• Added a note about pip install -e as an alternative to python setup.py develop
• Added a note about testpypi.python.org
• Fixed the section about the manifest (I forgot to add include_package_data to the setup script)

This post attempts to summarize the important bits using a “Hello World” project and steeping through the process of creating the setup.py file:

• Creating a package distribution
• Automatic generation of executables
• Version numbers
• Dependency management
• Publishing your package (thus also making it available for automatic dependency resolution)
• Some more food for thought.

Our Hello World project

Our project will provide the following:

• a package called helloworld
• a method returning the string “Hello World!”
• an executable printing “Hello World!” to the standard output.

Creating a package distribution

Before you can do any of the aforementioned points, you need to be able to create a package file. In most cases, this would be a tarball (a source distribution). First, you need to think about your file layout:

In python, every package lives in its own folder. Unless you have a one-file module. For the sake of this post, I will assume you have a folder, and leave the one-file module as an exercise to the reader. After all, the differences are not all that great. Let’s also assume, for the sake of simplicity, we have only one module in our package folder.

Let’s create the package

First, the file laout:

NOTE: This is just an example project, and it could have been implemented as a one-file module (using core.py), or the application logic could have been stuffed into __int__.py. But I assume that multi-file packages are far more common, so I will use an appropriate layout for that. Also, I personally like to always create my projects like this. On the one hand for consistency, and on the other hand it makes it easier to “grow” the project later on. It’s up to you to decide for yourself.

In this case, I named the project root “the-helloworld-project”. But (again, personally), I just give the root folder the same name as the package name. The difference between the root (“the-helloworld-project”) and the package (“helloworld”) is that  the package folder contains the actual package code, and the root folder contains packaging metadata and possibly some other development related stuff (unit-tests, fabric files, a readme, the license file, …)

Next, let’s create our business logic. For our target features, we need both a method generating our “Hello World!” message, and one to print it to stdout. Let’s put this into helloworld/core.py:

So far so good. We have our business logic. Yay \o/.

Creating a skeleton setup.py file

Next up, we will create a very simple setup.py file. Let’s also add a README.txt file for good measure (it should be formatted in ReST!):

Next, let’s create a very simple setup script, and put it into the root folder:

Note that we don’t use distutils, but setuptools. You will be better off with setuptools until distutils2 is available.

IMPORTANT:
The package setuptools is actually made available by a package named distribute. You should not install the old setuptools any more!

WARNING

As Ionel Maries Cristian pointed out in the comments, the line long_description=open('README.txt').read() could cause problems. Let’s make it safer…

We know that the README file sits in the same folder as the setup script. So we can use os.path.dirname on __file__ to construct a filename we can reach:

Now we can test to create a source distribution by running:

There will be some warnings as we did fill in neither an author email nor a package URL. For this example we consider this okay!

This will give you a file called sdist/helloworld-1.0.tar.gz. You can inspect it if you want using tar tzvf sdist/helloworld-1.0.tar.gz.

With this done, we already have two points of our targets done:

• a package called helloworld
• a method returning the string “Hello World!”
• an executable printing “Hello World!” to the standard output.

Testing inside a virtual environment

The details about virtual environments are out of the scope of this document (see the package documentation for more). But because it is highly recommended to use them, we will do so right now. This gives us the ability to test the package installation without polluting our system packages.

So let’s create one:

This will create an environment called env in our current working folder and install pip and distribute into it. With this in place, we can already test the package installation:

Now we can test our package within the virtual environment:

Perfekt!

But we still want our executable…

Creating an executable

Following the official distutils documentation, we should specify the scripts keyword argument on the setup method. On the other hand, distribute gives us entry points. Personally, I prefer entry points. So let’s get this done. Modify the setup.py file and add the following:

The complete setup.py file should now look like this:

What does this mean?

