I just wanted to re-point out the fact that there are some forums associated with this blog. There’s not much happening there, and recently they have become a haven for spammers, but I’m trying to clean them up and if other Python programmers read this blog maybe the forums could actually become useful!

Either way for those that didn’t know, there are learning python forums available.

Edit: Due to popular demand (well a couple of comments) I’ve decided to allow multiple answers to the poll. This should make everyone that uses two versions happy.

With two major versions of Python available to us Python programmers (2.X and 3.X) I thought it would be interesting to see which version the readers of this blog are using and targeting.

Personally I’m still using 2.5 on my Debian box because it’s the default, and 2.6 on my Windows PC. While I have used 3.X and have it installed, I’ve remained on the 2.X branch largely because many of the modules that I play around with are still focused on the 2.X branch so that’s where my focus has remained.

I voted 2.5 since I do that majority of my programing on my Debian box. So now what about you: Note: There is a poll embedded within this post, please visit the site to participate in this post's poll.

If you want to explain your choice leave a comment below.

Note: This article was first published the March 2008 issue of Python Magazine

Mark Mruss

Over the past few years Google has expanded it’s services beyond those of a normal search engine. One of those new services is the Google Calendar. This article will provide an introduction to working with the Google Calendar using Python.

Introduction

As many of you know, Google has branched out and started offering more services besides their ubiquitous search engine. You have email, calendars, documents, spreadsheets, photos, maps, videos, source code hosting, and the list goes on. Fortunately for us Python programmers, Google released the Google data Python Client Library on March 26th, 2007, giving Python programmers easy access to some of these services.

What the Google data Python Client Library, or “gdata-python-client”, does is provide “a library and source code that makes it easy to access data through Google Data APIs.” [1] This leads to the question: “what are the Google Data APIs?” In the words of Google: “The Google data APIs provide a simple standard protocol for reading and writing data on the web. These APIs use either of two standard XML-based syndication formats: Atom or RSS.”[2]

The Google services that use the Google data APIs include many of the services that we have grown to know and love:

• Google Apps
• Google Base
• Blogger
• Google Calendar
• Google Code Search
• Google Documents
• Google Notebook
• Picasa Web Albums
• Spreadsheets
• YouTube

This tutorial will only deal with the Google Calendar service specifically, but it’s important to know that many of the techniques used here can easily be applied to other Google services.

The Google data Python Client Library requires Python 2.2 or greater and the ElementTree module to be installed. I recommend using Python 2.5 since the ElementTree module is included in that release of Python. This column assumes that you are using Python 2.5 and version 1.0.10 of the “gdata-python-client”.

Getting and Installing the “gdata-python-client”

We must first download and install the “gdata-python-client” files from the “gdata-python-client” website.[3] Once you have downloaded the compressed file, extract it’s contents to a folder. You will then need to browse to that folder and run the extracted setup.py file with the install command with root access. For me, the command looked like this:

# python2.5 setup.py install

Notice that I ran python2.5 instead of simply python. I did this to ensure that Python 2.5 was used instead of a previous version of Python.

Once you have done this, you can test to make sure that the gdata-python-client module has been installed properly by trying to import the gdata module. You can test this easily in the interactive Python shell:

$ python2.5
Python 2.5.2a0 (r251:54863, Jan  3 2008, 17:59:56)
[GCC 4.2.3 20071123 (prerelease) (Debian 4.2.2-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import gdata
>>>

If everything works properly you should see no errors when importing gdata. If you do run into problems, you can consult the install.txt file that was extracted with the rest of the files in the “gdata-python-client” archive that you downloaded.

Getting Started

There are some great Google Calendar examples that come with the “gdata-python-client” module. I found them to be very useful learning tools. The “Google Calendar Developers Guide”[4] is also very helpful. If you get stuck it is essential reading. The “Google Calendar API Reference Guide”[5] is also an indispensable help.

It is important to remember that when you are working with the Google Calendar service, you are actually working with XML data. You are sending and receiving XML data to and from the Google Calendar service – Atom or RSS feeds to be precise. The Python classes that wrap these feeds, or XML blocks, are dynamically “formed” around the XML. When you access something like the following:

e_link.href

You are actually accessing the “href” attribute of an XML link block that may look something like this:

<ns0 :link href="http://www.google.com/calendar/feeds/<username>/private/full" rel="alternate" type="application/atom+xml" xmlns:ns0="http:www.w3.org/2005/Atom" />

In the above example, if we had the link as an instance (perhaps an atom.Link object), e_link.rel would be equal to “alternate”.

Logging In

There are two types of Google calendars – public calendars and private calendars. Public calendars do not require any authentication while private calendars do. There are three forms of authentication for private calendars: “AuthSub proxy” authentication, “ClientLogin” authentication, and “Magic cookie” authentication.

“AuthSub proxy” authentication is meant for web applications so it will not be covered in this tutorial. “Magic cookie” authentication requires a string (the magic cookie) obtained from your Google Calendar settings page. This type of authentication gives you read only access to a private calendar. Its usage is quite specific and will not be covered in this column either.

For this column we will focus on good old-fashioned “ClientLogin” authentication. In order to perform “ClientLogin” authentication we need two things: a Google Calendar username and a password. If you do not have these, head over to the Google Calendar website [6] and get yourself signed up. If you have gmail or have signed up for other Google services, you can probably sign in using that username and password.

Note: I say username and so does the documentation, but this will actually be an email address.

We are going to read the username and password in from the command line using the raw_input function and the getpass module. I must thank the people who wrote the Google code examples for introducing me to the getpass module. Before looking through the examples, I had not heard of it.

The raw_input function simply reads a line of input from the command line and returns it with the newline stripped. The getpass.getpass function does the exact same thing as raw_input except it does not echo the input, which is handy when working with passwords.

We need to import the getpass module:

import getpass

Then, in our main function we will ask the user to input their username and password:

def main():
    username = raw_input("Enter your username: ")
    password = getpass.getpass("Enter your password: ")

if __name__ == "__main__":
    main()

Now that we have our username and password, we need to log the user in. To do this we are going to use the CalendarService class. The CalendarService “extends the GDataService to streamline Google Calendar operations.” [7] The GDataService class “provides CRUD ops. and programmatic login for GData services.” [8]. CRUD is an acronym that stands for Create, Retrieve, Update, and Delete. What the CalendarService class will allow us to do is create, retrieve, update, and delete things from a Google Calendar.

The first step in logging in is to create an instance of the CalendarService class. To do this we will pass in the username and password that we have collected:

calendar_service = gdata.calendar.service.CalendarService(username
    , password
    , "PythonMagazine-Calendar_Example-1.0")

The third parameter that we pass to the CalendarService constructor is the “source” string. This is a short “string identifying your application, for logging purposes. This string should take the form: “companyName-applicationName-versionID”"[9]

Now that we have a CalendarService instance, we need to login. To do this we will use the ProgrammaticLogin function. This will log into the Google Calendar using the CalendarService classes current username and password. The ProgrammaticLogin function can raise three possible exceptions that we want to be aware of:

1. CaptchaRequired – Raised if the login requires a “captcha” response in order to login.
2. BadAuthentication – Raised if the username and/or password were not accepted by the Google Calendar.
3. Error – Raised if a 403 error occurs that is not a “CaptchaRequired” or “BadAuthentication” error.

For this example, shown in Listing 1, we will only worry about the “BadAuthentication” exception.

Listing 1

def main():
    username = raw_input("Enter your username: ")
    password = getpass.getpass("Enter your password: ")

    calendar_service = gdata.calendar.service.CalendarService(username
        , password
        , "PythonMagazine-Calendar_Example-1.0")
    try:
        calendar_service.ProgrammaticLogin()
    except gdata.service.BadAuthentication, e:
        print "Authentication error logging in: %s" % e
        return
    except Exception, e:
        print "Error Logging in: %s" % e
        return

Working With Calendars

Now that we’ve logged into the calendar service, we can start working with the user’s available calendars. To get a list of all of the available calendars you can call the GetAllCalendarsFeed function. This will return a CalendarListFeed instance representing all of the user’s calendars. Since I want to show how to add and delete Calendars, I’m going to use the GetOwnCalendarsFeed function to get a list of all of the calendars that the “authenticated user has owner access to.”[10]

The GetOwnCalendarsFeed function returns a CalendarListFeed class instance. This contains some information (like a title) along with a list of CalendarListEntry classes. Each CalendarListEntry in this list represents a calendar. An example of a function that uses GetOwnCalendarsFeed and then prints out information about each calendar can be found in Listing 2. Sample output from this function can be found in Listing 3. The function was passed a CalendarService after logging in:

list_own_calendars(calendar_service)

Listing 2

def list_own_calendars(calendar_service):

    try:
        #Get the CalendarListFeed
        all_calendars_feed = calendar_service.GetOwnCalendarsFeed()
    except Exception, e:
        print "Error getting all calendar feed: %s" % (e)
        return
    #Print the feed's title
    print all_calendars_feed.title.text
    #Now loop through all of the CalendarListEntry items.
    for (index, cal) in enumerate(all_calendars_feed.entry):
        #Print out the title and the summary if ther is one
        if (cal.summary is not None):
            print "%d) %s - Summary: %s" % (
                index, cal.title.text, cal.summary.text)
        else:
            print "%d) %s" % (index, cal.title.text)
        #Print out the authors
        print "\tAuthors:"
        for author in cal.author:
            print "\t\t%s" % (author.name.text)
        #Print out other information
        print "\tPublished: %s \n\tUpdated: %s \n\ttimezone: %s" % (
            cal.published.text, cal.updated.text, cal.timezone.value)
        print "\tColour: %s \n\tHidden: %s \n\tSelected: %s" % (
            cal.color.value, cal.hidden.value, cal.selected.value)
        print "\tAccess Level: %s" % (cal.access_level.value)

