Zovirl Industries

I’ve been thinking for a while now about how software engineers can help their communities.  Lawyers have pro bono work.  Engineers and contractors can help build houses for people.  But computer programming is really specialized; most people don’t need custom computer programs.  This spring, though, I found a rewarding way to use my computer science skills: teaching kids how to program. I did this though the Citizen Schools program, which encourages members of the community to share what they know with middle school students.   It is a really neat idea: the kids get to learn things their school probably doesn’t have the expertise (or time) to teach, and the “citizen teachers” get a chance to help the community and work with some fun kids.  The topics which people teach vary widely.  For example, here are some of the topics other people were teaching during this last semester:

• Digital photography.  The kids presented a gallery of their photos at a local coffee shop.
• The science (and math) of baseball.  10 teenage boys happily doing statistics for an hour.  Awesome.
• Make a text adventure game in Javascript.  You can play the game they made right now.
• Cooking and nutrition with a chef from one of Google’s famed cafeterias.

So what did I teach?

I showed the kids in my class how to use Python to create a drum machine program which plays various drum sounds when you hit different keys on the keyboard.  For example, pressing S played the snare drum, C played the cowbell, and B was the bass drum.  The finished version of the program had about 20 different drum sounds which the user could play.  There were some colorful graphics (drawn by one of the students) showing which drums were being hit.  The program could record drum loops, letting the user layer more beats on top of the original beat, and could save and load the sequences of beats.  The kids ended up being much better at laying down beats than I was.  At the end of the class, the kids presented the program to their parents & teachers, plus had a field trip to a local cafe to demo their work to the community.

What’s it like teaching a class?

Citizen Schools took care of most of the logistics, provided some training on effective ways to teach, and paired us with advisors who helped us work with the children every week.  This was really helpful for me, because I was a bit lost figuring out how to present computer programming in a way that middle school students would enjoy.  Arturo, my advisor, was super helpful.  He worked with the kids every day so he knew them all, plus he had lots of great ideas for ways to present the material and keep the students’ attention.

The semester I taught was a one hour class per week, for ten weeks.  That’s not very much time, so I had to work really hard to keep the material short and simple so the kids could learn it.  Code is like essays: being terse takes a lot more effort than being verbose.  I definitely spent more time preparing for class than I did teaching it.

The kids all knew how to use computers, thanks to myspace, youtube, etc. but none of them had ever written a computer program before.  For the first several classes, I would project some simple code and have them type it in and run it.  Then I would have them change it or write some similar code.  For example, one week we were making the program play different sounds.  I showed them how to make it play a snare drum sound, then had them figure out how to make it play a bass drum sound, cymbal sound, etc.  They got very good at the repetitive code.  There were some one-off lines of code that I don’t think they ever did understand (a function definition, for example).

It was interesting to watch as they learned how to make the computer do what they wanted it to do.  I was a little worried a few weeks before the final presentation because the kids weren’t able to explain how to make the program play a new sound.  However, when I asked them to code it they knew exactly how to do it.  It turns out they just didn’t know the right jargon to explain what they were doing.  By the end of the semester, they could make simple modifications to the program to change the way it behaved, or to fix bugs in the code.

Would I do it again?

Will I teach another semester?  Yes, I think so.  Although it turned out to be a pretty big time commitment this time, I should be able to reuse my lesson plans the next time around so it should be much easier.  Like I said at the beginning, it is a really good way to use software engineering skills to help the community and it was really cool to see the students figuring out how to modify their programs on their own.  I think that’s reason enough to do it again.

In my 20% time at work, I’ve been writing a Python library for generating charts using Google’s Chart API. It is called graphy, and is now available as open source software on code.google.com.

I’ve frequently wanted to visualize data for various projects I’m working on but have been frustrated because the tools I’ve used are too heavyweight for making simple charts. When the opportunity came up to use the Google Chart API, I jumped at the chance to make a chart library I would enjoy using. The basic philosophy of graphy is to let you present your data naturally and then get out of your way.