• helloworld = ...
  Create an executable with the name helloworld (on windows it will be a .exe file)
• ... = helloworld.core:...
  The method is found in this package
• ... :print_message
  Call this method on execution

Let’s re-install our package:

As you can see in the output, the setup script installed an executable into our local bin folder. If you execute it, your method from your package will be executed.

Awesome! We can cross the last item off our basic tasks:

• a package called helloworld
• a method returning the string “Hello World!”
• an executable printing “Hello World!” to the standard output.

Version Numbers

Version numbers are a very important part of your project! You should manage them as soon as possible. They are primarily important in two situation:

• Installation of the package if an older version exists already (upgrading)
• Dependency management

If you ever publish a package, it should always stay available! People using your package have the option to “pin” a version in their dependencies. Because of this, you will never know how long a specific version is going to be used. Again: Once published it should never disappear!

In the above example, we “hard-coded” the version number as 1.0. There is a better solution however! It would be nice to give your package users the ability to check the version number. To do that, we will move the version number into our top-level __init__.py file. By convention, the variable should be named __version__. In our package, this will be the only line in the file:

This will allow users to do the following:

This is a very convenient information to have!

But now there is one annoyance… We have to edit the version number in two locations. In our setup script, and the __init__.py file. But there's a simple solution for that. In your setup script, simply import your project and get the version number from there.

WARNING:
As bboe over on reddit pointed out, when doing this, you must be aware that this could fail if the project tries to import one of it's dependencies when importing helloworld. As the dependency cannot be available yet when running the script, it will raise an ImportError. So, either you don't import the version variable, or you must not trigger an import of any dependency when importing the package.
 
Other alternatives are possible. Such as storing your version number in a separate .py file and importing that one. Or, if all else fails, running a regex on you __init__.py file. You can see implementations of these solutions in the reddit link mentioned before.

which will give you this result:

It is recommended that the version numbers follow a well-known scheme. There are a few:

• from PEP 386
• from The official documentation
• from The Hitchhiker's Guide

I suggest following the "strict" numbers stated in PEP386 for distutils. An example from the PEP document (ordered by version number):

Dependency Management

As stated above, with setup scripts, you can specify dependencies. So let's make a small hello-world web application using Flask. A web-app will also contain "data" files (templates, ...), which will give us the opportunity to talk about the manifest later on. But for now, let's simply add Flask into our dependencies. To do that, add the following to your setup script:

So the complete setup script now reads:

Let's run the script again. But let's use pip now:./env/bin/pip install -e .. Alternatively we could have used ./env/bin/python setup.py install or ./env/bin/python setup.py develop, but pip gives us the option of uninstalling. Not that this would matter in our development environment. But it's good to know it exists. On the output, we will see that this will now automagically pull in everything required. Python will automatically query pypi to find the latest version of Flask and follow it's dependencies.

Okay. So now we got the latest Flask version, and we will start to develop against that version. Because of this, we want to pin the version, so that we (and our users) will not be surprised if the API of Flask will change in the future. To determine the version of Flask, we can run the following:

This will list all packages, including the version number installed in the environment from which pip was run.

This output can also be used as a "requirements file" for pip installations. I won't cover that topic yet. It's still new to me

So now we know we got version 0.8. We can now change our setup script to pin our version:

You are strongly encouraged to do this as it puts you in control about when and where you will upgrade to newer versions!

Now, as a small interlude, let's create a one-page web application, basing ourselves on the integrated server. This will also give us the opportunity to revisit the creation of executables. In a production environment, you should not do this, but rather deploy a WSGI application. But this post is not about web application. We just want some "data" files!

Create a folder for our templates (which incidentally contains our only data file):

... and put the following file into it, called index.html:

Next, create the Flask entry point inside helloworld/web.py

... and add the entry-point to setup.py:

Execute the setup script again (to make the new executable available):

... and test the server:

Good! We have everything in place to talk about the manifest.