Listing 3

Mark Mruss's Calendar List
0) Mark Mruss - Summary: Main Calendar
	Authors:
		Mark Mruss
	Published: 2008-02-07T04:15:15.701Z
	Updated: 2008-02-07T03:43:26.000Z
	timezone: America/Toronto
	Colour: #5229A3
	Hidden: false
	Selected: true
	Access Level: owner
1) PythonMagazine - Summary: Calendar for Articles
	Authors:
		PythonMagazine
	Published: 2008-02-07T04:15:15.702Z
	Updated: 2008-02-07T03:37:15.000Z
	timezone: America/Toronto
	Colour: #0D7813
	Hidden: false
	Selected: true
	Access Level: owner

Adding a Calendar

If we want to add a calendar, we need to create a CalendarListEntry class instance and then call the CalendarService classes InsertCalendar function. Let’s say I wanted to add a Banking calendar to my account, I could do the following to create the CalendarListEntry:

new_calendar = gdata.calendar.CalendarListEntry()
new_calendar.title = gdata.atom.Title(text="Banking")
new_calendar.summary = gdata.atom.Summary(text="Bills and payments")
new_calendar.timezone = gdata.calendar.Timezone(value="America/Toronto")
new_calendar.hidden = gdata.calendar.Hidden(value="false")
new_calendar.selected = gdata.calendar.Selected(value="true")

The calendar now needs to be added to the account:

try:
    created_calendar = calendar_service.InsertCalendar(new_calendar)
except gdata.service.RequestError, e:
    print "Error adding calendar: %s" % (e[0]["reason"])

This will add the new calendar to the authenticated users list of calendars, make it visible, and select it. Notice that InsertCalendar also returns a CalendarListEntry instance. This is the calendar that was actually added to the authenticated user’s list of calendars. It is wrapping the XML that represents the actual calendar, as opposed to the smaller version that we created for insertion.

Deleting a Calendar

Deleting a Calendar is very easy. You simply need to call the CalendarService classes DeleteCalendarEntry function. The DeleteCalendarEntry function takes three parameters which are documented in the source code:

1. edit_uri – The edit uri (Uniform Resource Identifier) of the Calendar that you want to delete.
2. url_params – Defaults to None. A dictionary containing URL parameters that will be included in the delete.
3. escape_params – Defaults to True. A boolean that controls whether or not the url_params will be escaped.

Note: There is another optional parameter: extra_headers, which is the second parameter, but at this point it does not seem to be used at all in the source code.

The simplest case is to ignore the optional parameters and simply pass in the edit uri of the calendar that you wish to delete. You can get the edit uri of a calendar by calling the GetEditLink function of the CalendarListEntry instance that represents the calendar that you are going to delete. An example of a function that will delete a calendar can be seen in Listing 4. This function takes a CalendarService instance and a CalendarListEntry instance as parameters.

Listing 4

def delete_calendar(calendar_service, calendar):
    e_link = calendar.GetEditLink()
    if (e_link is not None):
        try:
            calendar_service.DeleteCalendarEntry(e_link.href)
        except gdata.service.RequestError, e:
            print "Error deleting calendar: %s" % (e[0]["reason"])

Listing Events

Now let’s take a look at events. Events are items that are added to a specific calendar. If you want to remind yourself to pay your bills at the end of the month you might add that to your banking calendar. That “note” on your calendar is an event.

You can get a list of events for the primary calendar using the CalendarService classes GetCalendarEventFeed function. Since you may be working with more than one calendar, it’s probably more useful to be able to list the events for a specific calendar. You can do this in one of two ways:

1. You can pass in the optional uri parameter to the GetCalendarEventFeed function. From testing I found that a Calendar’s “alternate” link works.
2. You can use the CalendarService classes CalendarQuery function to query for a specific calendars event feed.

If you want to use the first option you can use the CalendarListEntry classes GetAlternateLink member function to get the uri, and then pass it to GetCalendarEventFeed:

a_link = calendar.GetAlternateLink()
#Make sure the link is valid
if (a_link is not None):
    event_feed = calendar_service.GetCalendarEventFeed(a_link.href)

If you use the CalendarQuery method, you need to get the calendar username (or id) of the calendar whose events you want to query. It seems strange to me that there appears to be no way to get a calendar’s username besides parsing one of the calendar’s links. The username of a calendar can be found in the alternate link after “feeds” and before the visibility and projection:

http://www.google.com/calendar/feeds/< <username>>/private/full

Note: You can use the username “default” to query the default calendar.

For the sake of simplicity I will use the alternate link method for my examples. A full example that prints out calendar data and a calendar’s events can be found in Listing 5. The method that prints out the event data is called print_event_feed. It is called near the end of the list_own_calendars method:

# Now Print out the events
print "Events:"
a_link = cal.GetAlternateLink()
if (a_link is not None):
    event_feed = calendar_service.GetCalendarEventFeed(a_link.href)
    print_event_feed(event_feed)

Listing 5

#! /usr/bin/env python

import gdata.calendar.service
import getpass

def print_event_feed(event_feed):

    for index, event in enumerate(event_feed.entry):
        print "\t%d) %s\r\n\tContent: %s" % (
                index, event.title.text, event.content.text)
        print "\t\tWho:"
        for person in event.who:
            print "\t\t\tName: %s\n\t\t\temail: %s" % (person.name
                , person.email)
        print "\t\tAuthors:"
        for author in event.author:
            print "\t\t\t%s" % (author.name.text)
        print "\t\tWhen:"
        for e_index, e_time in enumerate(event.when):
            print "\t\t\t%d) Start time: %s\n\t\t\tEnd time: %s" % (
                e_index
                , e_time.start_time
                , e_time.end_time)

def list_own_calendars(calendar_service):

    try:
        #Get the CalendarListFeed
        all_calendars_feed = calendar_service.GetOwnCalendarsFeed()
    except Exception, e:
        print "Error getting all calendar feed: %s" % (e)
        return
    #Print the feed's title
    print all_calendars_feed.title.text
    #Now loop through all of the CalendarListEntry items.
    for (index, cal) in enumerate(all_calendars_feed.entry):
        #Print out the title and the summary if there is one
        if (cal.summary is not None):
            print "%d) %s - Summary: %s" % (
                index, cal.title.text, cal.summary.text)
        else:
            print "%d) %s" % (index, cal.title.text)
        #Print out the authors
        print "\tAuthors:"
        for author in cal.author:
            print "\t\t%s" % (author.name.text)
        #Print out other information
        print "\tPublished: %s \n\tUpdated: %s \n\ttimezone: %s" % (
            cal.published.text, cal.updated.text, cal.timezone.value)
        print "\tColour: %s \n\tHidden: %s \n\tSelected: %s" % (
            cal.color.value, cal.hidden.value, cal.selected.value)
        print "\tAccess Level: %s" % (cal.access_level.value)
        # Now Print out the events
        print "\tEvents:"
        a_link = cal.GetAlternateLink()
        if (a_link is not None):
            event_feed = calendar_service.GetCalendarEventFeed(a_link.href)
            print_event_feed(event_feed)

def main():
    username = raw_input("Enter your username: ")
    password = getpass.getpass("Enter your password: ")

    calendar_service = gdata.calendar.service.CalendarService(username
        , password
        , "PythonMagazine-Calendar_Example-1.0")
    try:
        calendar_service.ProgrammaticLogin()
    except gdata.service.BadAuthentication, e:
        print "Authentication error logging in: %s" % e
        return
    except Exception, e:
        print "Error Logging in: %s" % e
        return

    list_own_calendars(calendar_service)

if __name__ == "__main__":
    main()

An example of the output produced by Listing 5 can be seen in Listing 6. Don’t mind the formatting – it’s not pretty. It’s merely meant to give you an example of some of the data that you can mine from an event feed.

Listing 6

Mark Mruss's Calendar List
0) Mark Mruss - Summary: Main Calendar
	Authors:
		Mark Mruss
	Published: 2008-02-12T02:30:18.731Z
	Updated: 2008-02-08T04:03:10.000Z
	timezone: America/Toronto
	Colour: #5229A3
	Hidden: false
	Selected: true
	Access Level: owner
	Events:
	0) Dinner at the Drake
	Content: Dinner
		Who:
			Name: Mark Mruss
			email: mark.mruss@gmail.com
		Authors:
			Mark Mruss
		When:
			0) Start time: 2008-02-15T21:00:00.000-05:00
			End time: 2008-02-15T22:30:00.000-05:00
	1) Cezanne's Closet
	Content: Cezanne's Closet
		Who:
			Name: Mark Mruss
			email: mark.mruss@gmail.com
		Authors:
			Mark Mruss
		When:
			0) Start time: 2008-02-09
			End time: 2008-02-11

Adding an Event

Adding a new event to a calendar is very similar to creating a new calendar. You need to create a new CalendarEventEntry instance. You then populate it with options, and pass it to the CalendarService classes InsertEvent function, along with the alternate link of the calendar to which you would like to add the event.

If you take a look at Listing 7 you can see a simple example of how to add an all day event. If you want to specify an event that lasts for less then a day, or include a specific start and end time, you need to use the “RFC 3339 timestamp” format for your start_time and end_time values. Also notice that just like adding a Calendar, the newly created event is returned by the InsertEvent function.