I’ll give a couple examples of how it is used. If you’re interested, there are more examples in the user guide and in the source code. First, a simple bar chart showing how population has changed over the past 4 decades in three bay area cities:

from graphy.backends import google_chart_api

cities = ('San Jose', 'San Francisco', 'Oakland')
pop_1960 = (200000, 740000, 370000)
pop_2000 = (900000, 780000, 400000)

chart = google_chart_api.BarChart()
chart.AddBars(pop_1960, label='1960', color='ccccff')
chart.AddBars(pop_2000, label='2000', color='0000aa')
chart.vertical = False
chart.left.labels = cities
chart.bottom.min = 0
print chart.display.Img(300, 180)

That snippet gives this chart:

Next an example using a line chart to compare average temperature trends in two US cities:

from graphy.backends import google_chart_api
from graphy import formatters
from graphy import line_chart

# Average monthly temperature
sunnyvale = \
    [49, 52, 55, 58, 62, 66, 68, 68, 66, 61, 54, 48, 49]
chicago = \
    [25, 31, 39, 50, 60, 70, 75, 74, 66, 55, 42, 30, 25]

chart = google_chart_api.LineChart()
chart.AddLine(sunnyvale, label='Sunnyvale')
chart.AddLine(chicago, label='Chicago',
    pattern=line_chart.LineStyle.DASHED)
chart.bottom.labels = ['Jan', 'Apr', 'Jul', 'Sep', 'Jan']

chart.left.min = 0
chart.left.max = 80
chart.left.labels = [10, 32, 50, 70]
chart.left.label_positions = [10, 32, 50, 70]
chart.AddFormatter(formatters.InlineLegend)
print chart.display.Img(350, 170)

Here’s the resulting chart:

Check out the graphy project page for more information, including the full user guide and source code for graphy 1.0, released under the Apache 2 license.

I’m crafting an economy game using Python and Google App Engine.  Called Island of Naru, it is a simulation of a small society on a tropical island. The core design revolves around production and trade:

• Towns produce resources based on the surrounding terrain.
• Towns can trade with each other through a complex trading network.

Because terrain affects production, the location of a town matters. A town surrounded by mountains, for example, can’t grow very much corn, while a town surrounded by corn fields can’t mine very much iron ore.  The disparity between locations is what drives the need for towns to trade with each other.  By trading with each other, our hypothetical mining camp and farming town can become much more prosperous than they could on their own.

The previous release of Island of Naru added simple trade: cities that were connected by roads could exchange goods.  The latest version adds simple production: towns near fields will produce food, towns near mountains will produce raw materials, and towns near factories will produce manufactured good.  This change is exciting because it means the core game concept is now playable.  In essence, this is the first version of Naru which actually resembles the game I set out to build.  Now it is possible to start experimenting with the game design, looking for the fun aspects of it and bringing those to the forefront. Much like a rough draft of an essay, the game needs rework and refinement to bring out the best qualities buried inside it. I’ve already done a little exploring with some changes I’ll talk about below, plus I have some half-formed, fuzzier ideas about where the fun may be hiding that I’ll mention at the end.

As soon as terrain-based production was finished, my first thought for improvement was to make the economy more complex by providing ways to enhance production and by adding another resource type: manufactured goods. Production of raw materials can be improved by digging mines in mountains, while irrigating fields increases the amount of food they will produce.  Manufactured goods are only available if you build a factory next to a city. These changes gave the game more depth.

Next I added exploration. I want the player to have to think about where they build their cities, especially the first one.  It should be beneficial to hunt around a little for an ideal site, and I think that with a little tweaking, this can be accomplished now that terrain impacts the city’s production.  I’m hoping that exploration will help enhance the feeling of landing on a new island and looking for a good spot to build.  The island starts out unexplored except for the beach where you came ashore.  You can then explore outward from there.  I tried making the player explore one tile at a time, but I found that it took too much time: most of the player’s clicks were spent exploring instead of building.  That’s not what the focus of the game should be, so I changed the exploration to uncover several squares at a time, which helped.  With exploration, the early game is more interesting because the player has to balance exploring with the need to start a town as soon as possible. 