The MANIFEST

There is still one problem with our project. By default the package only contains python files. If you need to add additional data, for example template of image files in a web-application, you need to specify these. The above example contains a template. So let's see what this means for us. Let's create a new source distribution:

Now, inspect the contents of the tarball:

As you can see, the templates are not included! So our package will not work if we distribute like this! To do this, we need to tell python that we want other non-source files to be added. We can do this easily with a file called MANIFEST.in. In our case, it will look like this:

Finally, we need to add the following line to the setup script:

There are other ways to address the issue of data files. Most notably, as of Python 2.7, the setup procedure will automatically create a manifest based on package_data and data_files. The advantage of this is that your setup specs are no longer split into multiple files. As it is fairly new, and as Python 2.7 is only now gaining traction, I have not yet worked with this. I leave this as an exercise to the reader.

If we now re-create the sdist, we see that the files are included in the tarball. We need to do this for all non-source files which we want to include in our project! You can find more detailed information on this in the official documentation.

Accessing packaged files

When you need to access these packaged data files you cannot simply use open(filename). You don't know the filename! It could even be stored inside a .egg. This all depends on how the end-user will install the package. You don't have control over this! For this reason, distribute provides a couple of methods to access these files. The most likely candidates are:

• resource_stream which returns a file-like object,
• resource_string which returns a string and
• resource_filename

Note that when using resource_filename the resource may be (in the case of a zipped installation) extracted to a cache folder. See the pkg_resources (resource-extraction) for details on this behaviour.

Publishing

If you want to make your package available to others, the obvious choice is pypi. Python makes it easy to publish. Simply run:

See the hitchhiker's guide for more details on this.

Before you publish on pypi you may want to test your upload process. For this reason, there is http://testpypi.python.org/pypi
To use this (or any other package index), you need to specify the details in your ~/.pypirc. Here is an example:

Having this, you can then upload packages to the given repositories by appending -r ppt for example.

But corporate policy may prevent you from publishing to the public domain. Or your project contains sensitive data which should not be made public. In this case, it is very easy to set up a local repository of packages. You only need to have a HTML document with the links available. A very easy way is to set up an Apache host which indexes a folder into which you upload your packages.

Even if not behind corporate restrictions, this is an easy way to publish pre-release packages which you don't want to push to pypi just yet.

To be able to use these repositories for your dependencies, you need to add the links into your setup.py file. Let's assume you create a new package depending on our helloworld package which we published on our local repository, and that your local repository is available as http://our.local.repo/. Then you would create a setup script like this:

Alternatively, when using pip, you can specify URLs at which to look for packages using pip -f ....

Food for thought

As promised, here's another tidbit. For many python beginners, the setup.py file is some sort of black magic and must contain the call to the setup method and nothing else. But that is not true! The setup script is a completely normal python file which you execute. This has an awesome implication: You can do whatever you want before and after the call to setup. Everything you execute before the call to setup, will obviously be run before the setup process, everything after the setup call will in turn be run after the installation is finished. I will let your imagination run wild with what you can do. Keep in mind though, that you may want to keep away from user-prompts and so forth to avoid breaking automated installations! But it is your project and your choice what you will or won't do.

In most cases this level of fine-tuning is not needed however distribute and distutils already offer a lot of functionality. Investigate these first! Only do such things if there is really no other way around! Or if you want to play around

References

• The Hitchhiker's Guide to Python Packaging
• A Guide to Python Packagin (IBM)
• The official Python documentation (setupscript, source-dist)
• Distribute
• PEP 386 - Changing the version comparison module in Distutils

Now run the following with path/to/project being your projects root directory, and give gource the .git subfolder. Run it and you should see the animation being presented.

Now to export this to an mpeg4 video do the following.

Here an example I created from one of my projects.

Existing comments have been put into the the disqus importer queue, and according to them, they should appear after 24 hours. So nothing is lost!

Simply add the following to your ~/.bashrc

You may already have a block like if [ -f /etc/bash_completion ]... in that case, simply add the extra line into that block.