Listing 7

a_link = calendar.GetAlternateLink()
if (a_link is not None):

    new_event = gdata.calendar.CalendarEventEntry()
    new_event.title = gdata.atom.Title(text="New Article")
    new_event.content = gdata.atom.Content(text="Write Article")
    new_event.when.append(gdata.calendar.When(
        start_time="2008-02-20"
        , end_time="2008-02-21"))
    try:
        created_event = calendar_service.InsertEvent(new_event
            , a_link.href)
    except gdata.service.RequestError, e:
        print "Error adding event: %s" % (e[0]["reason"])

Deleting an Event

Deleting an event is (you guessed it) almost identical to deleting an entire calendar. This is because both helper functions wrap the same GDataService base class function. However the 'DeleteEvent event function does a little bit more work than the DeleteCalendarEntry function by making sure that your edit_uri is in the correct format.

To delete an event you simply need to call the DeleteEvent function of the CalendarService class. An example of a function that will delete an event can be seen in Listing 8. This function takes a CalendarService instance and a CalendarEventEntry instance as parameters. As with the majority of these actions, the CalendarService instance should already be authenticated.

Listing 8

def delete_event(calendar_service, event):
    e_link = event.GetEditLink()
    if (e_link is not None):
        try:
            calendar_service.DeleteEvent(e_link.href)
        except gdata.service.RequestError, e:
            print "Error deleting event: %s" % (e[0]["reason"])

Conclusion

Hopefully this article has given you a small taste of what is possible with the gdata-python-client module and Google Calendars. Maybe you can already imagine a use for these features in your next Python application? Please keep in mind that there is much more that can be done with Google Calendars, including updating existing Calendars and Events (CalendarService member functions: UpdateCalendar and UpdateEvent), and almost any other task you can perform using the “online” version of the Google Calendar service.

Also, it is important to note that the Python documentation for the Google Calendar service is sparse but growing. A lot of what is contained in this article was found through browsing the examples, the gdata-python-client source code, other people’s examples, as well as a whole bunch of trial and error on my part. Since this is the case, there may be other ways, or more preferred methods of accomplishing what I have shown here.

[1] http://code.google.com/p/gdata-python-client/
[2] http://code.google.com/apis/gdata/index.html
[3] http://code.google.com/p/gdata-python-client/
[4] http://code.google.com/apis/calendar/developers_guide_python.html
[5] http://code.google.com/apis/calendar/reference.html
[6] http://www.google.com/calendar
[7] gdata.calendar.service.py (From the source comments)
[8] gdata.service.py (From the source comments)
[9] http://code.google.com/apis/accounts/AuthForInstalledApps.html
[10] http://code.google.com/apis/calendar/developers_guide_python.html

By: Mark Mruss

Note: This article was first published the February 2008 issue of Python Magazine

Of all the tasks assigned to programmers, commenting code and writing documentation are among the most disliked. This article introduces you to Python’s documentation strings. While they won’t make commenting your code any more enjoyable, they will provide a systematic approach to doing it, as well as access to additional tools for documentation generation and testing.

You’ve just finished your new Python module. You can’t wait to upload it to the Web and let all the other Python hackers start using it. The only step left is the most dreaded for many programmers: documentation. Unless you’ve been commenting and documenting your code as you wrote it, you’re going to have to go back through each source file, class, and function, trying to remember exactly what your code was supposed to do. Not an enjoyable task.

Sound familiar to you? If it does, you’re probably not using documentation strings, commonly known as doc strings or docstrings (I prefer docstrings, without the space), or any of the handy tools that work with docstrings. This article will introduce you to Python’s docstrings, and a few of the tools that make them a great addition to your code.

Docstrings

If you are not already using docstrings in your Python code, you really should. They provide a standard way to comment your code, giving you and other developers (who might want to use your code at some point) easy access to descriptions of the modules, classes, and functions found within.

At the heart of it, docstrings are simply comments placed in special locations in your Python source code. These comments can then be looked at by tools designed to work with docstrings or other Python programmers using your code. Note that I said using// your code, not reading your code. This is because docstrings are accessible via a Python object’s __doc__ attribute. This is very helpful is you are testing out a new module in Python’s interactive shell and really need to know what sort of parameters a certain function needs.

Pep 257 has a good definition of docstrings: “A docstring is a string literal that occurs as the first statement in a module, function, class, or method definition. Such a docstring becomes the __doc__ special attribute of that object.”[1] Definitions are nice but it might be easier to look at a quick example of some docstrings:

def add(x, y):
    """This is the add function's docstring."""
	return x + y

def subtract(x, y):
    """This is the subtract function's docstring.
    It is longer then the add functions, it goes
    on and on and on and on."""
    return x - y

Since docstrings are comments they can be in any format that you want, however since this is Python there are a few style guidelines that you should probably be aware of. You don’t have to follow these guidlines but if you do it will be easier for other Python programmers to understand and work with your code.

In general there are two types of docstrings: one-line docstrings and multi-line docstrings. The difference between the two should be fairly obvious: one-line docstrings are only one line in length and multi-line docstrings are more then one line in length. One-line docstrings and multi-line docstrings each have different style guidelines that will be explained in more detail in the following two sections.

One-Line Docstrings

Let’s look at a quick example of a one-line docstring for a simple function:

def add(x, y):
    """Return the sum of two numbers."""
    return x + y

In this example """Return the sum of two numbers.""" is the docstring of the add function. If you were to run the following:

print add.__doc__

The output would look like this:

Return the sum of two numbers.

In his “Python Style Guide” Guido van Rossum has a few notes on the preferred style of one line docstrings:

• Triple quotes are used even though the string fits on one line. This makes it easy to later expand it.
• The closing quotes are on the same line as the opening quotes. This looks better for one-liners.
• There’s no blank line either before or after the doc string.
• The doc string is a phrase ending in a period. It prescribes the function’s effect as a command (”Do this”, “Return that”), not as a description: e.g. don’t write “Returns the pathname …” [2]

One-line docstrings should only be used to document the simplest of cases. If what you are documenting does anything complicated, accepts input, or returns a value, it’s probably a good idea to use a multi-line docstring.

Multi-Line Docstrings

Multi-line docstrings should be used to document the majority of your modules, classes, functions, and methods. This is because most of what you are programming performs tasks more complicated then that which can fit into a single sentence summary. Multi-line docstrings should be used to documents the input, output, and complex behaviour of your objects. Like one-line docstrings, multi-line docstrings should start with a single sentence summary. After that there should be a blank line and then a more detailed description. The blank line separating the one line summary and the additional information is important as certain tools will use the blank line to separate the summary from the rest of the docstring. An example of a multi-line comment can be found in Listing 1.

Listing 1

def subtract(x, y):
    """Return the difference between two numbers.

    Arguments:
    x -- The minuend.
    y -- The subtrahend.

    Returns:
    A number, the difference between x and y (ie. x - y)

    """
    return x - y

What to Document

There are also style guidelines that dictate what you should include in your multi-line docstrings when documenting different sections of your code. These are general guidelines but following them will help ensure that your code is well documented and easily understood by other Python programmers. For more information on this please see Guido’s “Python Style Guide”.[2]

Modules – Document the module and provide a one line summary of everything that is exported by the module. (eg. classes, exceptions, and functions).

Classes – Summarize the functionality of the class. List all public methods and data members of the class. If there is any addition information needed to subclass the class, or if there is an additional interface for subclasses provide a description.

Functions and Methods – Summarize the functionality of the function and “document its arguments, return value(s), side effects, exceptions raised, and restrictions on when it can be called (all if applicable).”[3]

An example of all of these can be found in Listing 2.

Listing 2

#!/usr/bin/env python
"""A simple math module.

Exported Classes:

Math -- A simple math class with mathematical functions.

"""

class Math(object):
    """A simple math class with mathematical functions.

    Public functions:
    add -- Adds two numbers together and
    returns the result.

    subtract -- Returns the difference between
    two numbers.

    """

    def subtract(self, x, y):
        """Return the difference between two numbers.

        Arguments:
        x -- The minuend.
        y -- The subtrahend.

        Returns:
        A number, the difference between x and y (ie. x - y)

        """
        return x - y

    def add(self, x, y):
        """Return the sum of two numbers.

        Arguments:
        x -- Number to be summed.
        y -- Number to be summed.

        Returns:
        A number, the sum of x and y (ie. x + y)

        """
        return x + y

Documentation Generation

Generally if you have a complicated module or API people would rather read a help file, or online documentation, as opposed to constantly having find and browse the source code. Thankfully for us there are many different documentation generation tools out there that make use of docstrings. So when you are writing your docstrings, you aren’t just commenting your source code you’re also writing your help file!

The easiest tool to use is PyDoc, it’s a module, and a stand-alone application, that has been included in the Python standard library since version 2.1. There is much that can be done with PyDoc but for this column we are going to focus on it’s documentation generation. PyDoc can take the docstrings found within a module and output them as either simple text documentation (much like UNIX or Linux man pages) or HTML documentation.

Creating HTML documentation of the SimpleMath module used in Listing 2 is very easy. On a UNIX like computer (Linux, OS X, etc.) it can be accomplished using the following command:

$ pydoc -w /home/selsine/python/SimpleMath.py

On Windows the command will look something like this:

C:>c:Python25Libpydoc.py -w c:pythonSimpleMath.py

This will create an HTML file named SimpleMath.html in the current folder. A sample of what the generated HTML looks like can be seen in Figure 1.

Figure 1 - pydoc

While PyDoc is a great tool and easy to use because it is in the standard library, there are a few other tools out there that you might consider using. If you look at the HTML file that PyDoc generates you will notice that it does not mark up your docstrings. It simply reads them from your source code and spits them back out. If you are looking for something a little bit fancier with a few more options you might consider Epydoc[4] or docutils[5].