Finally I tried to tweak the numbers behind the economy so towns could grow to larger, more realistic populations.  This change took more effort than I expected because it uncovered some problems with the way I was calculating demand and the happiness of the population.  Fixing it involved modifying the internals of the supply/demand system to allow more control over the minimum and maximum required amounts for each resource.  This not only fixed the problem, but also gave me more control over the balance of the game, which may be useful later. The new numbers don’t seem glaringly low, so the game feels more realistic now.

Even with these improvements, I feel like the fun is still buried. So where should I look next? I have three guesses. First, I suspect the game needs more depth. There are only three resource types at the moment, for example. The trade is also very simple. A more complicated trading network with bottlenecks and different capacities for different types of roads would be much more interesting to manage.  Right now, once a player connects all the cities with roads, there is nothing else to manage.  There’s no upkeep, improvement, foreign trade, or ports.  I expect adding these details will help a great deal once the game is almost finished, but they should probably wait until the bare skeleton of the game is a little more refined.  You don’t start decorating a house until after the walls are finished being built.

My second guess for making the game more fun is adding a goal. In its current state, Naru is just a toy, not a game.  Sure, the player can invent a goal for themselves, like “highest island population in 100 moves” or “biggest single city” but self-imposed goals aren’t very powerful motivators.  I think imposing some goals and adding a high-score table or two would help add a competitive edge to the game.  I may add a simple goal just to give some direction to the player and avoid confusion about what they are supposed to be doing, but fancier changes like multiple goals or score tracking need to wait until the core idea provides more fun on its own.  High score tables enhance existing fun, they don’t magically make boring tasks fun.

I think the biggest thing hiding the fun is lack of resource constraints on the player.  They can build everything for free, so nothing feels valuable.  For example, the player should have to agonize a little over the perfect location for their first city, but right now there’s no need: if they find a better location later they can always just build a city there.  Similarly for roads: the player should feel a sense of accomplishment when they manage to add a new city to the trading network but since roads are free this isn’t difficult and there’s no sense of accomplishment.  A typical game goes something like this: build a city in the flatlands, build a second city by the mountains, build 4 road segments to connect them, build a factory, uhh, now what? 7 moves into the game the player already has access to every resource. Each new city gets connected to the others in a handful of moves, and the end result is that the player doesn’t spend any time thinking about the trading network.

I’m going to try the standard solution to this problem: gold.  Make the player pay for building cities and roads, so they are more valuable.  I’m not sure what the mechanism will be for getting money in the world.  Sim City had taxes to raise money, Oasis let you search for followers in the desert, Civilization made you use special “settler” units that were difficult to obtain.  Any of these would work; the key thing is that the player has to make choices about what they want to build with limited resources.

If you want to play the game now, even though the fun may still be elusive, go to http://naru.countlessprojects.com.  Feedback is always welcome, of course. You can leave it below.  I’d be especially interested to hear any thoughts about refining game designs to uncover hidden fun within them.

BEEP BEEP BEEP BEEP…

I hate waking up to an alarm clock. Especially on cold, dark winter mornings. My wife and I want to bike to work around dawn (so we can leave work and ride home before dark) which means we have to get up while it is still dark outside. This is painful with a regular alarm, but we’ve been experimenting with a prototype sunrise alarm built with an Arudino.

I’ve noticed that in the spring, when sunrise is getting earlier, it becomes easier to wake up before the alarm goes off. This feels wonderful: instead of feeling groggy, I feel rested and sometimes even energetic. In the fall, though, the trend reverses and it gets harder to wake up as it gets darker each morning. The sunrise alarm counteracts this trend by simulating an early sunrise, making sure the room is bright when it is time to wake up. This prompts the body to wake up naturally, rather than shocking it awake with loud noises.