#                                                                                                                                                                                                    
# Bash completion for fabric                                                                                                                                                                         
#                                                                                                                                                                                                    
function _fab_complete() {                                                                                                                                                                           
    local cur                                                                                                                                                                                        
    if [ -f "fabfile.py" ]; then                                                                                                                                                                     
        cur="${COMP_WORDS[COMP_CWORD]}"                                                                                                                                                              
        COMPREPLY=( $(compgen -W "$(fab -F short -l)" -- ${cur}) )                                                                                                                                   
        return 0                                                                                                                                                                                     
    else                                                                                                                                                                                             
        # no fabfile.py found. Don't do anything.
        return 1                                                                                                                                                                                     
    fi                                                                                                                                                                                               
}                                                                                                                                                                                                    
                                                                                                                                                                                                     
# enable programmable completion features (you don't need to enable                                                                                                                                  
# this, if it's already enabled in /etc/bash.bashrc and /etc/profile                                                                                                                                 
# sources /etc/bash.bashrc).                                                                                                                                                                         
if [ -f /etc/bash_completion ] && ! shopt -oq posix; then                                                                                                                                            
    . /etc/bash_completion                                                                                                                                                                           
    complete -o nospace -F _fab_complete fab                                                                                                                                                         
fi                                                                                                                                                                                                   

Have fun

The next thing that caught my eye, was CoffeeScript. It allowed me to write pythonesque code, and compile right down to proper JavaScript.

So what has this to do with the closure library? Well, recently (as stated above) I started web-development again. And some parts of the project made it obvious, that a good dash of JavaScript would help the project a lot. So I needed to take a decision on what library I would base my work on. My first reflex was jQuery. But out of sheer coincidence I stumbled across a blog post from someone discussing jQuery and closure. Unfortunately, I don’t have the link anymore The TL;DR of that blog post was however (paraphrasing):

If I had known about closure before, I would have used it instead of jQuery, because it makes it easier to structure the source code.

This made me more that curious. So the next thing I did, was read the closure tutorials, and also read the API top to bottom. I wanted to know what was in there before deeming it useful. And boy did it look interesting! The most interesting feature, as the author of said blog post pointed out, it the ability to easily structure your code. Using goog.provides and goog.require you can very easily split your JS files into multiple, well structured files, and the closure compiler will then re-combine them into one file, all properly ordered. To me, this feels a lot like writing Java or Python, which both allow you to structure your code very well. This also allows you to easily write modules which can in turn be re-used with ease in other projects (or, you could publish them).

For now, I am not missing anything from jQuery except the easy DOM querying from it. But I can happily live without it.

With my current experience, I can see the following benefits:

• Makes it easy to structure code
• The optional linter forces you to write proper/clean code. Especially with the --strict flag. Using it right from the start forces you to write in a uniform coding style, which makes code more readable!
• The compiler solves dependency chains with the help of goog.provide and goog.require, and can optionally minify your code.
• The library feels very professional. It’s even got a Logger loosely inspired by java.util.logger, and let’s you do exactly what you expect it to! This is awesome for debugging!
• The library source code is very readable

And some disadvantages:

• The community is much smaller than the one around jQuery. But so far I could figure out all I needed by reading the code.
• It does not seem to have versioned downloads. You have to checkout from SVN trunk. I would prefer to be able to say: “I am basing my work on version 1.0″ instead of “trunk”
• Instead of linking one CSS file (as in jQuery), you have to link multiple styles and figure out which ones by yourself. But given the filenames are self-explanatory, that’s not too difficult.
• There are no predefined CSS themes. Something jQuery’s theme builder would be nice. But, I assume that the closure-style-sheet project should help writing CSS files easily. I did not yet look into that one!

