said before, your sandboxed python sys.path still references the global system path. You can however hide them by:
- either use the --no-site-packages that do not give access to the global site-packages directory to the sandbox
- or change your PYTHONPATH in my-sandbox/bin/activate in the same way as for PATH (see tips)
$ virtualenv --no-site-packages closedPy $ sed -i '9i PYTHONPATH="$_OLD_PYTHON_PATH" 9i export PYTHONPATH 9i unset _OLD_PYTHON_PATH 40i _OLD_PYTHON_PATH="$PYTHONPATH" 40i PYTHONPATH="." 40i export PYTHONPATH' closedPy/bin/activate $ source closedPy/bin/activate (closedPy)$ python -c 'import sys; print "\n".join(sys.path)' /home/you/some/where/closedPy/lib/python2.5/site-packages/setuptools-0.6c8-py2.5.egg /home/you/some/where/closedPy /home/you/some/where/closedPy/lib/python2.5 /home/you/some/where/closedPy/lib/python2.5/plat-linux2 /home/you/some/where/closedPy/lib/python2.5/lib-tk /home/you/some/where/closedPy/lib/python2.5/lib-dynload /usr/lib/python2.5 /usr/lib64/python2.5 /usr/lib/python2.5/lib-tk /home/you/some/where/closedPy/lib/python2.5/site-packages $ deactivateThis way, you'll get an even more isolated sandbox, just as with a brand new python environment.
Work with different versions of Python
It is possible to dedicate a sandbox to a particular version of python by using the --python=PYTHON_EXE which specifies the interpreter that virtualenv was installed with (default is /usr/bin/python):
$ virtualenv --python=python2.4 pyver24 $ source pyver24/bin/activate (pyver24)$ python -V Python 2.4.6 $ deactivate $ virtualenv --python=python2.5 pyver25 $ source pyver25/bin/activate (pyver25)$ python -V Python 2.5.2 $ deactivate
Distribute a sandbox
To distribute your sandbox, you must use the --relocatable option that makes an existing sandbox relocatable. This fixes up scripts and makes all .pth files relative This option should be called just before you distribute the sandbox (each time you have changed something in your sandbox).
An important point is that the host system should be similar to your own.
Tips
Speed up sandbox manipulation
Add these scripts to your .bashrc in order to help you using virtualenv and automate the creation and activation processes.
rel2abs() { #from http://unix.derkeiler.com/Newsgroups/comp.unix.programmer/2005-01/0206.html [ "$#" -eq 1 ] || return 1 ls -Ld -- "$1" > /dev/null || return dir=$(dirname -- "$1" && echo .) || return dir=$(cd -P -- "${dir%??}" && pwd -P && echo .) || return dir=${dir%??} file=$(basename -- "$1" && echo .) || return file=${file%??} case $dir in /) printf '%s\n' "/$file";; /*) printf '%s\n' "$dir/$file";; *) return 1;; esac return 0 } function activate(){ if [[ "$1" == "--help" ]]; then echo -e "usage: activate PATH\n" echo -e "Activate the sandbox where PATH points inside of.\n" return fi if [[ "$1" == '' ]]; then local target=$(pwd) else local target=$(rel2abs "$1") fi until [[ "$target" == '/' ]]; do if test -e "$target/bin/activate"; then source "$target/bin/activate" echo "$target sandbox activated" return fi target=$(dirname "$target") done echo 'no sandbox found' } function mksandbox(){ if [[ "$1" == "--help" ]]; then echo -e "usage: mksandbox NAME\n" echo -e "Create and activate a highly isaolated sandbox named NAME.\n" return fi local name='sandbox' if [[ "$1" != "" ]]; then name="$1" fi if [[ -e "$1/bin/activate" ]]; then echo "$1 is already a sandbox" return fi virtualenv --no-site-packages --clear --distribute "$name" sed -i '9i PYTHONPATH="$_OLD_PYTHON_PATH" 9i export PYTHONPATH 9i unset _OLD_PYTHON_PATH 40i _OLD_PYTHON_PATH="$PYTHONPATH" 40i PYTHONPATH="." 40i export PYTHONPATH' "$name/bin/activate" activate "$name" }- Note:
- The virtualenv-commands and virtualenvwrapper projects add some very interesting features to virtualenv. So, put on eye on them for more advanced features than the above ones.