The hardware for our prototype alarm is simple: I have a Lutron Maestro wireless light dimmer, and the Arudino is controlling it with a single IR LED. (You can read more about how to use the Arudino as a remote control in this post). The nice thing about controlling the dimmer wirelessly is that everything is 5 volts, and I don’t have to worry about 120 volt shocks.

The software uses the remote control signal code described in the earlier post and combines it with the example from the Arduino DateTime library. The DateTime example has code for setting the clock on the Arudino over USB. I extended the code to set the current time and also set the time for the alarm to go off. The Arduino waits for the alarm time to arrive, then starts sending commands to the light dimmer to slowly turn on the lights. Right now I have it turn them on over the course of 20 minutes.

At the moment this is just a prototype. I’ve been pretty happy with it so far, so at some point I’ll probably rebuild it using a bare AVR chip (so that I can use the Arudino for other projects). Here are some of the things I’ve noticed so far from using it:

1. Using the regular lights in our room guarantees that it is bright enough to wake us up. It is a little too bright, actually. I might put lower-wattage bulbs in.
2. I originally had it turn the lights on over 15 minutes. However, that felt too sudden. I kept waking up when it was already pretty bright. I slowed it down to take 20 minutes total; it ramps up to half brightness over the first 15 minutes then up to full brightness in the last 5 minutes. This seems to be more gentle. It might be worth experimenting with even longer periods of time, like 30 minutes.
3. The Arudino Diecimila resets when it is connected or disconnected from USB, losing the time. However, if you just yank the USB cable it won’t reset.  This is nice because otherwise I’d have to leave my laptop turned on all night just so the Arudino wouldn’t lose the time.  Leave the IDE running and connected to the Arduino, then unplug the cable without disconnecting in software.  You’ll need to have the Arduino powered from a dedicated power supply so there’s still power after the USB is unplugged.
4. I originally had it run once and then stop, but that meant I had to set the alarm again every day which was a hassle. I changed it to automatically move the alarm time forward 24 hours, which is much more convenient (but also risky…if I forget and leave it running when we go on a trip, it will turn the lights on and leave them on).
5. Bugs in this type of device are annoying. If you make a coding mistake, it might turn the lights on in the middle of the night, or turn them on and leave them on all day. It might also fail to work at all, letting you oversleep.

We’re still setting our regular alarm to go off a few minutes after the lights reach full brightness, just in case we don’t wake up. (I may add a speaker to the finished alarm to consolidate this). Most days I’m already awake or just lightly dozing when it goes the regular alarm goes off, which is good. It feels much more civilized than getting dragged out of bed in the dark by that awful beeping.

It is really easy to build a universal remote using an Arduino. With just an infrared LED, it can impersonate remotes for your TV, fans, lights, etc. and can let you easily incorporate these into your electronics projects. You won’t even have to solder anything or void any warranties.

For a project I’m working on, I need to emulate the remote for a Lutron Maestro light dimmer. It uses a modulated IR signal, which is a common way for remotes to communicate and is easy to generate with an Arduino. The basic scheme is simple: each command is sent as a series of pulses representing bits, which can be either 1 or 0. To send a 1, you rapidly blink the LED on and off at 38 kHz, and for 0 you leave the LED off. (The 38 kHz modulation makes the signal more resistant to interference from other light sources).