Both Epydoc and docutils use simple markup languages to give the documentation generated a bit more punch. Docutils uses the reStructuredText markup language. While Epydoc uses the Epytext markup language, as well as being able to work with Javadoc and reStructuredText. An example of the HTML that Epydoc produces can be seen in Figure 2. The code that was used to generate the HTML can be found in Listing 3.

Figure 2 – Epydoc

Listing 3

#!/usr/bin/env python
"""A simple math module.

Exported Classes:

Math -- A simple math class with mathematical functions.

"""

class Math(object):
    """A simple math class with mathematical functions.

    Public functions:
    add -- Adds two numbers together and
    returns the result.

    subtract -- Returns the difference between
    two numbers.

    """

    def subtract(self, x, y):
        """Return the difference between two numbers.

        @type   x: number
        @param  x: The minuend.
        @type   y: number
        @param  y: The subtrahend.

        @rtype: number
        @returns: A number, the difference between x and y (ie. x - y)

        """
        return x - y

    def add(self, x, y):
        """Return the sum of two numbers.

        @type   x: number
        @param  x: Number to be summed.
        @type   y: number
        @param  y: Number to be summed.

        @rtype: number
        @returns: A number, the sum of x and y (ie. x + y)

        """
        return x + y

def _test():
    import doctest
    doctest.testmod()

if __name__ == "__main__":
    _test()

Doctest

One of the most interesting uses of docstrings is unit testing using the doctest module. As we all know testing our code is important, and (as many of us have begun to learn) writing unit tests for large projects is a great way to ensure that changes in one area of a project don’t cause problems elsewhere in the code. If you don’t know what a unit test is it’s basically a simple test case to prove whether a specific area of your code is functioning properly. Generally at least one unit test is created for each object in order to test the correctness of the project as a whole.

In a nutshell the the doctest module searches your dostrings "for pieces of text that look like interactive Python sessions, and then executes those sessions to verify that they work exactly as shown."[6] This means that it will search your docstrings for lines that start with >>> or with ... if they are the continuation of a statement (i.e. the inside of a function). If there is output generated by the statements it “must immediately follow the final ‘>>> ‘ or ‘… ‘ line containing the code, and … extends to the next ‘>>> ‘ or all-whitespace line.”[7] Adding doctests doesn’t just add unit tests to your code it also provides working examples in your docstrings and documentation.

Since doctests are formatted to look like interactive Python sessions a simple way to write them is to use Python’s interactive shell. You do this by simply executing your code in the interactive shell, and then copying and pasting the resulting test into your docstrings. For example, if we were to use this method to write a doctest for our subtract method we could do something like the following in the interactive shell:

>>> from SimpleMath import Math
>>> simple_math = Math()
>>> simple_math.subtract(10, 7)
3
>>>

We don’t need to import our module for the doctest so all we will need to copy from the interactive shell are the middle three lines:

>>> simple_math = Math()
>>> simple_math.subtract(10, 7)
3

As you can see, what we have here is a test compromising of two lines of Python code and then the expected result. The SimpleMath module containing a doctest for each function can be found in Listing 4.

Listing 4

#!/usr/bin/env python
"""A simple math module.

Exported Classes:

Math -- A simple math class with mathematical functions.

"""

class Math(object):
    """A simple math class with mathematical functions.

    Public functions:
    add -- Adds two numbers together and
    returns the result.

    subtract -- Returns the difference between
    two numbers.

    """

    def subtract(self, x, y):
        """Return the difference between two numbers.

        >>> simple_math = Math()
        >>> simple_math.subtract(10, 7)
        3

        @type   x: number
        @param  x: The minuend.
        @type   y: number
        @param  y: The subtrahend.

        @rtype: number
        @returns: A number, the difference between x and y (ie. x - y)

        """
        return x - y

    def add(self, x, y):
        """Return the sum of two numbers.

        >>> simple_math = Math()
        >>> simple_math.add(10, 7)
        17

        @type   x: number
        @param  x: Number to be summed.
        @type   y: number
        @param  y: Number to be summed.

        @rtype: number
        @returns: A number, the sum of x and y (ie. x + y)

        """
        return x + y

def _test():
    import doctest
    doctest.testmod()

if __name__ == "__main__":
    _test()

If you look at Listing 4 you will also notice the following code at the end of the source:

def _test():
    import doctest
    doctest.testmod()

if __name__ == "__main__":
    _test()

This is the code that will be executed if the module is launched directly from the command line. The code will import the doctest module and then use the testmod method to test the current module. Now when we run our SimpleMath.py file directly we will get the following:

$ python SimpleMath.py
$

Since nothing was written out to the command line we know that all of the doctests were successful. If you want to get more information you can use the -v flag to produce verbose output:

$ python SimpleMath.py -v

If you were to encounter an error it would look something like Listing 5.

Listing 5

**********************************************************************
File "SimpleMath.py", line 26, in __main__.Math.subtract
Failed example:
    simple_math.subtract(10, 7)
Expected:
    4
Got:
    3
**********************************************************************
1 items had failures:
   1 of   2 in __main__.Math.subtract
***Test Failed*** 1 failures.

As an added bonus if you are using Epydoc or docutils to generate your documentation, both tools will recognize doctest sections and highlight them. An example of what it looks like when Epydoc does this can be seen in Figure 3.

Figure 3 - doctest

Conclusion

Hopefully by this point you can see how useful docstrings can be to any Python code that you write. They give you a structured way to document your source code, the ability to easily generate great looking documentation, and a simple way to add unit tests to your code. But that's not all, as more and more people start using doctrings you can be sure that the list of docstring tools will continue to grow.

We all know that commenting source code and writing documentation are among the least enjoyable tasks a programmer can face. In fact the only thing worse then commenting and documenting is trying to use code with no comments and poor documentation! So do me, and yourself, a favour: start writing those docstrings.

[1] http://www.python.org/dev/peps/pep-0257/#what-is-a-docstring
[2] http://www.python.org/doc/essays/styleguide.html
[3] http://www.python.org/doc/essays/styleguide.html
[4] http://epydoc.sourceforge.net/
[5] http://docutils.sourceforge.net/
[6] http://docs.python.org/lib/module-doctest.html
[7] http://docs.python.org/lib/doctest-finding-examples.html

By: Mark Mruss

Note: This article was first published the January 2008 issue of Python Magazine

Iterators, iterables, and generators are features handled so wall by Python that people programming in other languages cannot help but drool over. Fortunately for us, creating iterators, iterables and generators is a relatively simple task. This article introduces the concepts of iterators, iterables, and generators and illustrates how easy it is to add them to your code.

1. Introduction
2. Iteration in Python
3. An Initial Example
4. Creating An Iterator
5. Looking More Closely At The Iterator
6. The Upside And Downside Of Iterators
7. Generators
8. Looking Closely At The Generator
9. But What About Iterables?
10. Creating An Iterable Object
11. Conclusion

Introduction

In this article I’m going to introduce three related Python features: iterators, iterables, and generators. Generators are easy to define, they are functions that create and return an iterator. Iterators and iterables on the other hand, are easier to use than they are to define. An iterable object is a “container object capable of returning its members one at a time.”[1] An iterator object is “An object representing a stream of data. Repeated calls to the iterator’s next() method return successive items in the stream. When no more data is available a StopIteration exception is raised instead. At this point, the iterator object is exhausted and any further calls to its next() method just raise StopIteration again.”[1] You can think of the difference between the two in this way: an iterable object can be iterated over multiple times, whereas an iterator object can only be iterated over once. In general an iterable produces an iterator every time something wants to iterate over its data.

Note: Classes that define the __getitem__ function are also considered iterables, but since that falls outside the scope of this article, it will not be covered here.

In this tutorial, I will begin by discussing iterators, the most basic concept. Then I will move onto generators, and finish by discussing iterables, the most wide open topic of the three.

Iteration in Python

Iterators objects are used in Python in order to iterate over an objects data. For example, we all know how to do this in Python when we work with lists:

my_list = [1,2,3]
for num in my_list:
	print num

This code will iterate over the list object my_list and print out all of the list items , i.e., the numbers 1, 2, and 3. Iterating over sequences in this simple and transparent manner happens to be one of my favourite features of Python.

According to our definition above, lists are iterables since you can iterate over them multiple times. In fact, each time you iterate over a list you are actually using a listiterator iterator object produced by the list.

It may not be immediately clear to you when you should add an iteration support to a class. However, the more you work with Python the more you’ll find instances when doing just this is very useful, sometimes the only advantage is cleaner looking code. One nice thing about iterators (and generators too) is that the processing for each item happens as you need it. Instead of collecting all of the data into a list and then running through the list, you will collect each item as you need it. This might not seem like a large difference but imagine if there were tens of thousands of items to process? What if you were collection your data from an online source? Performing all of your processing up front may take a very long time, especially if you only wanted the first few items.

An initial example

In order to explain iteration further, let’s look at a simple example task where we might use iterators. For this example we will create a class that takes a string of characters as input and then converts each character into its byte value. If we were NOT going to use iterators we might do something like what is found in Listing 1.

Listing 1

class ByteValue(object):

	def __init__(self, data):
		self.data = data

	def to_bytes(self):
		bytes = []
		for char in self.data:
			bytes.append(ord(char))
		return bytes

This code is pretty simple. We have a data member, named data, that we use to store the string that was used to initialize the class. In the to_bytes function we loop through the string, converting each character to its byte value using the built in ord function. We store each byte value in a list and once we have collected all of the values we return that list.