Conclusion
I found it to be irreplaceable for testing new configurations or working on projects with different dependencies. Moreover, I use it to learn about other python projects, how my project exactly interacts with its dependencies (during debugging) or to test the final user experience.
All of this stuff can be done without virtualenv but not in such an easy and secure way.
I will continue the series by introducing other useful projects to enhance your productivity : pip and Distribute. See you soon.
Other Documentation and Links
virtualenv homepage: http://virtualenv.openplans.org/
pip homepage: http://pip.openplans.org/
Distribute homepage: http://packages.python.org/distribute/
virtualenv-commands extension for virtualenv : http://thisismedium.com/tech/extending-virtualenv/
virtualenvwrapper extension for virtualenv : http://www.doughellmann.com/projects/virtualenvwrapper
(Image under creative commons by-nc-nd by minlynn071502 / Mindy Roth)
Salomé accepted into Debian unstable
Salomé is a platform for pre and post-processing of numerical simulation available at http://salome-platform.org/. It is now available as a Debian package http://packages.debian.org/source/sid/salome and should soon appear in Ubuntu https://launchpad.net/ubuntu/+source/salome as well.
A difficult packaging work
A first package of Salomé 3 was made by the courageous Debian developper Adam C. Powell, IV on January 2008. Such packaging is very resources intensive because of the building of many modules. But the most difficult part was to bring Salomé to an unported environment. Even today, Salomé 5 binaries are only provided by upstream as a stand-alone piece of software ready to unpack on a Debian Sarge/Etch or a Mandriva 2006/2008. This is the first reason why several patches were required for adapting the code to new versions of the dependencies. The version 3 of Salomé was so difficult and time consuming to package that Adam decided to stop during two years.
The packaging of Salomé started back with the version 5.1.3 in January 2010. Thanks to Logilab and the OpenHPC project, I could join him during 14 weeks of work for adapting every module to Debian unstable. Porting to the new versions of the dependencies was a first step, but we had also to adapt the code to the Debian packaging philosophy with binaries, librairies and data shipped to dedicated directories.
A promising future
Salomé being accepted to Debian unstable means that porting it to Ubuntu should follow in a near future. Moreover the work done for adapting Salomé to a GNU/Linux distribution may help developpers on others platforms as well.
That is excellent news for all people involved in numerical simulation because they are going to have access to Salomé services by using their packages management tools. It will help the spreading of Salomé code on any fresh install and moreover keep it up to date.
Join the fun
For mechanical engineers, a derived product called Salomé-Méca has recently been published. The goal is to bring the functionalities from the Code Aster finite element solver to Salomé in order to ease simulation workflows. If you are as well interested in Debian packages for those tools, you are invited to come with us and join the fun.
I have submitted a proposal to talk about Salomé at EuroSciPy 2010. I look forward to meet other interested parties during this conference that will take place in Paris on July 8th-11th.
EuroSciPy 2010 schedule is out !
The EuroSciPy 2010 conference will be held in Paris from july 8th to 11th at Ecole Normale Supérieure. Two days of tutorials, two days of conference, two interesting keynotes, a lightning talk session, an open space for collaboration and sprinting, thirty quality talks in the schedule and already 100 delegates registered.
If you are doing science and using Python, you want to be there!
HOWTO install lodgeit pastebin under Debian/Ubuntu
Lodge it is a simple open source pastebin... and it's written in Python!
The installation under debian/ubuntu goes as follows:
sudo apt-get update sudo apt-get -uVf install python-imaging python-sqlalchemy python-jinja2 python-pybabel python-werkzeug python-simplejson cd local hg clone http://dev.pocoo.org/hg/lodgeit-main cd lodgeit-main vim manage.py
For debian squeeze you have to downgrade python-werkzeug, so get the old version of python-werkzeug from snapshot.debian.org at http://snapshot.debian.org/package/python-werkzeug/0.5.1-1/
wget http://snapshot.debian.org/archive/debian/20090808T041155Z/pool/main/p/python-werkzeug/python-werkzeug_0.5.1-1_all.deb
Modify the dburi and the SECRET_KEY. And launch application:
python manage.py runserver
Then off you go configure your apache or lighthttpd.
An easy (and dirty) way of running it at startup is to add the following command to the www-data crontab
@reboot cd /tmp/; nohup /usr/bin/python /usr/local/lodgeit-main/manage.py runserver &
This should of course be done in an init script.
Hopefully we'll find some time to package this nice webapp for debian/ubuntu.