Decoding the Signal

To decode what the remote was sending, I used an oscilloscope and a small photodiode. The photodiode generates a small amount of voltage when light hits it, and responds to changes in light level quickly enough that the oscilloscope can draw a really nice plot of the signal. I have a Parallax USB oscilloscope, which is perfect for showing the command pulses and is just fast enough to find the modulation frequency. As an aside, I’m really happy with the Parallax oscilloscope for projects like this. It is simple to use and I love being able to save images to share with people.
Here’s what two of the commands from the dimmer remote look like. The top signal is the “fade lights up” command, and the bottom one is “fade lights down”:

I captured several different commands, then measured the length of all the pulses. Looking at all the commands, a couple patterns are obvious:

• The entire command takes 82.8 ms (after which it immediately repeats, presumably because I held the button on the remote down a little too long).
• The lengths of the on/off pulses are all multiples of 2.3 milliseconds (which means that the entire 82.8 ms command represents 36 bits of data).
• The last four bits are always 0, so really each command is four bytes long, followed by four 0 bits

The particular remote I’m emulating has five commands, and once they are decoded they look like this:

fade up        = [255, 136, 130, 34]
fade down      = [255, 136, 130, 20]
full on        = [255, 136, 132, 184]
full off       = [255, 136, 189, 18]
memory recall  = [255, 136, 132, 183]

The one other missing piece of information is the modulation frequency which can be measured by zooming in on any of the pulses. This remote uses a frequency of 39.68 kHz. (In the plot below, it says the frequency is 3.96 kHz because I included 10 pulses in the measurement to increase accuracy)

Programming the Arduino

Using an Arduino makes the circuit really simple. I put an IR LED between pin 13 and ground. Since pin 13 has an internal resistor, that’s all that’s required. The code is also pretty straightforward:

/* Control a Lutron Maestro light dimmer */
#define BIT_IS_SET(i, bits)  (1 << i & bits)

// LED connected to digital pin 13
const int LED_PIN = 13;
// Width of a pulse, in microseconds
const int PULSE_WIDTH = 2300;
// # of bytes per command
const int COMMAND_LENGTH = 4;    

const int UP[]     = {255, 136, 130, 34};
const int DOWN[]   = {255, 136, 130, 20};
const int ON[]     = {255, 136, 132, 184};
const int OFF[]    = {255, 136, 189, 18};
const int RECALL[] = {255, 136, 132, 183};

void setup()
{
  pinMode(LED_PIN, OUTPUT);
}

/* Modulate pin at 39 kHz for give number of microseconds */
void on(int pin, int time) {
  static const int period = 25;
  // found wait_time by measuring with oscilloscope
  static const int wait_time = 9;  

  for (time = time/period; time > 0; time--) {
    digitalWrite(pin, HIGH);
    delayMicroseconds(wait_time);
    digitalWrite(pin, LOW);
    delayMicroseconds(wait_time);
  }
}

/* Leave pin off for time (given in microseconds) */
void off(int pin, int time) {
  digitalWrite(pin, LOW);
  delayMicroseconds(time);
}

/* Send a byte over the IR LED */
void send_byte(int bits) {
  for (int i = 7; i >= 0; i--)
  {
    if (BIT_IS_SET(i, bits)) {
      on(LED_PIN, PULSE_WIDTH);
    } else {
      off(LED_PIN, PULSE_WIDTH);
    }
  }
}

/* Send a full command */
void command(const int bytes[]) {
  for (int i = 0; i < COMMAND_LENGTH; i++) {
    send_byte(bytes[i]);
  }
  off(LED_PIN, 4 * PULSE_WIDTH);
}

void loop()
{
  command(UP);
  delay(1000);
  command(DOWN);
  delay(1000);
}

Checking the Arduino with the Oscilloscope

After programming the Arduino, I used the oscilloscope to check the output of the Arduino vs. the original remote. On my first attempt, the wait_time in on() was too long, so the command was stretched out. I hadn’t accounted for the fact that the digitalWrite() calls would consume some time, so I just decreased wait_time until the length of the overall command was correct. As you can see, the final synthesized command overlaps exactly with the authentic one. The green signal on top is from the remote, and the blue signal on the bottom is the arduino:

Final Test

Once everything was ready to go, it was time for the last test: I tried using the Arduino in place of the remote and it worked! It was able to control the light dimmer flawlessly. This lets me finish my project, plus now that I have all the code written it should be easy to adapt to other remote control devices.