When we run the following:

bv = ByteValue("abcdef")
for byte in bv.to_bytes():
	print byte

we would get this as our output:

97
98
99
100
101
102

Creating an Iterator

Let’s convert this into an iterator. Making your class into an iterator requires adding “two methods, which together form the iterator protocol.”[2] The two functions needed are: 1) the __iter__ function; and, 2) the next function. The __iter__ function will return the object itself, while the next function will return the next item. The next function is where the actual iteration work occurs. The next function iterates by returning the next item in the “sequence” each time it is called for as long as there is a “next” item. When there are no more items to iterate over, the next function must raise the StopIteration exception to halt the iteration.

To be clear, in order to make your class an iterator you need to do two things:

1) Add an __iter__ function that returns the object itself (self)

2) Add a next function that returns the next item in the sequence each time it is called. When there are no more items in the sequence, the next function raises a StopIteration exception signal the end of the iteration.

For those of you still confused, the following example will help illustrate how iterators work. If we were to convert our ByteValue class into an iterator object, it might look something like Listing 2.

Listing 2

class ByteValue(object):

	def __init__(self, data):
		self.data = data
		self.current_item = 0

	def __iter__(self):
		return self

	def next(self):
		if (self.current_item == len(self.data)):
			raise StopIteration
		else:
			byte_value = ord(self.data[self.current_item])
			self.current_item += 1
			return byte_value

Let’s compare the code in Listings 1 and Listing 2 in detail, focusing on the iterator in Listing 2. The first difference is the addition of the data member current_item to the class, initialized in the __init__ function. current_item serves as a counter and keeps track of the current character in the string while we iterate over it. The counter must have class scope since the iterator works through successive calls to the next function. If current_item were local to the next function, its value would be reset with each subsequent call, and would not be of much use.

The second difference between the listings is the addition in Listing 2, of the __iter__ function where we return self.

The final addition to Listing 2 is the next function, where we first check to see if current_item is equal to the length of our string. If current_item is equal to the strings length we raise the StopIteration exception to signal the end of the iteration because we have no more characters left to iterate over. If there are more characters to iterate over we calculate the byte value of the current character, increase our current_item counter, and then return the byte value.

Note: Notice that current_item is only initialized when the ByteValue object is created. This happens because according to the iteration protocol, “once an iterator’s next() method raises StopIteration, it will continue to do so on subsequent calls.”[2] If we were to re-initialize current_item we would then be able to iterate over the iterator more than once breaking the iteration protocol.

Now that we have converted our class into an iterator we can use it as follows:

for byte in ByteValue("abcdef"):
	print byte

Doing so would result in:

97
98
99
100
101
102

Notice that we do not store the instance of our ByteValue class in a variable. Doing so would be useless because since ByteValue is an iterator it is only good for one pass of the data. If ByteValue were an iterable (returning an iterator object when __iter__ was called) it would make sense to keep an instance around because we could iterate over the instance more than once. We will look at creating iterables later on in this article.

Looking more closely at the Iterator

Let’s look at what is happening in more detail by examining what is happening behind the scenes during the iteration process. In order to illustrate what is happening in the for loop I will demonstrate the order in which things are being called behind the scenes.

The first step in the iteration process is to call to the __iter__ function in order to get the iterator object that will perform the iteration. Notice that this works on iterator and iterable objects, since iterators returns themselves and iterables return an iterator.

bv = ByteValue("abcdef")
iterator = bv.__iter__()

Now that we have the iterator object, we start iterating by calling the next function in order to get the next value:

print iterator.next()

This executes the next function which will return the byte value of character in the string with which we are currently working. Since this is the first call to the next function current_item will be zero and we will calculate the byte value of the first character in our string (’a') resulting in:

97

If we continue the iteration process by calling the next function six more times we would get the results shown in Listing 3. Notice that we have now made a total of seven calls to the next function, one more then the number of characters in our string. I’m running the python code from a file (iter.py found in Listing 4). Depending on how you are running it, you might get slightly different results. However, the most important thing to observe in this example is the exception that is raised.

Listing 3

97
98
99
100
101
102
Traceback (most recent call last):
  File "Listing4.py", line 34, in ?
    main()
  File "Listing4.py", line 30, in main
    print iterator.next()
  File "Listing4.py", line 13, in next
    raise StopIteration
StopIteration

Listing 4

#!/usr/bin/env python

class ByteValue(object):

	def __init__(self, data):
		self.data = data
		self.current_item = 0
	def __iter__(self):
		return self

	def next(self):
		if (self.current_item == len(self.data)):
			raise StopIteration
		else:
			byte_value = ord(self.data[self.current_item])
			self.current_item += 1
			return byte_value
		return self.data

def main():
    for v in ByteValue("abc"):
        if v in ByteValue("abc"):
            print "We have a %d" % v

    bv = ByteValue("abcdef")
    iterator = bv.__iter__()
    print iterator.next()
    print iterator.next()
    print iterator.next()
    print iterator.next()
    print iterator.next()
    print iterator.next()
    print iterator.next()

if __name__ == "__main__":
	# Someone is launching this directly
	main()

For the most part you won’t be calling an iterator object’s __iter__ or next functions manually, instead you’ll probably just let the for loop do it all for you.

The upside and downside of Iterators

Now that our class is an iterator object we can use any of the built-in functions and methods that work on iterators and iterables, such as: the sum, tuple, sorted, and list functions, to name a few.

In the first example the bytes function returned a list object that we could work with. If we want a list instead of an iterator for any reason, it will be as simple as using the list function, which takes an iterator as a parameter and returns a list:

list(ByteValue("abcdef"))

If we want a sorted list:

sorted(ByteValue("abcdef"))

If we want the sum of all of the bytes:

sum(ByteValue("abcdef"))

While very useful, iterators do have some downsides to them. The most obvious is that they are only good for one pass over the data. They also generally require you to add extra data members to your class in order to keep track of your current iterator position. Depending on what you are iterating over, this process can become quite complex. Iterators also only allow you to perform one “type” of iteration, i.e., in one direction or over one piece of internal data. You might address this by adding flags to your class but this will further clutter the class and decrease readability. So how can you simply add multiple types of iteration to a class? Enter the generator!

Generators

A generator is a function that creates, or generates, an iterator. In order for a function to become a generator it must return a value using the yield keyword.

Generators are interesting because they are functions, yet execution does not run through them as it does in a normal function. The first time execution enters a generator function it will start at the beginning of the function and continue until the yield keyword is encountered. When the iteration continues, execution will continue in the generator function on the statement immediately following the yield keyword. All local variables in the function will remain intact. If the yield statement occurs within a loop, execution will continue within the loop as though execution had not been interrupted.

Continuing with the ByteValue example, let’s add a generator function named reverse that can be used to iterate through the byte vales of the string in reverse order:

def reverse(self):
	current_item = len(self.data)
	while (current_item > 0):
		current_item -= 1
		yield ord(self.data[current_item])

So what’s going on here? The first thing to notice is that we did not add anymore data members to our class, this generator is a self-contained unit. Secondly, since this is a generator, our counter current_item can be a local variable.

In the reverse function the first step is to initialize current_item, which represents the current character in the string, to be equal to the length our string. We initialize it to the length of the string instead of zero since we are iterating through the string in reverse. Next we have a while loop that loops while current_item is greater than zero. We then subtract one from our counter, to give us the current character to process. Finally, we yield the byte value of the current character.

Note: We subtract one from our counter the first time through the loop because Python is zero-based and the length of a list minus one gives us the position of the last item in the list. In our examples we have used the following string:

abcdef
012345

When we calculate the length of the string we get 6. We then subtract 1 from that number, leaving 5, which is the index of the last number in the string.

Making use of our new generator function, we run the following:

bv = ByteValue("abcdef")
for byte in bv.reverse():
	print byte

We get our favourite byte values in reverse:

102
101
100
99
98
97

Looking closely at the Generator

Let’s take a detailed look at what is happening in the generator in the same way that we did earlier with the iterator object. The first thing that happens when you call a generator function is NOT the execution of the actual function, rather, it is the creation of a generator object. Running the following:

bv = ByteValue("abcdef")
gen = bv.reverse()
print gen

will result in:

<generator object at 0xb7d8e04c>

This demonstrates that, as stated above, the first call to our generator function does not return the byte value of that last character in the string, instead it creates a generator object. In fact the first time a generator function is called, the actual function is not executed at all. A generator object is “what Python uses to implement generator iterators. They are normally created by iterating over a function that yields values”.[3]

Once we have a generator object we can start calling its next function (sound familiar?) to perform the action iteration. An example of this can be found in Listing 5. The results of this execution can be found in Listing 6. The results will seem very familiar to you, especially the StopIteration exception.

Listing 5

#!/usr/bin/env python

class ByteValue(object):

	def __init__(self, data):
		self.data = data
		self.current_item = 0
	def __iter__(self):
		return self

	def next(self):
		if (self.current_item == len(self.data)):
			raise StopIteration
		else:
			byte_value = ord(self.data[self.current_item])
			self.current_item += 1
			return byte_value
		return self.data

	def reverse(self):
		current_item = len(self.data)
		while (current_item > 0):
			current_item -= 1
			yield ord(self.data[current_item])

def main():

	bv = ByteValue("abcdef")
	gen = bv.reverse()
	print gen.next()
	print gen.next()
	print gen.next()
	print gen.next()
	print gen.next()
	print gen.next()
	print gen.next()

if __name__ == "__main__":
	# Someone is launching this directly
	main()

Listing 6

102
101
100
99
98
97
Traceback (most recent call last):
  File "Listing5.py", line 40, in ?
    main()
  File "Listing5.py", line 36, in main
    print gen.next()
StopIteration

If we want to look at the contents of the generator object we could use the following code:

print dir(gen)

And getting the following results:

['__class__', '__delattr__', '__doc__', '__getattribute__', '__hash__', '__init__', '__iter__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__str__', 'gi_frame', 'gi_running', 'next']

Notice that the generator object contains the __iter__ and next functions making the generator object itself an iterator. Because the generator itself is an iterator object, the same built-in iterator functions I mentioned earlier can also be used on our generators:

print sum(bv.reverse())

It is also important to remember that your generator or iterator does not have to perform only “dumb” iteratation, simply moving through some sort of an internal list. Rather, it can make decisions just like any other block of code.