As I progress with the library I’ll post my findings…

Here are two related links I found while looking for the mentioned blog post:

• http://stackoverflow.com/questions/1690197/what-does-google-closure-library-offer-over-jquery
• http://sayspy.blogspot.com/2010/10/lessons-learned-porting-from-jquery-to.html

It’s possible to use this list to react to important events in the job executions. For example, you could loop through the list and send emails to the appropriate people if the variable “lastResult” is non-zero.

/**
 * Naive CSV splitter
 *
 * This splitter is *very* simplistic and may result in errors when parsing
 * unknown CSV sources. This works well in the current problem domain.
 *
 * As we have well defined data, with no escaped quotes inside the fields, we
 * can sefaly assume that this will work.
 */
function simpleCsvSplit(lineText){
	var columns = [];
	var inside_quote = false;
	var current_data = "";
	var i=0;
	for(i=0; i<lineText.length; i++){
		var chr = lineText.substring(i, i+1);
		if (chr === "\""){
			inside_quote = !inside_quote;
			continue;
		} else if (chr === "," && !inside_quote){
			columns[columns.length] = current_data;
			current_data = "";
		}
		if (inside_quote){
			current_data += chr;
		}
	}
	columns[columns.length] = current_data;
	return columns;
}

/**
 * A container object for the task metadata
 * This makes further processing with the data a lot more expressive.
 *
 * @param lineText A string taken from the CSV output representing one line
 */
var Task = function(lineText){
	// parse the CSV data
	columns = simpleCsvSplit(lineText);

	// put everything into explicitly named variables
	this.hostName = columns[0];
	this.name = columns[1];
	this.nextRun = columns[2];
	this.status = columns[3];
	this.lastRun = columns[4];
	this.lastResult = columns[5];
	this.creator = columns[6];
	this.schedule = columns[7];
	this.command = columns[8];
	this.startIn = columns[9];
	this.comment = columns[10];
	this.scheduledState = columns[11];
	this.scheduledType = columns[12];
	this.startTime = columns[13];
	this.startDate = columns[14];
	this.endDate = columns[15];
	this.days = columns[16];
	this.months = columns[17];
	this.runAs = columns[18];
	this.deleteIfNotRescheduled = columns[19];
	this.stopIf = columns[20];
	this.repeatEvery = columns[21];
	this.repeatUntilTime = columns[22];
	this.repeatUntilDuration = columns[23];
	this.repeatStopIfRunning =columns[24];
	this.idleTime = columns[25];
	this.powerManagement = columns[26];
};

// execute the shell command to retrieve the scheduled tasks
var WshShell = new ActiveXObject("WScript.Shell");
var oExec = WshShell.Exec("schtasks /Query /FO CSV /V");

// retrieve the lines from stdout and save them as tasks
var tasks = [];
while( !oExec.StdOut.AtEndOfStream){
   tasks[tasks.length] = oExec.StdOut.ReadLine();
}

// now do something with the tasks
for(var i=2; i<tasks.length; i++){
	var tmp = new Task(tasks[i]);
	WScript.Echo(tmp.lastResult + " - " + tmp.status);
}

However, if you let Netbeans create the persistence configuration, it will hardcode all connection parameters into the persistence.xml file. When retrieving an EntityManager instance, it will use this information to connect.

If, instead you would like to do this at runtime, you can do the following:

1. Remove the “properties” tag from the persistence.xml. This may not be necessary, but this will make it clear, that the properties are set inside the code.
2. Create a “Map<String, String>” which will contain the properties. A list of standard properties can be found in the specs in section 8.2.1.9.
3. Use this map to create an EntityManagerFactory and use this to create your EntityManager

An example persistence.xml without properties

In case Netbeans created a method “getEntityManager”, you can safely replace this. Here is an example I currently have in use. The “appConf” instance is a singleton I use to store configuration data in the user’s home folder, and yes, the password is stored in plain-text, but for this test-case I did not need to go any further:

This example uses eclipselink. All available properties can be found it the EclipseLink Wiki