Normally there are all kinds of aspects to a simulation that are driven by mathematical functions, like gravity, momentum, population growth rates, economic trends, etc. However, mathematical functions can be difficult to tweak, and I’m finding it much easier to use lookup tables instead. This makes it much easier to make slight modifications: rather than adding additional terms to a function, I just have to modify the values in a table.

I wrapped up the logic for storing a table of points and providing interpolated values between those points into a Python class. Using Python’s __getitem__ method lets my object behave like an array, so I can just index into it when I need a value. Here’s the class:

class InterpolatedArray(object):

  """An array-like object that provides
  interpolated values between set points."""

  def __init__(self, points):
    self.points = sorted(points)

  def __getitem__(self, x):
    if x < self.points[0][0] or x > self.points[-1][0]:
      raise ValueError
    lower_point, upper_point = self._GetBoundingPoints(x)
    return self._Interpolate(x, lower_point, upper_point)

  def _GetBoundingPoints(self, x):
    """Get the lower/upper points that bound x."""
    lower_point = None
    upper_point = self.points[0]
    for point  in self.points[1:]:
      lower_point = upper_point
      upper_point = point
      if x <= upper_point[0]:
        break
    return lower_point, upper_point

  def _Interpolate(self, x, lower_point, upper_point):
    """Interpolate a Y value for x given lower & upper
    bounding points."""
    slope = (float(upper_point[1] - lower_point[1]) /
             (upper_point[0] - lower_point[0]))
    return lower_point[1] + (slope * (x - lower_point[0]))

You use it like this:

points = ((1, 0), (5, 10), (10, 0))
table = InterpolatedArray(points)
print table[1]
print table[3.2]
print table[7]

Here are some examples of the kinds of curves you can easily make:

points = [(0, 0), (2, 9.5), (4, 8.5), (6, 4), (8, 1), (10, 0)]

points = [(0, 10), (2, 10), (4, 9), (6, 7), (8, 4), (10, 0)]

points = [(0, 0), (2, 0), (4, 1.5), (6, 8.5), (8, 10), (10, 10)]

Feel free to use this in your own projects.

This post is about being productive.  It is also about drawing charts, Google App Engine, and monkeys.  But mostly about being productive.

For a couple years now I’ve been running a project night.  Once a week, myself and a few friends get together to work on whatever projects we’re currently involved with.  The goal is to sit down and get stuff done, in the company of other productive people.

Last week, a friend and I decided to collaborate on a service for recording and charting measurements.  The basic problem we’d be solving: “I want to measure some property X every N seconds and then draw a chart of the data.”  The service would take care of aggregating the data and drawing charts.  The user of the service would just have to write a client to periodically report measurements.

We set ourselves a challenge: try to finish a (very) rough end-to-end prototype in 2.5 hours.  It would have a simple API for reporting new data points, a web page where you could view the charts, and one example client.  Here’s what we managed to get done:

• Stats Monkey (the live site)
• The developer site (with source code)

The whole things is done using Django on Google App Engine.  There’s a simple REST API for posting new measurements, and a simple web interface for viewing the charts  (which are drawn using Google Chart API).

Some observations from the project:

Picking a name is hard.  Unless you’re in a hurry, in which case it becomes easy: you just pick the first name someone shouts out that isn’t taken.  Stats Monkey was the first name that passed this test for us.

Having two people sitting next to each other made it much easier to stay in scope.  When one of us was about to waste time over-doing something or going off on a tangent, the other one would step in.  I don’t think we would have finished the project if we hadn’t been sitting next to each other announcing what we were about to do.

We both already had a lot of experience with all the tools we were using.  This really helped, because there was basically no setup time.  Google Code and App Engine both have really low startup costs for a project like this.  I think maybe only 10-15 minutes were spent getting a subversion repository and hosting.