For example, let’s say that in our reverse generator we want to use the byte value 99 as an end condition. We can do something similar to the example found in Listing 7.

Listing 7

def reverse(self):
	current_item = len(self.data)
	while (current_item > 0):
		current_item -= 1
		value = ord(self.data[current_item])
		if (value == 99):
			return
		yield value

In Listing 7 if the byte value equals 99 we return from our generator function using the return keyword. Since we didn’t yield anything this will cause the StopIteration exception to be fired halting the iteration.

Be careful about getting too smart with your iteration because you cannot return any information from a generator you can only yield it. So if you tried to execute the code in Listing 8, in an attempt to return one last value before quitting (or if you wanted to return a success or failure code) you will get the following error when the return value line of code is executed:

SyntaxError: 'return' with argument inside generator

Listing 8

def reverse(self):
	current_item = len(self.data)
	while (current_item > 0):
		current_item -= 1
		value = ord(self.data[current_item])
		if (value == 99):
			return value
		yield value

But what about iterables?

I’m sure that by now you are wondering about the iterable objects that I mentioned at the start of this tutorial. As you have probably guessed, simply making your class an iterator isn’t that useful unless it is only performing one task like our ByteValue example. Since your classes will generally be performing more than one task, you will likely want to make your class an iterable object rather than an iterator object. To recap, iterable objects return an iterator object when their __iter__ function is called, which allows for multiple passes over their data.

Creating an Iterable object

Since the definition of an iterable object is an object that returns an iterator object when its __iter__ function is called, creating an iterable object can be done in a variety of ways. Two options that come to mind are: 1)creating an iterator helper class to perform the iteration and, 2) using a generator function. I prefer using a generator function since it keeps the functionality within the main class.

See Listing 9 for an example of creating an iterarable object using a generator. You will see that we have replaced the next function with the forward function. The forward function is a generator that iterates through the data in the “forward” direction. In the __iter__ function we return the results of a call to the forward function, a generator object. Since generator objects contain the iterator protocol and are, in fact iterators, by returning one from our __init__ function we have successfully created an iterable.

Listing 9

class ByteValue(object):

	def __init__(self, data):
		self.data = data

	def __iter__(self):
		#We are an iterable, so return our iterator
		return self.forward()

	def forward(self):
		#The forward generator
		current_item = 0
		while (current_item < len(self.data)):
			byte_value = ord(self.data[current_item])
			current_item += 1
			yield byte_value

	def reverse(self):
		#The reverse generator
		current_item = len(self.data)
		while (current_item > 0):
			current_item -= 1
			yield ord(self.data[current_item])

def main():
    bv = ByteValue("abc")
    for v in bv:
        if v in bv:
            print "We have a %d" % v

if __name__ == "__main__":
    main()

Now that we have an iterable object, we can iterate over it as many times as we want. We can even have fun with nested iteration:

bv = ByteValue("abcdef")
for value in bv:
	print value
	for second_value in bv:
		print second_value

Conclusion

This concludes our introduction to iterators, iterables, and generators. I hope I have demonstrated the immense power and flexibility that they provide. In general, making your object an iterable object and/or using generators allows for more flexibility than simple iterator objects provide. For most complex classes or complex sets of data, multiple iterations are a given.

With all of the praise that I have heaped upon iterators, iterables, and generators, it’s important to remember that they are not a panacea and should not be used in every case where a sequence of items is needed. There are many instances where a returning a list is the desired result. This being said, iterators, iterables, and generators are extremely useful and provide a great way to loop through data.

[1] http://docs.python.org/tut/node18.html
[2] http://docs.python.org/lib/typeiter.html
[3] http://docs.python.org/api/gen-objects.html

So just to prove that I’m a masochist and that the Dodger Editor is not dead (even though I don’t think that anyone has been using it) I thought I’d post a quick update. No the editor is not dead and no neither am I.

I’m still writing for Python Magazine and working on the editor in some of the free time that I get. I just added a selection cursor mode to the editor which makes it easier for me to use. You can see it in the second row of the pallet in the following screen shot.

Now with a Select Mode!

If you want access to the newest code you can grab it from the mercurial repository: http://freehg.org/u/selsine/dodger/

If anyone is at all interested in this project, whether it’s using or helping to develop, or just some suggestions please let me know. I’m always open to opinions. If I get the time I’d like to work on a short post showing you how you can use the Dodger Editor to create the level files for a game, when that will be I don’t know but hopefully soon.

It’s been a long time since I worked on TextWidget at all, but since someone posted a question about it I decided to fix the issue and re-release the source. But since I didn’t want to simply update the blog post I decided to give the project a proper home on google code: http://code.google.com/p/textwidget/

The project is really simple and meant as an easy way for you to create “text buttons” for your PyGame projects. It’s not meant to be the definitive way to do this, just a simple solution for people that just want to drop a class in and have working “text buttons”. It’s LGPL so you can use it in whatever way you want. If you do decide to use it please drop me an email and let me know.

For more information on how to use the project please take a look at the initial blog post.

By: Mark Mruss

Note: This article was first published the December 2007 issue of Python Magazine

While the command line will never cease to be useful, nothing will impress your friends more than your latest python masterpiece wrapped up in a slick cross-platform Graphical User Interface (GUI). This tutorial will show you how to create a simple GUI in Python using PyQt4.

1. Introduction
2. Installing PyQt4
3. Your First PyQt4 Application
4. The Main Window
5. Adding Some Widgets
6. Signal Handling
7. Displaying a Message
8. Conclusion

Introduction

While writing console applications and modules using Python can be very enjoyable, I think that almost everyone learning Python eventually wants to do one thing: create a application with a full Graphical User Interface (GUI). Fortunately for Python users, there are a few options available to achieve this. One of the more interesting options is PyQt4, Python bindings for the fourth version of the famous cross platform application development API Qt.

Qt, owned by Trolltech software, is probably most famous as the foundation for the KDE window manager on Linux. PyQt4, and PyQt version 3, were created and are maintained by Riverbank software. Qt and PyQt4 are both open source and free for open source applications, but if you wish to develop commercial applications you will need to purchase the commercial versions of both Qt and PyQt4.

One of the best features of Qt is that it is a cross platform library which means that it gives you easy access to the three main desktop environments: Linux, Windows, and OS X. This is an important requirement for me because when I’m developing a small open source application I want it available on as many platforms as possible. The nice thing about Qt’s latest version, Qt4, is that it also gives your GUI a native look and feel on the different operating systems.

This article will create a simple application to introduce you to the basic features of PyQt4. The application will consist of a single window that does the following: accepts user input, responds to a button click, and displays a message using a pop-up dialog.

Installing PyQt4

Before installing PyQt4 you need to have Python 2.5 or newer and Qt version 4 installed. You can download Python 2.5 from the main Python website.[1] Qt version 4 is available from the TrollTech website’s Downloads section.[2] Installing PyQt4 on Linux, Windows, or OS X is relatively easy, and I know since I installed it on each of them. You can download the different PyQt4 packages from the Riverbank website, including an all in one (Qt version 4 included) installer for Windows.[3] There are also installation instructions in the PyQt4 online documentation that are worth a read if you are new to installing software from source.[4] If you are using a modern Linux distribution, you may want to consider using your package manager to install PyQt4 as it should being along all the necessary dependencies for you.

Your First PyQt4 Application

Now that you have PyQT4 properly installed, let’s dive right in and create an application. The first step is to start a new Python file, I called mine PyQt4Intro.py but you can call it whatever you like. Set it up using the following code:

#!/usr/bin/env python
import PyQt4

if __name__ == "__main__":
	# Someone is launching this directly
	pass

If you’ve read any of my other columns, you may recognize this code as a general template that I use for my Python projects. The major difference in this example is that we import PyQt4. After you have set your file up run the code. If all goes well nothing will happen. If you do get an error, something like the following, it probably means that PyQt4 is not installed properly:

ImportError: No module named PyQt4

Remember that PyQT4 requires Python 2.5. If you have Python 2.5 and older versions of Python installed, try specifying python2.5 when you run the script:

python2.5 PyQt4Intro.py

Now that we have everything in order, let’s create and show a window. After all this is a GUI tutorial! If you are creating a GUI application using PyQt4 (of course, you can also create a non-GUI application using PyQt4), you will need to create a QApplication instance.

The PyQt4 documentation provides a great description of the “QApplication” class: “The QApplication class manages the GUI application’s control flow and main settings. It contains the main event loop, where all events from the window system and other sources are processed and dispatched. It also handles the application’s initialization and finalization, and provides session management. It also handles most system-wide and application-wide settings. For any GUI application that uses Qt, there is precisely one QApplication object, no matter whether the application has 0, 1, 2 or more windows at any time.”[5]

What we need to do is create the one and only QApplication instance for our application. To do this, we will import two new modules:

import sys
from PyQt4 import QtGui

The first line imports the sys module. This is needed to initialize the QApplication. The second line imports the QtGui module. It allows us to reference QApplication and other QtGui components.