We hit a lot of SVN conflicts.  We also had trouble with incomplete syncs/commits because we were both used to p4 semantics (where a commit includes files from the entire workspace, not just the current directory).  It would be worth trying to mitigate this if we try something like this again.

The code is full of bugs and security holes, and doesn’t have any tests.  Since we just barely made our deadline, I don’t think this was a mistake.  We didn’t hit any major roadblocks, but this was probably just luck.

I’ve finished a first pass at building cities on an island. You can create a new island* and then start building on it. You can start cities, build roads, and irrigate bare land to create fields. There’s also a terraforming menu, so you can flatten mountains and fill in the ocean if you want. Terraforming won’t be there in the finished game, but makes it easier to play around with different island shapes at this stage. Some of the basic building logic is implemented (so you can’t build a road across mountains, you can’t build a city in the water, etc.)

The interface is…simple. It is the simplest interface that could just barely be construed as working.  I’ll definitely be spending more time on this, but I want to get the core of the game working first.

As I hinted at in my last post, I’m writing this game in 2 distinct parts:

1) A library which implements a simple island economy game
2) A django app which puts a nice user interface on this library

By implementing my core game logic as pure (non-django-related) python, I have a bit more freedom writing tests and experimenting. I’m just writing normal tests using the unittest module. I’ve started on a basic economy, and so I have a couple command-line programs that let me poke around at different parameters trying to get something reasonably stable and convincing.

The live version is running at http://naru.countlessprojects.com. If you try it out, I’d appreciate any comments, questions, or feedback you might have.

* For now, I’m requiring sign-ins using google accounts. This was the fastest way for me to make sure no one else can edit your maps. I definitely want to allow instant, no-sign-in play later, but right now there are much bigger features to tackle.

I’m finding that BlobProperties are a very handy way to optimize for the App Engine Datastore. The first iteration of my island map had a Map model, and a Tile model. It used entity groups, so one Map entity was the parent of 100 Tile entities. Nice and clean, and of course dog slow. You don’t want to be loading 101 entities with every request if you can avoid it.

I added some quick-and-dirty benchmarking to time the datastore calls and then tried several different approaches. The one I settled on was BlobProperties and pickle. Basically, my Map model has a BlobProperty which stores all the tiles in pickled format. When I load the map out of the datastore, I use pickle.loads() and before I save the map back, I use pickle.dumps(). This means that I only have to load a single entity out of the datastore to display a map.

Nice side benefit: My core game logic is now pure python, so it is really easy to work on it outside of app engine (or even django). This is handy when writing benchmarking tools, testing, and prototyping new algorithms.

Possible drawback: I think I will hit compatibility problems if I try to load old pickled objects after renaming python modules. If this becomes a problem, I plan to start pickling an intermediate format like a dict instead of full objects.

Some of the folks at Google have released a sweet Google App Engine Helper for Django. It consists of a skeleton Django project with all the little tweaks necessary to run on top of App Engine, and it makes it really easy to get started.

I used the helper to start a simple Django app that shows tiled maps, using the images I previously created. Making a map generator is way, way down on the list of things that might possibly make this game fun, so I just quickly made three 10×10 islands manually. Here’s the code which generates the island in the screenshot:

island1 = _Expand("""O O L L O O L L L O
O L C_road_s L L L L F L O
O L L_road_en L_road_ew L_road_ew F_road_esw C_road_w F O O
O L L L L F_road_ns F L L O
O O O L L L_road_ns L L L O
O L L L M L_road_ns L L L L
L L L L L L_road_ns M L L L
L L L M M L_road_ns L L L O
O L L L L L_road_en C_road_w O L O
O O O L L L L O O L""")

The app picks one of the three islands randomly and spits out an HTML table containing the image tiles. A little bit of CSS makes it fit together without borders/gaps:

table {
  border-collapse: collapse;
}

td, tr {
  line-height: 0;
  padding: 0;
}

.island {
  border: solid 1px black;
  float: left;
  margin: 1em;
}

If you want to see it live, the latest version is running here.