In our main block, we will add the following code:

if __name__ == "__main__":
	# Someone is launching this directly
	# Create the QApplication
	app = QtGui.QApplication(sys.argv)
	# Enter the main loop
	app.exec_()

This will create the QApplication instance, initialized with the command-line parameters that may be passed to our script. This is a necessary parameter to the QApplication class and is the reason that we imported the sys module. The next call is to the exec function which “enters the main event loop and waits until exit() is called or the main widget is destroyed”.[6]

If you run the entire script at this point, which I don’t necessarily recommend since it will seem to hang, you will be running a PyQt4 application. You won’t see anything since there is no visible component. However, the script will continue to run in its main loop instead of returning right after being executed. If you do decide to run this script, use CTRL+Z to exit.

The Main Window

GUI applications without a visible component are not that useful so let’s add a window to our application. We will do this by sub-classing the QMainWindow class. The QMainWindow class “provides a framework for building an application’s user interface. Qt has “QMainWindow” and its related classes for main window management. “QMainWindow” has its own layout to which you can add “QToolBars”, “QDockWindows”, a “QMenuBar”, and a “QStatusBar”.”[7]

Note: There are other ways to create a visible window. For example you could use a QWidget to accomplish much of what we are going to do. I chose to use the QMainWindow since it provides an application framework that you might want to use when building a full GUI application.

We can subclass the QMainWindow as follows:

class HelloWindow(QtGui.QMainWindow):

	def __init__(self, win_parent = None):
		#Init the base class
		QtGui.QMainWindow.__init__(self, win_parent)

Then in the main block we can create and show our main window like this:

app = QtGui.QApplication(sys.argv)
#The Main window
main_window = HelloWindow()
main_window.show()
# Enter the main loop
app.exec_()

The entire source code thus far is found in Listing 1. When you run the code, you will finally be greeted with your very first PyQt4 window. It will look something similar to Figure 1.

Listing 1

#!/usr/bin/env python
import PyQt4
import sys
from PyQt4 import QtGui

class HelloWindow(QtGui.QMainWindow):

	def __init__(self, win_parent = None):
		#Init the base class
		QtGui.QMainWindow.__init__(self, win_parent)

if __name__ == "__main__":
	# Someone is launching this directly
	# Create the QApplication
	app = QtGui.QApplication(sys.argv)
	#The Main window
	main_window = HelloWindow()
	main_window.show()
	# Enter the main loop
	app.exec_()

Figure 1

Adding Some Widgets

A blank window is only slightly more useful then an invisible oen. Therefore our next step is to add some functionality to our window by adding some interactive widgets. In order to do this, we will set the central widget of our main window and use a “geometry manager” (or “layout manager”) class to manage the positioning of our widgets.

We will add a new function to our HelloWindow class called create_widgets where we will create all of our widgets. We will call this function from the __init__ function. The first step in the create_widgets function is to create a widget that will serve as the “central widget” for our main window. As mentioned earlier, the QMainWindow class already has a built-in layout making it easy to add menus, toolbars, and other items common to applications. QMainWindow classes also have a “central widget” which you can think of as the main part of the application. More to the point, the “central widget” is not the toolbars, menu, or docking panes, but the actual functional area of the application. For a greater description of the “central widget” see the PyQt4 website.[8]

We will create and set our “central widget” using the following code:

def create_widgets(self):
	central_widget = QtGui.QWidget()
	self.setCentralWidget(central_widget)

So far this won’t do anything, since we still have not added anything interactive. To add interactivity, we must add widgets to our “central widget”. Rather than place the widgets in static positions, we will use a layout manager to automatically position and adjust our widgets. If you are at all familiar with modern GUI toolkits, the idea of a layout manager should be familiar to you. They serve as a way to “pack” widgets into an area where their positions will be relative to each other and to the window. When you resize the window (or area), geometry manager’s will automatically adjust the size and positions of their child widgets to accommodate the new size.

We will be creating three widgets in our simple application:

1. A QLabel widget to display some static text.
2. A QLineEdit widget to let the user enter some text into an edit field.
3. A QPushButton button widget that will pop up a dialog to display a message when the user clicks on it.

We will create the widgets using the following code. Notice that in the creation of the QLabel and QPushButton widgets we are also setting the text that will appear on each widget. (We can do the same for the QLineEdit, but for now we’ll leave it blank):

def create_widgets(self):
	#Widgets
	self.label = QtGui.QLabel("Say hello:")
	self.hello_edit = QtGui.QLineEdit()
	self.hello_button = QtGui.QPushButton("Push Me!")

Next, we will create a QHBoxLayout instance to serve as the central widget’s layout manager. The QHBoxLayout class is a specific type of layout manager that lays widgets out, and manage their positions, in the horizontal direction. Once we have created the QHBoxLayout instance, we will add our three widgets to it. Adding widgets to a QHBoxLayout will pack them from left to right:

#Horizontal layout
h_box = QtGui.QHBoxLayout()
h_box.addWidget(self.label)
h_box.addWidget(self.hello_edit)
h_box.addWidget(self.hello_button)

The last step in our widget creation is to set h_box as the layout manager for our central widget:

#Create central widget, add layout, and set
central_widget = QtGui.QWidget()
central_widget.setLayout(h_box)
self.setCentralWidget(central_widget)

The entire code can be seen in Listing 2. When you run this code, you will finally be greeted with a window containing actual widgets. This is illustrated in Figure 2.

Listing 2

#!/usr/bin/env python
import PyQt4
import sys
from PyQt4 import QtGui

class HelloWindow(QtGui.QMainWindow):

	def __init__(self, win_parent = None):
		#Init the base class
		QtGui.QMainWindow.__init__(self, win_parent)
		self.create_widgets()

	def create_widgets(self):
		#Widgets
		self.label = QtGui.QLabel("Say hello:")
		self.hello_edit = QtGui.QLineEdit()
		self.hello_button = QtGui.QPushButton("Push Me!")
		#Horizontal layout
		h_box = QtGui.QHBoxLayout()
		h_box.addWidget(self.label)
		h_box.addWidget(self.hello_edit)
		h_box.addWidget(self.hello_button)
		#Create central widget, add layout and set
		central_widget = QtGui.QWidget()
		central_widget.setLayout(h_box)
		self.setCentralWidget(central_widget)

if __name__ == "__main__":
	# Someone is launching this directly
	# Create the QApplication
	app = QtGui.QApplication(sys.argv)
	#The Main window
	main_window = HelloWindow()
	main_window.show()
	# Enter the main loop
	app.exec_()

Figure 2

Signal Handling

Responding to the user’s interaction with the GUI is something that must be done in all GUI applications. Therefore the next step in constructing a GUI application is signal or event handling. If you are unfamiliar with these terms you can thing of it his way: when the end user interacts with your GUI, her interaction will causes signals to be sent to your application. So if she clicks on a button the clicked() signal will be sent. If your application wants to do something when that button is clicked, then you will have to handle that widgets clicked() signal. In our example, we are going to show a pop up a dialog when the user clicks on our button widget.

I find connecting to signals in PyQt4 a bit odd because of a built in macro that needs to be called but the process is relatively easy so I shouldn’t complain too much. The idea is similar to that of other Python GUI toolkits: you connect functions that you have written to signals or events that are emitted by a widget. So whenever a widget emits a signal that has been connected to a function, that function will be called.

Note: Qt goes a bit further with the idea of built-in “slots” but for the sake of this tutorial we will be ignoring them. For more information on “slots” please see the PyQt4 documentation.

In order for the “QPushButton” to do something when pushed, we have to connect a function in our HelloWindow class with the clicked signal. To do this, we need to call the QObject classes connect static function. We must tell it three things:

1. The QWidget that will emit the signal.
2. The signal that we want to connect to.
3. The function that should be called when the QWidget emits the signal.

In our example, we will do this like so:

#connect signal
QtCore.QObject.connect(self.hello_button
	, QtCore.SIGNAL("clicked()")
	, self.on_hello_clicked)

Note: We could just as easily have called self.connect since a QMainWindow is a descendant of the QObject class.

QtCore.SIGNAL is a macro that you have to use for the second parameter. It accepts the signal name as a string (in this case clicked()) and converts it into an object that is required for the signal connection. The documentation is a bit sparse regarding exactly what the macro does, but luckily for us we don’t really need to know the details.

In order for this code to compile, we also have to import the QtCore module:

from PyQt4 import QtCore

We also have to add a new function to the HelloWindow class. This is the function on_hello_clicked that will respond to the clicked() signal:

def on_hello_clicked(self):
	print "Clicked"

When you run the code in Listing 3, you will see “Clicked” printed out to the command line every time you click the “Push Me!” button.

Listing 3

#!/usr/bin/env python
import PyQt4
import sys
from PyQt4 import QtGui
from PyQt4 import QtCore

class HelloWindow(QtGui.QMainWindow):

	def __init__(self, win_parent = None):
		#Init the base class
		QtGui.QMainWindow.__init__(self, win_parent)
		self.create_widgets()


	def create_widgets(self):
		#Widgets
		self.label = QtGui.QLabel("Say hello:")
		self.hello_edit = QtGui.QLineEdit()
		self.hello_button = QtGui.QPushButton("Push Me!")

		#connect signal
		QtCore.QObject.connect(self.hello_button
			, QtCore.SIGNAL("clicked()")
			, self.on_hello_clicked)


		#Horizontal layout
		h_box = QtGui.QHBoxLayout()
		h_box.addWidget(self.label)
		h_box.addWidget(self.hello_edit)
		h_box.addWidget(self.hello_button)
		#Create central widget, add layout and set
		central_widget = QtGui.QWidget()
		central_widget.setLayout(h_box)
		self.setCentralWidget(central_widget)

	def on_hello_clicked(self):
		print "Clicked"
		
if __name__ == "__main__":
	# Someone is launching this directly
	# Create the QApplication
	app = QtGui.QApplication(sys.argv)
	#The Main window
	main_window = HelloWindow()
	main_window.show()
	# Enter the main loop
	app.exec_()

Displaying a Message

Now that we have a function connected to the clicking of the button widget we need to read the text from the QLineEdit widget and display it to the user. Fortunately both tasks are quite easy in PyQt4.

In order to get the text from a QLineEdit widget, the QLineEdit classes member function displayText needs to be called. The displayText function simply returns the contents of the QLineEdit widget.

Showing a pop-up dialog using PyQt4 is just as easy as getting the contents of a QLineEdit widget, thanks to the QMesssageBox classes static function information. This function allows you to set the title, text and what buttons are on a simple “information dialog”. Calling it from our PyQt4 application in response to the button click is very simple:

def on_hello_clicked(self):
	QtGui.QMessageBox.information(self
		, "Hello!"
		, "Hello %s" % self.hello_edit.displayText()
		, QtGui.QMessageBox.Ok)

In this code, we call the information function, set its title text to be “Hello!”, set its main message to be “Hello ” followed by the contents of the hello_edit QInputEdit, and give it one button, an “ok” button. All of the code is found in Listing 4. What this looks like running on Linux, Windows and OS X can be seen in Figure 3, Figure 4, and Figure 5 respectively.

Listing 4

#!/usr/bin/env python
import PyQt4
import sys
from PyQt4 import QtGui
from PyQt4 import QtCore

class HelloWindow(QtGui.QMainWindow):

	def __init__(self, win_parent = None):
		#Init the base class
		QtGui.QMainWindow.__init__(self, win_parent)
		self.create_widgets()


	def create_widgets(self):
		#Widgets
		self.label = QtGui.QLabel("Say hello:")
		self.hello_edit = QtGui.QLineEdit()
		self.hello_button = QtGui.QPushButton("Push Me!")

		#connect signal
		QtCore.QObject.connect(self.hello_button
			, QtCore.SIGNAL("clicked()")
			, self.on_hello_clicked)


		#Horizontal layout
		h_box = QtGui.QHBoxLayout()
		h_box.addWidget(self.label)
		h_box.addWidget(self.hello_edit)
		h_box.addWidget(self.hello_button)
		#Create central widget, add layout and set
		central_widget = QtGui.QWidget()
		central_widget.setLayout(h_box)
		self.setCentralWidget(central_widget)

	def on_hello_clicked(self):
		QtGui.QMessageBox.information(self
			, "Hello!"
			, "Hello %s" % self.hello_edit.displayText()
			, QtGui.QMessageBox.Ok)

if __name__ == "__main__":
	# Someone is launching this directly
	# Create the QApplication
	app = QtGui.QApplication(sys.argv)
	#The Main window
	main_window = HelloWindow()
	main_window.show()
	# Enter the main loop
	app.exec_()

Figure 3

Figure 4

Figure 5

Conclusion

That’s it for this quick introduction to PyQt4. All-in-all, I was pleasantly surprised by PyQt4. Even during my first foray into the toolkit, I found it very straightforward and was able to get things going in a matter of minutes. Granted, what I was trying to accomplish was pretty simple, but it’s always a good sign when doing something simple is easy!

You should now have enough information to go and create your very own “GUIfied” applications. Of course, there is much, much more to PyQt4 than I have covered in this tutorial. But if you use the PyQt4 documentation and the fundamentals from this tutorial, you shouldn’t have much trouble moving forward.

[1] http://www.python.org/download/
[2] http://www.riverbankcomputing.co.uk/pyqt/download.php
[3] http://trolltech.com/downloads
[4] http://www.riverbankcomputing.com/Docs/PyQt4/pyqt4ref.html#installing-pyqt
[5] http://www.riverbankcomputing.com/Docs/PyQt4/html/qapplication.html
[6] http://www.riverbankcomputing.com/Docs/PyQt4/html/qapplication.html#exec
[7] http://www.riverbankcomputing.com/Docs/PyQt4/html/qmainwindow.html#details
[8] http://www.riverbankcomputing.com/Docs/PyQt4/html/qmainwindow.html#details

So here it is the initial release of the Dodger Editor. You can download the source from the google code page. Originally I wanted to wait longer to release the first version, but as time went on and it kept taking longer and longer I realized that if I didn’t release the first version at some point in time I might never release it.

If I had gotten done everything that I wanted to get done then I would be releasing the full first version of the project instead of this incredibly alpha release. So if something doesn’t work don’t be surprised. That being said you should be able to use it for it’s basic functionality. Or at least that’s what I’m hoping.

Installation

Once you have met the requirements:

• Python >= 2.5 http://www.python.org/download/
• GTK+ >= 2.10 http://www.pygtk.org/downloads.html
• PyGTK >= 2.10 http://www.pygtk.org/downloads.html
• pyGObject >= 2.10 http://www.pygtk.org/downloads.html
• pyglet >= 1.1 http://www.pyglet.org/download.html
• PyYAML > 3.0 http://pyyaml.org/wiki/PyYAML
• PyOpenGL 3.x http://pyopengl.sourceforge.net/
• PyGtkGlExt? http://www.k-3d.org/gtkglext/Main_Page

You should be able to install dodger by downloading the and extracting the source tarball. Once you have extracted the source change into the source directory and install using:


python setyup.py install


If you don’t want to install and just want to test dodger out simply follow the above instructions except instead of installing dodger run the following:


python runner.py


Contributing

I still want to setup a dedicated site that will host a mercurial repository of the code but for now I’m going to use the fabulous http://freehg.org site. For now if you want you can pull the source from  http://freehg.org/u/selsine/dodger/:


hg clone http://freehg.org/u/selsine/dodger/


For now any changes that you make or fixes will have to be emailed as a patch but eventually I want to make it much much easier for people to submit fixes.

If you are really interested in contributing to this project let me know via a comment to this post and or an email. Then once all of the source issue are setup we can start working together.

New Features

Since the last release there has been a lot of changes under the hood. The most visible changes for anyone using dodger is the addition of the zoom feature:

zoomed out example

zoomed in example

General Usage

For general usage information please read the README file or General Information page on the google site. It’s pretty rough right now but hopefully over time we’ll be able to work on it.

History

If you are interested in the history of this project you can read the following blog posts to see how everything came about:

• A Simple Python Game Engine?
• More thoughts on the simple Python Game Engine
• Pyglet Level Editor
• Level Editor 0.2
• Level Editor 0.3 (Dodger)

Beware

If you do decide to use or test dodger remember that you are going to find a lot of bugs during the iteration. Please be kind and report the issue.

I know it’s been a while and for that I apologize the last few months have been pretty crazy around here…although I’m starting to see a trend with life in general lately, namely that it’s always crazy.

I’ve been busy with work, Python Magazine, my wife, trips to Dallas, and yes whenever I get a chance this slowly growing level editor. Let’s see what I’ve been working on for the last little while:

Name: There has a been a name for the editor ever since I started working on it. I wasn’t sure if I was going to think up something really cool and change, or leave it. Turns out I just left it.

So from this point forward this project is christened: “Dodger”, or probably more correctly: “Dodger Level Editor”.

The name has its roots in the name of one of my cats and a history in the multitudes of level editors and game engines that I have tried to create in the past, but I wont’ go into that. So Dodger it is.

Saving and Loading: Saving and loading in the default YAML project type now really works. I still need to put in support for optional project types: XML, JSON, and other formats

Welcome Dialog: This was a real pain, but it’s made the last while so much easier. I added a working (at least I hope) welcome dialog with a way to create new projects, open old ones, and a recent file list. The recent file list really makes testing easier for me.

Rect tracking: Rect tracking is finally working properly. I’ve had the rect tracker in there for a while but it didn’t really do anything until now.

Multiple Selection/Multiple Properties: I’ve also finally got multiple selection going, which is what makes the rect tracker actually useful. You can select multiple sprties, move them around and add properties to all of them.

Remove Properties: Now you can remove custom properties that you have added. This was a must but I was lazy and left it for a while.

Steps towards being made public: A lot of the changes (and I do mean a lot) that I’ve been making have been behind the scenes. There has been a lot of refactoring and reorganizing of the code, often the result of quick and dirty implementations that I made earlier (sigh). I’ve also started working on getting the distribution of this editor going so that other people can use/develop it.

I’ve added support for zc.Buildout so that if anyone wants to develop they can quickly gather dependencies and won’t have to install the editor system wide, not that you have to anyways but zc.Buildout is really neat.

I’ve also worked on the license (GPLv3) and setup.py and README and all of that. None of it’s done but it’s working its way forward.

Faster: It’s also much faster now. None of you have used it so you’ll probably think it’s slow, but trust me it’s much faster then it was before.

Bugs: There have been loads of bugs that have been fixed and created. Plus one doozy related to
changes made to pyglet. Not pyglets fault but it took a long time to figure out what the issue was.

Google Code: The project has a temporary homepage over at google code: http://code.google.com/p/dodger-editor/ There’s nothing there yet but over time I will start to host the project there so that people can easily download it. I’ll still post updates here until the site has a full-time home.

I’m going to use Mercurial for the revision control system for the project so the CVS support at the google code site will just be for downloading. Eventually I will want to host the project on some space of my own and get a nice web interface for the mercurial repository going. I’ll have to find a new hosting company so it will take a while.

So that’s it, that’s what’s happened to the Dodger Level Editor over the last few months. I know I promised to make it public earlier but given the shape it was in at that time there really was no point. I want this to be at a point where people can actually almost use it before I make it public.

I know it’s been a while, and I know the few of you that actually care about this project have probably moved onto bigger and better things, but hopefully if you stick with me there will be something out soon.